eleanor awbery white msc thesis lse 2009
TRANSCRIPT
AN498: 2008 -‐ 2009 MSc China in Comparative Perspective Candidate Number 77732
Racing to Learn, or Learning to Race?
How ethnography can help China realize its technological advantage over India
Word Count: 9,978
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Acknowledgements
Despite the theme of the following pages, ultimately, learning is unquantifiable. I consider the following contributions to my own learning inestimable. My gratitude goes to Professor Stephan Feuchtwang, from whom I learned precision, cogency and more about China than I imagined possible; and to Dr. Gonçalo Santos, for his always insightful comments and thought-‐provocation. My appreciation goes to Prof. Vivek Wadhwa, Pratt School of Engineering, Duke University, for his kindness in helping me locate primary data sources. To my classmates, for shared enthusiasm and suggestions for research and contacts. And to David, from whom I am always learning; without his support I could not have attended LSE.
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Table of contents
Acknowledgements ........................................................................................................................ 2 Table of contents ............................................................................................................................ 3 List of Tables and Figures................................................................................................................ 5 Abbreviations and acronyms .......................................................................................................... 6 Abstract .......................................................................................................................................... 7 Introduction .................................................................................................................................... 9 1 Technology, development and learning ................................................................................. 11
1. i Technology, economic growth and causality .................................................................. 11 Convergence Theory............................................................................................................. 12
1. ii Learning, innovation and R&D........................................................................................ 13 The Industrial Worker Hypothesis ........................................................................................ 15
1. iii Changing conditions for learning................................................................................... 15 2 A comparison of high-‐tech graduation in China and India ..................................................... 17
2. i The high-‐tech outsourcing debate .................................................................................. 18 2. ii Data sources ................................................................................................................... 21 2. iii A statistical comparison of high-‐tech graduate awards in China and India................... 22
3 High-‐tech development strategy in China and India .............................................................. 26 3. i A policy comparison ........................................................................................................ 26
The legal framework for high-‐tech education and development ......................................... 28 Reform strategy comparison................................................................................................ 29
3. ii Connections between education and industry............................................................... 30 Analysis of China and India’s domestic sector compatibility................................................ 30
3. iii A historical perspective ................................................................................................. 33 3. iv Conclusions.................................................................................................................... 34
4 Learning about learning ......................................................................................................... 36 4. i Untying the learning bundle............................................................................................ 37
Routine and creative skills.................................................................................................... 37 English language .................................................................................................................. 39 Formal and informal learning .............................................................................................. 40
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4. ii The way forward............................................................................................................. 40 Conclusion: ................................................................................................................................... 43 Data sources for graduate statistics ............................................................................................. 44 Bibliography.................................................................................................................................. 47
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List of Tables and Figures
Box 1: Global Resourcing definitions ............................................................................................ 20
Table 1: Graduate numbers in high-‐tech subjects, 2001 to 2007 by degree level. ...................... 23
Graph 1: Technical graduates per million population.................................................................. 25
Box 2: Research and development definitions. ............................................................................ 31
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Abbreviations and acronyms
CAS – Chinese Academy of Sciences
CS – Computer Science
EI – Engineering Index
FDI – Foreign Direct Investment
GSLI – Global Services Location Index
ICMR – International Centre for Management Research
ICT – Information and Communication Technologies
IEEE – Institute of Electronics and Electrical Engineers
IMF – International Monetary Fund
IPR – International Property Rights
ISTP – Index to Scientific and Technical Proceedings
ITES – Information Technology Enabled Services
LDC – Less Developed Country
LPP – Legitimate Peripheral Participation
MNC – Multi-‐National Corporation
NASSCOM – National Association of Software and Services Companies
NIC – Newly Industrialised Country
OECD – Organisation for Economic Cooperation and Development
PPP – Purchasing Power Parity
R&D – Research and Development
S&T – Science and Technology
SCI – Science Citation Index
SIPIVT – Suzhou Industrial Park Institute of Vocational Technology
STIP – Science and Technology Industrial Park
ZGC – Zhonggwancun (in Beijing: China’s most prominent technological region)
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Abstract
The motivation for this thesis is the desire to develop ways to better understand—and so
improve—learning for innovation, to maximize the technological potential of an economy.
China and India’s technological capability are often assessed against a background of
Convergence Theory and economic models that regard knowledge and skill acquisition as
aggregates of generic, technical abilities. But high-‐tech industrial development has increasingly
diverse potential. Nations are likely to specialise in niche industries, and technological
development beyond industrialisation is not predictably uniform. In this changing global
technological market, skills for innovation are increasingly important relative to routine
technological competence. R&D innovation is no longer the prerogative of only the few, most
developed countries, and is increasingly necessary to gain share in the global high-‐tech market.
Similarities are often emphasized in comparisons between China and India with the West, for
example in graduate award numbers, growth in market share of global outsourcing and low
wages. However, a similarity in outputs hides considerable differences in institutional legacy,
policy and approach. In this context, graduate award numbers cannot be regarded as direct
indicators of technological potential. An international outsourcing debate recognizes the
importance of ‘quality’ of training, but fails to differentiate between formal and informal
learning and a variety of skills that contribute to capacity for innovation.
I reconsider graduate numbers as significant indicators of China and India’s technological
development, but in relation to their respective policies for education and R&D, and the
different institutions of their political economies. Despite quantitative similarities, China’s
graduate statistics are found to represent a stage in development towards an increasingly
innovative skill base, whereas India’s reflect response to market demand for routine skills.
Following several decades of state-‐led development of public-‐private collaboration, China is
‘racing to learn.’ By contrast India is ‘learning to race’: following China in only recently changing
its principal strategy towards commercialisation of research. This suggests India is ‘catching-‐up’
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with China in strategy, though the reverse is often assumed based on outsourcing figures and
established markets.
Given the analysis, we would expect to see China increasingly competitive in the market for
global high-‐tech resources. There are some signs of this, but not as many as we might expect.
China has strong institutional structures, the prerequisite for successful R&D, but has paid less
attention to nurturing individual technical creativity. If China’s strengths in institutional, formal
learning were matched by equivalent success in individual, informal learning, it could realize its
formidable technological potential. Ethnography is the best way to understand individual
successes and failures in access to the global high-‐tech labour market. This thesis suggests that
such ethnography is the missing link needed for China to fulfil its competitive advantage, by
understanding the informal learning needs of its high-‐tech workforce. Through understanding
local examples of creative high-‐tech learning, ethnographic studies can help nurture systemic
spread of technical application and innovation.
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Introduction
This thesis compares the technological human potentials of China and India. The question of
whether China will surpass India in its growth of technological talent is interesting because the
two countries are more often considered together in comparison with the West, particularly
America. The typical comparison takes one of two approaches, both making aggregate
assumptions about learning characteristics in China and India. The first assumes similarities –
through graduate numbers and growth in high-‐tech resourcing. The second assumes
differences, typically highlighting each country’s specialisation – China in high-‐tech
manufacturing and India in service outsourcing. In either case, similarities or differences
respectively serve to justify comparison of China and India as an Asian threat to Western
technological superiority.
Citing graduate statistics is common practice in this context. The most recent detailed statistical
comparison was Gereffi and Wadhwa’s analysis of graduate awards in the US, China and India
up to 2004. Although China and India far outstrip the US in numbers, they concluded that the
US produces a much higher percentage of talented high-‐tech graduates and that ‘quality,’ not
quantity, is the key variable. Consequently later discourse on comparative human capital in
global resourcing has focused largely on ‘quality.’ Despite variations in cultural definitions of
quality, some sources assume the definition as understood (e.g.Machrotech, 2009; OECD, 2007)
and at most, it is short and general, such as “the outcome of performance indicators” (Varghese,
2006, p3), or “improved suitability [for job applications]” (Farrell et al, 2008, p37). Such
assumptions ignore changing conditions of informal learning in the context of a country’s unique
development path.
This thesis shifts the focus from an East-‐West comparison to consider China and India’s
comparative learning advantages in the context of a globalised economy. It seeks to regain the
balance between quantitative and qualitative comparison through an analysis of graduate
statistics in light of the educational Research and Development (R&D) policies of each state. In
chapter 2, I conduct an analysis of recent graduate statistics, focusing on change over time, from
2001 to 2007. The analysis demonstrates similarities in output, both in absolute terms, and in
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growth rate. In chapter 3, the figures are then considered in the context of each country’s high-‐
tech development policy.1 Although economic outcomes are quantitatively similar to date, they
result from different approaches. This suggests the quantitative measures may reflect different
underlying individual, organisational and national learning curves.
The comparison suggests that China’s statistics represent a stage in the deliberate development
of an increasingly innovative skill base, whereas India’s statistics reflect response to market
demand for routine skills, and are less coordinated towards the systemic, cultural spread of
innovation necessary for sustained technological prowess. China’s thus-‐far unrealized
technological advantage reflects in its smaller share of the global R&D market. The final chapter
shows how ethnography can give insight into informal learning processes that could help China
fulfil its technological potential.
1 I use ‘high-‐tech development policy’ to cover Education, Science and Technology and Research and Development strategies, considered in detail in chapter 3.
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1 Technology, development and learning
The underlying principle of this thesis is the desire to develop ways to measure, evaluate and
improve learning for innovation so as to maximise the technological potential of an economy.
Technology and learning are intrinsically connected: the development of complex technology to
work the environment for material productivity is definitively human (Seyfarth and Cheney,
2002). Since our inception, humanity has evolved and transmitted increasingly sophisticated
skills to improve efficient production and living standards. As the learning required for those
skills increased in complexity, specialisation occurred. Post-‐industrialisation, the development
of processes relying on complex machinery gave rise to further division of labour and the
development of ever more specific skill sets (Mokyr, 1995).
Economic modelling is concerned with the aggregate result of learning processes; learning is
regarded as uniform and standardized as ‘knowledge acquisition’ and its aggregate product as
‘human capital.’ Economic models are therefore concerned with the combined product of
learning – the level of ‘technological competence’ of the economy (Mokyr, 1995). Human
capital increases in value as the labour force absorbs the spread of new technology (EIU, 2004).
“Learning by doing” theory, for example, models this process of absorption as the top-‐down
spread of technical skills that remain the same, regardless of time or place (Arrow, 1962; Solow,
1997).
1. i Technology, economic growth and causality
Technology’s role as exogenous or endogenous to economic growth is historically debated. The
Malthusian tenet, that population growth would inevitably overtake technical progress, held
sway until laissez-‐faire progressivism swung the pendulum in the opposite direction, fixing
technological progress as inevitable, “accelerating and capable of teleological import” (Ball,
1957). Neoclassical economics treats technological change as an externality (Grubler, 1998): in
its equilibrium growth models, the long-‐term (natural) growth rate equals the sum of the growth
rate of the working population and technical progress (Stout, 1980). Exogenous Growth Theory
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therefore does not explain how change in technologies occurs; technical change itself is seen as
the explanation along with simple accumulation.
Alternatively, Joseph Schumpeter’s model made R&D the chief driver of growth, through its
incentive for innovation. ‘Creative destruction’ describes processes of endogenous destruction
and replacement by factors that increase productivity. Following his theoretical heritage and
the Information and Communications Technology (ICT) boom in the 1980s, Endogenous Growth
Theory sought to explain the gains of the New Knowledge Economy. Technological innovation
and investment in human capital were modelled as internal variables driving growth (Mokyr,
1995). Endogenous growth theory recognized that technical progress is not an “independent,
given, force…it is the rate at which a bank of technical knowledge is applied” (Stout, 1980,
p159), and is therefore responsive to policy incentives for innovation.
Regarding the application of technical knowledge as a primary cause of growth has important
implications for learning. Applicability is subject to cultural and institutional difference; learning
is not the automatic result of technical change, but directs it, at the heart of technical progress
and economic development. For this reason, Endogenous Growth Theory sits uncomfortably
with economic models for ‘knowledge acquisition’ and ‘learning by doing.’ I believe the
economic conceptualisation of learning is not yet adequately revised.
Convergence Theory
Assumptions that aggregate levels of technological know-‐how are key differences between
highly and less developed countries underlie current mainstream approaches to development
policy (EIU, 2004).
Convergence Theory (or ‘catch-‐up’) emerged as an explanation of the rapid industrialisation of
Asian economies following the Second World War and forms the theoretical base for policy in
many less developed countries (LDCs) today (Kopp, 2008). It says poorer economies can grow at
faster rates than richer economies, causing convergence of per capita income and productivity.
Capital investment boosts productivity and incomes to increase the growth rate beyond that of
leading economies, as the gap between existing and new technology levels is greater in the LDC.
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Convergence is seen to occur through imitation and replication of existing technology, but this
process no longer implies technological teleology, as was assumed when the theory became
influential during the 1950s and 60s.2
Successful convergence requires what Abramovitz (1986) terms ‘social capabilities’ (1986).
However, he recognizes difficulty in measuring a nation’s technical competence: “The trouble
with absorbing social capability into the catch-‐up hypothesis is that no one knows just what it
means or how to measure it” (ibid, p388). He makes a distinction between competence at the
institutional level – industrial, commercial, financial and educational – and that of human
capital, the aggregate level of individual skills. Additionally, levels of competence are
distinguished from an economy’s openness to technological improvement.
The ‘catch-‐up’ hypothesis therefore regards knowledge flow from leader countries to followers
as a priori. This ‘supply-‐side’ approach derives from the theory that market forces across
borders naturally give rise to convergence in terms of trade (Rodrik, 1998). It leads logically to
attempts to assess facilities for the diffusion of knowledge and the potential for structural
change – leaving the government assigned to a minimal, ‘gatekeeper’ role. This thesis suggests
a comparative approach can be developed on the demand side, to understand informal learning
processes and cultural receptivity to technological change. Understanding some learning as
non-‐uniform and culturally defined, within an economic convergence model, creates a space for
government involvement in the creation of locally appropriate paths of aptitude development
for high-‐tech workplaces.
1. ii Learning, innovation and R&D
Links between systematized R&D in industry and economic growth grew in importance at the
beginning of the 20th century, with the establishment of chemical and electrical industries and
the foundation of the first industrial laboratories (Pavitt, 1973). R&D expenditure during the
Cold War clarified that political and economic needs can direct technological innovation (Taylor,
2004). Historically, developed economies’ governments have relieved the heavy costs for
2 Martin and Sunley, 1998, provide a concise overview of the historiography of Convergence Theory and its evolution through Endogenous Growth Theory.
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investment in long-‐term, experimental research prior to the development of new products; such
basic research is unlikely to occur without incentives or certainty of commercial success, but is
instrumental in keeping a lead in technical innovation. This is why convergence theory regards
‘leading’ economies as most likely to plan R&D. Imitation is sufficient for catch-‐up but growth at
the technological frontier occurs only through innovation (Grubler, 1998).
This theory may hold true for industrial manufacturing; however, high-‐tech development is now
so fast that the transaction costs and delays in its diffusion may render it less efficient than
domestic innovation. In a prophetic article in 1980, Stout predicted that cost reductions for
innovation following the onset of microprocessor manufacturing might dramatically change the
landscape of national and global technological development. Earlier, innovation multiplied
‘muscle,’ and added to the advantages of economies of scale. Diffusion of microelectronic
technology, and its widespread application across sectors and industries, augments ‘brain,’ and
“makes possible the economic decentralization of processes,” (Stout, 1980, p164). A
characteristic of microprocessor manufacturing that also holds true for the knowledge industry
is that product innovation at the supply side often equals process innovation for the user. “So
dramatic are the savings…the effective constraint this time is likely to be the mismatch between
traditional skills and the newly necessary skills, in programming, in personal services, in
interfacing between the intelligent machine and the user and in providing for newfound
leisure…the growth of demand for adjustment will be formidably fast; the growth of supply of
adjustment, through education…and multi-‐skill training…may lag a long way behind in all but a
handful of the most adaptable and successful economies,” (ibid, p161).
In other words, technology may have already changed the nature of competition and economic
growth. Specialization and comparative advantage are less subject to environmental constraint
than previously (Farrell et al, 2008). The implications for definitions, and development, of
productive learning are profound. Whereas innovation based competition was possible only for
a minority of leading economies in the past, it is not only more available now, but may be
requisite for technological convergence. In this case, learning to innovate should be at the heart
of development policy; it must no longer be marginalised as the automatic result of successful
development.
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Government support for learning processes that contribute to growth is therefore increasingly
relevant, in particular the acquisition of high-‐tech skills instrumental in creating new production
possibilities through innovation. It is proven that basic R&D facilities play a key role in nurturing
such skills (Cohen and Levinthal, 1989); but there is less understanding of the informal learning
necessary for local innovation.
The Industrial Worker Hypothesis
Convergence theory predicted uniformity of learning and gave rise to the Industrial Worker
Hypothesis, that: “the more similar nations are in their industrial development, the more their
workers will resemble each other [and] whatever their cultural socialization…will respond to
machine technology and industrial organization in much the same way,” (Form and Bae, p621).
Evidence for the hypothesis comes from studies of industrial manufacturing in different
cultures. However, the theory is not yet tested for applicability to the new knowledge, services,
or high-‐tech manufacturing industries in emerging economies.3
But high-‐tech industrial development has increasingly diverse potential. Nations are likely to
specialise in niche industries, and technological development beyond industrialisation is not
predictably uniform (Stout, 1980). Greater individual autonomy is required for the application
of non-‐manual technical skills, particularly those in product and process development, whereas
traditional manufacturing requires relatively uniform technological skill development across a
workforce. Creativity in the application of technology to local circumstances may now be more
desirable for a larger proportion of the high-‐tech workforce, particularly in knowledge-‐oriented
development and services (Yusuf and Nabeshima, 2007).
1. iii Changing conditions for learning
It seems that the changing nature of the technology industry requires greater attention to
individual and cultural learning processes in that generic technological skills may be insufficient
for a growing segment of the national workforce. Governments must develop appropriate ways
3 To the best of my knowledge – I found no research in this area.
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to understand how their citizens learn, and encourage local creativity and innovation to
maintain competitiveness in global technical resourcing.
The following chapters compare China and India’s position in the global market for high-‐tech
labour. In light of the discussion above, assessing an economy’s human capital through
quantitative analysis of graduate statistics may only partially indicate technological potential. To
consider graduate numbers predictive, it is not enough to regard them as the quantitative
product of a country’s learning; their significance must be understood in relation to national
paths of development.
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2 A comparison of high-‐tech graduation in China and India
“India, the Philippines and China are often the top choices for locating IT and engineering-‐
based services for companies from the UK and the US, the main sources of demand. If US
and UK companies continue to concentrate their activities on these countries and current
rates of offshoring persist, the demand for engineers from these two countries would fully
absorb the suitable supply by 2011.”
Farrell et al, 2008, McKinsey Global Institute Report, p42.
Graduate statistics are most frequently used to compare China and India as ‘supply’ countries of
high-‐tech labour with ‘demand’ countries, in particular America (Farrell et al, 2008).
Comparisons may assess labour competition, in which case they are motivated by fear of job
losses in the West, or hopes for higher employment rates in the East (Greenspan, 2009; ICMR,
2005) or they may assess technological challenge, and are concerned with declining market
share (Gupta, 2008). Sometimes popular coverage fails to distinguish between these and
portrays China and India as a general economic threat, using graduate statistics as ‘evidence’
(Colvin, 2005; Western Morning News, 2008).
The latest detailed analysis of high-‐tech graduate awards in China and India is Gereffi and
Wadhwa’s 2005 comparison of engineering, computer science (CS) and technology graduate
statistics for the US, China and India.4 They concluded that the US produces a significantly
higher proportion of individuals “capable of abstract thinking and high-‐level problem solving
using scientific knowledge” (p4). Their findings are much quoted within an international
outsourcing debate. In the framework of East-‐West competition this debate, if not entirely
disregarding the significance of quantities, focuses primarily on ‘quality’ of high-‐tech skills (e.g.
Farrell et al, 2008). I take up this theme later.
This chapter reconsiders graduate numbers as significant indicators of China and India’s
technological development. The use of graduation statistics in selected subjects may not fully
4 Gereffi et al. published follow-‐up articles in 2006 and 2008. Their data sets are for 2004.
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represent the high-‐tech potential of a national economy: some knowledge industry workers
learn their skills not in an educational institution, but on their own or ‘on the job’ (Graham,
2006). However, graduate numbers year on year do represent growth or decrease in the high-‐
tech education system, and available high-‐tech labour supply.
China and India’s graduate counts in engineering, CS and technology from 2001 to 2007 are
compared below. Relative growth may indicate whether China is increasingly technologically
competitive with India. A similar comparison might be possible including or comparing awards
in natural sciences and mathematics, but lies beyond the scope of this thesis. To avoid
distorting the analysis of connections between education and R&D policy in each country, a
comparison of Chinese and Indian graduate diasporas is not included; although incentives for
returnees are discussed in the next chapter. The focus on engineering, CS and technology
provides a fair comparison of ‘potential’ at the level of qualification, since it is likely that the
majority of graduates in these subjects would choose work in the high-‐tech industry above
others (Varghese, 2006). This cannot be assumed for those graduating in natural sciences and
mathematics, though a proportion of them do indeed move into the high-‐tech industry.
2. i The high-‐tech outsourcing debate
The outsourcing debate concerns the degree to which India, and more recently China, present a
future “Asian challenge” to US technological supremacy. The effects of global offshoring on
economic relations are yet to be fully realized: improvements in transaction time, transportation
and telecommunications have globalised service and product sourcing, to the point that cost of
labour and development of new technologies across national boundaries are key variables.
Theoretically, fungible skills, for example manual labour in factories, can be sourced anywhere
(Hall and Soskice, 2001). Constraining factors are political stability and sophistication of
infrastructure. Skills for high-‐tech innovation are more usually specialized (Taylor, 2004):
constraining factors then extend to availability of labour pools with the required skills.
Whether focusing on their differences or similarities, an East-‐West dyadic implicitly suggests
cooperation between China and India. However, it is likely that if either economy is to become
increasingly globally competitive, they will do so in the first instance through competition with
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each other for the growing number of offshore jobs. This thesis is more concerned with China’s
capacity to challenge India’s supremacy in the global market for high-‐tech skills and in domestic
capacity for innovation, than with their combined impact ‘against’ the West.
Some global sourcing definitions are outlined in Box 1. ‘Outsourcing,’ and ‘offshoring’ are often
confused. However, the technical distinction between offshore, in-‐house sourcing and
outsourcing is important for this thesis. If an MNC offshores labour to its Chinese or Indian
subsidiary, no local institution is left if it later chooses to purchase labour elsewhere. If it
terminates a contract outsourced to a Chinese or Indian-‐owned firm, the local firm (with its
managerial talent, client base and organisational connections) remains (Zhou, 2008). Building
domestic institutions to meet global high-‐tech demand is therefore more sustainable for
employment and growth than supplying a specialised labour force alone (Khan and Jomo, 2000).
The next chapter discusses the extent to which China and India have done this.
The outsourcing debate is fuelled by the steady growth in global resourcing5 over the last three
decades. Offshore services in emerging markets grew at a rate of 30% annually, from 1998 to
2005. In 2005, India’s offshore service revenue, at $12.2bn, was the largest in the world.
China’s market was fifth ($3.4bn), after Ireland, Canada and Israel (Farrell et al, 2008). China is
developing its base in high-‐tech manufacturing skills, whereas India’s strengths are in IT-‐enabled
services (ITES), for example business process outsourcing (Fuller and Narasimhan, 2007),
including higher-‐end knowledge processing in finance, accounting and insurance (Mitra, 2007).
The debate concerns both quantity and quality. Confusion over quantitative data is common,
due in part to different classifications of graduates used in each country (I discuss this in more
detail later). Colvin argued Americans may be “destined to be the scrawny and pathetic dweebs
on the world’s economic bench” (2005, p1), based on the remarkable fact that China and India
are producing six times more engineering graduates per year than the US.
5 The process a company goes through to decide where to locate its activities and who will do them (Farrell et al, 2008).
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Box 1: Global Resourcing definitions
Source: author
This assumes a model in which a count of engineers is a proxy for position along a standard
linear ‘catch-‐up’ progression. Others (Raghavan, 2006; Graham, 2006) replied that the superior
“quality” of the American graduates will preserve America’s technological lead. These two
extreme positions over-‐simplify the nature of learning and the complex interdependency of the
global high-‐tech market – and ignore its volatility.
Outsource onshore Service or development contracted to a third party with specialized knowledge, within the demand market
Outsource offshore No location or organizational restrictions on service provision and product development other than market forces
Captive onshore Constrained by the need within the firm for local customer contact and/or knowledge
Captive offshore Products developed or services provided in-‐house, but outside their destined market
Control
Location
In-‐house
Outsource
Onshore Offshore
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Accurate comparison of graduate statistics is the first step towards understanding growth in
national technological competency. Rather than using these numbers alone as predictive
measures of catch-‐up development, Chapter 3 analyses their role in national high-‐tech and
education strategies. This is an alternative application of data normally used to fuel the
outsourcing debate, which models learning as a product, not cause, of technical progress. This
thesis holds, to the contrary, that learning drives development. Technological potential must be
measured as national capacity to produce innovative technologies and organizations, not to just
supply high-‐tech labour. National technological strength helps shape the global market, rather
than merely being subject to its volatility.
2. ii Data sources
Data source details are listed at the end of the thesis.
Primary data sources were used when possible; these were more accessible for China than
India. Some data for India was taken from graphs on the Government of India website. Those
figures are rounded to the nearest 500 graduates.
Different engineering, computer and technology subject areas are amalgamated. The use of
different sources of statistical data for each country might be fair grounds for doubt, but I am
confident that secondary sources had first hand contact with primary sources that were
unavailable to me. Additionally, Gereffi et al. sourced data from the Chinese Ministry of
Education and Indian Science and Technology department representatives through personal
contact.
The Chinese Ministry of Education produces aggregate statistics from provincial reporting; but
there is no national standard of definition of degrees. Also, Chinese statistics may include
mechanics and industrial technical qualifications under ‘engineering,’ which is not so in India.
This may suggest the comparison is slightly biased towards China in absolute terms, however,
for the purpose of this analysis the positive annual growth is more significant than absolute
comparisons. The quantitative bias for China may also be balanced by the fact that provincial
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enrolments for degree subject specializations of less than 10,000 are not forwarded on to the
national statistics (China Statistical Yearbooks, introduction, Education Science and Technology).
Gereffi and Wadhwa’s qualitative research, including interviews with Chinese and Indian
universities regarding graduation definitions and specializations has been invaluable. I have also
used consultancy reports such as the McKinsey Global Institute paper, “Demand for Offshore
Talent in Services.” Although not necessarily conducted with academic rigour, this was a useful
source for understanding the debate.
2. iii A statistical comparison of high-‐tech graduate awards in China and
India
In this section I present data in engineering, computer science and IT degree awards in China
and India, from 2001 to 2007.
An entirely accurate comparison is impossible due to the different classification criteria for
professions and subjects across international and provincial borders and local institutional
settings. An ‘engineer’ for example, varies in definition according to context. In academic
circles s/he may be “a person capable of using scientific knowledge to solve real-‐world
problems,” (Gereffi and Wadhwa, 2005). But for the purposes of calculating populations,
quantitative criteria must be used, for example, the number with ‘engineering’ in their job title,
or with a graduate degree in a subject related to high-‐technology. In China, classifications follow
the Soviet model adopted during the Mao era, which used the term ‘engineering’ (工程 -‐ gong
cheng) to apply to a broad category of science and technology (Rongping, 2003). The
outsourcing boom in India and the comparatively lower cost of CS and IT awards, compared to
traditional engineering, has led to greater numbers of Indian students graduating from
‘engineering’ institutions, with computer training, but no traditional engineering content
(Varghese, 2006). For these reasons, combined graduations in engineering, CS and IT seems
most accurately comparable.
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Table 1: Graduate numbers in high-‐tech subjects, 2001 to 2007 by degree level.
Notes: MCA = Master of Computer Applications. This is a Masters Degree created in 1997 offering foundation
skills in CS to individuals with a bachelors degree in a different subject. At graduation, the knowledge
base is roughly equivalent to that of an undergraduate award in CS.
NA = Not Available; I was unable to find data.
Sources are listed in detail in Appendix I.
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Table 1 compares graduate numbers in engineering, CS and IT in China and India between 2001
and 2007. China is consistently producing between two and three times as many technology
graduates as India. Both China and India’s technology graduate numbers grew steadily at
roughly the same rate between 2001 and 2007. Over the period studied China’s growth rate
(2.7) was fractionally higher than India’s (2.6), and China produced 2.4 times as many
technology graduates as India. However, there is a notable difference between the two
countries in post doctorate awards. India produced a roughly constant 700 annual PhDs, where
data was available, whereas China saw a dramatic increase from 5000 in 2001 to 14,479 in 2007.
Additionally, the postgraduate percentage share of the total in India declined from 15.3% in
2001, to 6.2% in 2007.6 By contrast, China’s increased from 10.2% to 15.3% over the same
period. This suggests that the aggregate level of high-‐tech skills in China steadily improved over
the time period studied, whereas the type and level of skills appears to have remained static in
India.
However, national competitive ability may be better judged by per capita rather than absolute
comparisons. Graph 1 shows the data in Table 1 as graduates per million population for each
year in China and India. China’s population (1.32bn) is 1.2 times India’s (1.12bn), so with 2.4
times as many technical graduates, it is producing twice as many graduates per capita. This may
be a significant indicator of increasing technological competence. Gereffi and Wadhwa’s (2008)
recommendations for US educational strategy are in part based on the belief that a higher
proportion of engineers per population correlate to a country’s capacity for innovation.
Despite these differences, it is easy to see how similarities in numbers and growth rates in an
isolated statistical analysis might lead to the conclusion that China and India’s contribution to
high-‐tech global resourcing is differentiated mainly by sector specialization, not by technological
potential, and that therefore they are either equally competitive, or that competition between
the two is irrelevant. However, looking at the graduate numbers in the context of the two
countries’ political economies, their education strategy and their policies for R&D suggests quite
different implications for national learning and, consequently, future economic advantage.
6 I did not include MCAs in the calculation of postdoctoral percentages for India. Despite the label ‘Masters’ they are conversion degrees, to the equivalent of bachelor level skills in CS.
25
Graph 1: Technical graduates per million population
26
3 High-‐tech development strategy in China and India
“China has excelled in mobilizing resources for science and technology on an unprecedented
scale and with exceptional speed and is now a major R&D player.”
OECD, Review of China’s Innovation Policy, 2007: p22.
“Science teaching and research face a challenge in Indian universities. A major reason for
this trend is that the career in science is not attractive like a profession in business
administration or in politics. Teachers refuse to undertake research…and are resistant to
major structural changes in the system.”
Varghese, 2006, p12.
A comparison of graduate statistics bodes well for China’s competitiveness, but should not be
taken as conclusive. For the statistical trajectories to be meaningful, it is necessary to view
them in the context of government strategy and historical perspective. In this chapter, I
compare China and India’s policy objectives for high-‐tech skill development and their impact in
each country.
3. i A policy comparison
China and India have both liberalised previously centrally planned economies. Following Deng
Xiaoping’s ‘Gaige Kaifang’ (‘reform and opening up’) in the early 1980s, government owned
research institutions in China gained greater control of funding; then later competition was
introduced into the science and technology sector (Rongping, 2003). India’s reform, instigated
by economic crisis in 1991, was a dramatic but fragmentary process subject to sectional politics
and difficulties in consensus (Desai, 2007). Widespread market liberalisation occurred in the
1990s, more rapidly than China’s ‘step-‐by-‐step’ approach. Structural adjustment conditions of
an IMF loan led to an influx of FDI particularly from the global software outsourcing industry
(Goyal, 1996).
27
The two countries are hailed as likely great technological powers of the 21st century (ICMR,
2005; Mitra 2007; Gupta et al, 2009). Similarities are emphasized in comparisons between them
and the West, for example in graduate statistics (Gupta et al, 2009), FDI relative to GDP, growth
in market share of the global outsourcing industry (Williams, 2009) and low wage comparisons
on a PPP basis (EIU, 2004). However, a focus on similarity in outputs hides considerable
differences in institutional legacy, policy and approach.
China’s industrial know-‐how was developed under the Soviet training programme during the
Cold War, and after the Sino-‐Soviet split, through a drive for self-‐sufficiency. Inheriting
centralised institutions, high literacy levels and widespread basic technical understanding
(Bramall, 2006), the state continues to drive changes in science and technology (S&T)
infrastructure. Chinese reform focussed on creation of a legal infrastructure for parity with
international standardization, and nurturing an R&D infrastructure that coordinates education
and skills training with commercial enterprise (OECD, 2007). From a position of relative
isolation, the Chinese state has proactively sought international educational and business
collaboration.
India’s industrial development was inseparable from its colonial past, which also left a heritage
of international development relations, widespread familiarity with the English language and
ground to establish a democratic legal system. In contrast to China’s style of self-‐sufficiency,
India’s infant industry protectionism under Nehru was not isolationist. Following Washington
Consensus criteria for governance (see Mavrotas and Shorrocks, 2007) in the 1990s, India lifted
barriers to foreign investment and allowed the outsourcing business to flourish. India’s high-‐
tech boom was market-‐driven, compared with China’s state-‐driven development coordinated
alongside educational reform.
These distinct reform paths imply underlying differences towards convergence. Greater reliance
on the market to lead growth implies faith that technological advancement, including skill
development, will result naturally. Greater institutional guidance during liberalisation directs
technological change to influence the market.
28
The legal framework for high-‐tech education and development
China’s reform put in place a legal infrastructure that to some extent already existed in India as
a legacy of colonialism and democratic independence. A noticeable difference between Indian
and Chinese legislation is the degree to which ideology or pragmatism dominates statements.
This may reflect the different periods in which legislation was formed and their ideological bent.
Indian S&T legislation, formed under newfound independence, seems written as strategic vision.
By contrast Chinese legislation, written to guide reform of changing state-‐market relations, is
pragmatic; it reads more like operational policy. The differences in legislative style also reflect
historical and ideological orientations that I bring to the fore in the rest of the chapter.
China’s institutional reform was supported by a proliferation of new legislation over the last 20
years. These include statutes in three broad categories relevant to science, technology and
research (Yong, 2008). First, primary law such as the Technology Contract Law 1987, and the
Science and Technology Progress law 1993, advanced the commercialization of science and
technology. In the second area, concentrating on enterprise innovation, universities were
encouraged to exploit the economic value of research by setting up their own companies (OECD,
2007). The third phase covered regulations for International Property Rights (IPR) protection,
including Patent Law and a Statute for Computer Software. Ministries disseminated circulars
promoting the popularization and dissemination of S&T (Rongping, 2003). Last year alone, the
State Council issued over 60 supporting policies for innovation, covering taxation and the details
of state financial input (Yong, 2008).
India’s legal framework is older and was, until recently, strongly protectionist. Its 1958 Scientific
Policy Resolution created a Council of Scientific and Industrial research, to develop technology in
specified areas, most importantly agriculture.7 The Technology Policy Statement, 1983,
increased the drive for “self-‐sufficiency…[and] major technological break-‐throughs in the
shortest possible time for the development of indigenous technology.” A political shift occurs in
the Science and Technology Policy 2003, which sees it “essential for industry [as opposed to
government] to steeply increase its investments in R&D.”
7 See Commentary pages, S&T department, Government of India website for details.
29
Reform strategy comparison
Prior to reform, both nations encouraged technological know-‐how, India placing emphasis on
productivity gains in agriculture,8 China more on heavy industries in its interior, and successful
small-‐scale industrial enterprise, if not agricultural (Bramall, 2006).
Post-‐reform, China’s approach to technology development continues to be strongly proactive.
The state has developed bases for product development in manufacturing and created links to
kick-‐start commercial high-‐tech enterprise. For example, Lenovo, a globally successful computer
manufacturer, was nurtured in the Institute of Computing Technology of the Chinese Academy
of Sciences; and the Founder Group, a technology conglomerate, originated in a joint project
between Beijing and Tsinghua Universities (Zhou, 2008).
By contrast, India’s reform strategy has been criticised for being non-‐interventionist (Ahuja,
2000). Like China, education strategy encourages increases in engineer and computer science
graduate awards, but the goals of the S&T higher education system have not been
systematically restructured towards business innovation. Educational institutes are suppliers for
the labour market in India, but are not oriented towards creating it. This may mean India is
over-‐reliant on its global position as a supplier of comparatively low-‐waged technical workers.
Policy states that industry will lead the way in developing high-‐tech research facilities in India
(S&T policy, 2003).
The 2003 policy does, however, show some return to interventionism and policy makers have
recently expressed concern about China’s rise and its lower wages (Mitra, 2007). However, the
rhetoric of intervention in each state is notably different. China’s concern is with institutional
reform, directing convergence of educational R&D towards commercialisation and industrial
partnership. Indian policy is oriented to the individual, focusing on democratic importance of
equality of opportunity; for example, key policy objectives are: to continue to increase the adult
computer literacy rate and access to ICT for the masses (S&T policy, 2003), to build technical
capacity in the rural areas and to decrease inequalities. This seems in keeping with India’s
8 Industry was the sixth priority after five agricultural, water and housing related priorities, in its 1983 Technology Policy statement.
30
egalitarian polity and a political framework increasingly subject to the necessities of vote bank
capture (Desai, 2007).
3. ii Connections between education and industry
Culturally embedded knowledge oriented into a positive cycle for technical progress is the
hallmark of a successful industrial economy (Mokyr, 1992). In practice, the systemic diffusion of
useful and reliable knowledge through social institutions, particularly the academic system, is
difficult to achieve (Gaukroger, 2006); but once successful, the boundaries between scientific
knowledge and technical application are not always discernible. For accounting purposes, the
distinction is made between basic and applied research (see box 2); a better way to understand
developing a country’s skill base may be distinguishing between innovation and replication. The
more technologically competitive a country, the more likely it will have oriented its R&D
education programme towards innovation (Cohen and Levinthal, 1989). A reorientation in this
direction requires the establishment of lasting connections of trust between educational and
industry personnel, as well as physical infrastructure (Zhou, 2008).
Analysis of China and India’s domestic sector compatibility
Government strategy in China promotes coordination between institutions and business. Until
the late 1990s, China relied largely on imported technology and its S&T capability lagged
economic growth. FDI did not contribute as much as hoped to technology transfer, and
government intervention has since reversed the trend, with a focus on improving capacity for
innovation (OECD, 2007). 80% of large domestic enterprises have already established
cooperation partnerships with universities and research institutes (Rongping, 2003). As the
central government role became less managerial and narrowed its focus to strategy formation,
it created a National Leading Group for S&T and Education to organise a long-‐term plan for 2006
to 2020. This group promotes cooperation between industry, universities and research
institutes. To date, about a quarter of the 750 R&D centres established by foreign firms in China
are joint units with universities or public research institutes (OECD, 2007).
31
Box 2: Research and development definitions.
Research and Development definitions
Basic Research
• Usually carried out by universities • No commercial aim • Expensive, long-‐term investment • If successful, creates sustainable
technological edge for the national economy
Applied Research
• Experimental • May have a particular aim • Known but unquantifiable commercial
impact • Development of an entirely new product
Process Development
• Creation of new production processes • Extension of existing production
processes • May have quantifiable commercial
impact; often spill-‐over improves efficiency
Product Development
• Improvement or extension of existing products
• Quantifiable commercial impact
Sources: Varghese, 2006; Gereffi and Wadhwa, 2005; author.
Education-‐industry connections impact the learning curriculum; for example, Shanghai Jiao Tong
University continuously modifies its curriculum in engineering in response to developments in
automotive design (Rongping, 2003); the Suzhou Industrial Park Institute of Vocational
Technology (SIPIVT) has introduced an ‘order-‐driven’ training model, under which it “selects
students together with enterprises and cooperates with them to design labs, set specialities and
courses and create teaching programmes” (OECD, 2007, p42). Kowalenko (2004) suggests a
connection between international investment in technical education in China and IBM’s decision
to hire thousands of programmers there in 2004.
India has mixed success in establishing connections between its education system and industry.
Driven by successful outsourcing and ICT investment, professionally oriented graduate awards in
32
computer science, engineering and software development have mushroomed. This seems the
most obvious nation-‐wide connection between education and industry. Rather than a state-‐led
coordination of public-‐private research and development as in China, there has been a reliance
on foreign firms to lead the way in establishing R&D initiatives. To some extent, this has
worked. Firms like Cisco, Microsoft and IMB are establishing new research centres in India,
despite “government indifference” and fears that students “are not exposed to research from
an early age” (Chandran, 2009, p1). However Mitra observes “India’s economy predominantly
continues to focus on absorption of existing technology rather than development of new R&D or
innovation at the global knowledge frontier” (Mitra, 2007, p13). Chandran interviewed heads of
strategy at MNCs investing in India, who complained of “few government incentives and an
education system that emphasizes rote learning” (ibid, p3). Similar concerns have been
expressed regarding Chinese education (Greenspan, 2008); however policy suggests the
government seems attuned to the need for change in learning methodology, at least in the high-‐
tech postgraduate arena.
The difference is beginning to show in attitudes in high-‐tech learners in India and China.
Varghese (2006) surveyed attitudes of students towards sciences, teaching and research in India
and found that, despite increasing enrolments in engineering and technical degrees, 59% were
dissatisfied with the ICT learning process and only 1% had international collaboration on a
research project. Quality performance indicators for the network of state funded Indian
universities are falling and several universities have closed down science departments through
lack of funding. “There is little incentive for teachers with doctoral degree to turn their skills to
research projects or collaborations,” (Varghese, 2006, p3). Anecdotal evidence from online
professional forums corroborate his findings:
“Can we withstand yet another beating, this time in higher education, by the Chinese? What
are the reasons for such a disparity?”
“The main reason is the different models of higher education being pursued in these two
countries. China has a system of funding large public universities, similar to that in the
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US…for example, out of the 795 institutes that provide post-‐graduate programmes in China,
none is privately owned.”
– Participants in Rediff business forum, 2009, June 2nd.
China’s coordination of research and industrial development is reflected in emerging publication
competence. Its ranking in S&T citation indices improved from fifteenth in 1990 to fifth in 2004
(Zhong and Yang, 2007). India ranked fourteenth in 2004, with 1.9% of global share of science
citations to China’s 5% (Padma, 2006). China now rivals the US in nanotechnology publication
citations in top scientific journals (OECD, 2007). Varghese (2006) suggests that the Indian
diaspora with technical competencies in the United States is fed by a bottleneck for
postgraduate entrants domestically – and that many students remain in the US, particularly in
Silicon Valley. The minimal growth in Indian PhD statistics reported in Chapter 2 reinforce this
view. Indian policy recognises the need to instigate “new mechanisms…to facilitate the return
of scientists and technologists of Indian origin to India,” (S&T policy, 2003) but these are not yet
detailed or implemented. Chinese policy encourages overseas education, but also provides
incentive for returners such as allowing remittance of their after-‐tax earnings. Development
parks in China employed more than 40,000 returners in 2003 (OECD, 2007), supported by the
CAS “Hundred Talents” programme. “So many Chinese expats have returned in the past few
years that Valley-‐slang has given them a special name, B2C (back to China)” (The Economist,
2009).
3. iii A historical perspective
Whereas China and India’s strategic goals are similar – technological catch-‐up and skill
development – their approaches and methodologies are quite different. China’s approach is
more akin to Singapore or Taiwan, emphasizing a nurturing state, strong incentive for the
development of domestic technological know-‐how and productive rent creation to that end
(Khan and Jomo, 2000). India’s focus has been more ideologically liberal and democratic,
focusing on building institutions to allow the market mechanism to work smoothly, and to open
doors for equal opportunity across social and regional divides. Its approach seems in keeping
with an International Development Consensus that promotes creation of transparent and
accountable governance, ideal-‐type hallmarks of developed Western democracies.
34
Although an unusual comparison to draw due to apparent ideological difference, it seems that
China’s current development methodology is more akin to the development of early
democracies than is India’s. Without the inheritance of rule of law, China has, perhaps, the
social ‘space’ to create a body of practice based in part on de facto success stories and bottom-‐
up experience which is later institutionalized legally (De Soto, 2000; Chang, 2002). It also draws
on Maoist “mass line” and historical experience of the People’s Democracy (Bennett, 1976).
This practice tradition might help develop networks of informal learning, discussed further in
the final chapter.
3. iv Conclusions
A policy comparison between the two countries gives insight into potentially different causes for
the dramatic growth in graduate numbers in engineering, CS and technology. Both have
encouraged increased graduate enrolments in high-‐tech related subjects, and the growing
offshore market for high-‐tech skills has been influential. However growth of S&T capability and
innovation is core to China’s development strategy, with an aim to move from a low-‐skill,
resource-‐intensive, manufacturing economy to a global leader in high-‐tech R&D (OECD, 2007).
For the last two decades, Chinese strategy has oriented its S&T educational programme towards
commerce and built R&D connections between education and industry (Zhong, 2008). Graduate
numbers, particularly PhD awards, are likely to have increased as a result of new courses and
research opportunities created through this reorientation.
Political rhetoric in India makes a connection between education and industry, but in practice
they are separately organised by the state and the market respectively. This suggests Indian
increases in graduate numbers were driven over the last two decades by the outsourcing boom.
The difference between the two approaches may reflect degrees of coordination possible
between economic and educational policy.
In China, there is increasing concern that emphasis on commercially-‐oriented R&D has starved
basic research funding – now only 6% of total R&D spending. The government now plans to
gradually increase basic research funding (long-‐term strategy plan, 2006). So, while China is
35
racing to learn, building capacity for basic R&D, India may be learning to race, following China in
changing its principal strategy towards commercialisation of research. This move is still
controversial in India (Varghese, 2006). This suggests that India is strategically ‘catching-‐up’
with China, though the reverse is often assumed based on outsourcing figures and established
markets.
China’s graduate statistics may therefore represent a logical progression towards an increasingly
innovative skill base, whereas India’s statistics reflect response to market demand for routine
skills. Despite some globally recognised centres of excellence, India’s statistics may not
represent the cultural spread of innovation necessary for sustained technological prowess. Put
another way: China has an institutional advantage that is yet unrealised in technological output.
Given the above analysis, we would expect to see China increasingly competitive in the market
for global high-‐tech resources. There are some signs of this,9 though not as many as we might
expect. The final chapter finds China underperforming its potential compared to India, and
suggests the missing link is a better understanding of the informal learning of its high-‐tech
workforce. Ethnography can provide that link.
9 For example, a recent proliferation of outsourcing blogs noting China’s venture into software development and comparing China and India as service providers.
36
4 Learning about learning
Sustained share in the global technology market relies ultimately on ability to innovate (Kim and
Nelson, 2000). The analysis in Chapter 3 suggested China has the necessary institutional
framework to outgrow India in technological innovation but has paid less attention to individual
technical creativity. India’s current lead may rest on wage competition, established connections
with global markets, and its workforce well trained in generic skills, notably English language,
needed to meet international demand (Mitra, 2007).
China and India are the most competitive offshore locations considering wages only; yet Farrell
et al. (2008) suggest large differences in the percentage of their engineering and technical
graduates suited to work in the global high-‐tech industry. Only 10% of Chinese candidates,
versus 25% of their Indian peers, are a good fit for the degree-‐specific occupations for which
they applied.10 Reasons cited are: lack of necessary language skills, “low quality of education,”
lack of practical skills and lack of “cultural fit” shown through interpersonal skills and attitudes
towards teamwork and flexible working hours.
Such reports suggest learning differences between the two countries, but without further
detailed research might lead to assumptions regarding cultural differences or ‘quality of
individuals.’ Once again, ‘quality’ is unexplained and technical ability (perhaps included in ‘lack
of practical skills or ‘low quality of education’) is not clearly differentiated from social, business,
language or management skills. Placed in the context of the findings of chapters 2 and 3, the
percentages suggest a large margin of opportunity for China to increase its high-‐tech market
share. Put another way: China has perhaps the greatest unfulfilled high-‐tech potential in the
world.
The final section of this thesis shows how ethnography can enable realisation of that potential.
10 The survey interviewed 83 offshore recruitment managers for MNCs. Chinese and Indian percentages are low compared to regions such as Eastern Europe (50%) and China is the lowest of all cited.
37
4. i Untying the learning bundle
The idea that the market alone will cause unlimited skill development is no longer tenable.
Endogenous growth theory challenged this assumption, modelling the role of institutions in the
nurturing of talent for innovation. An economy that relies primarily on supplying low wage
labour for the global technology market is therefore, effectively, feeding domestic production
factors to the global market, rather than feeding the domestic economy through the global
market. The analysis in previous chapters suggests that India’s political economy may be more
inclined towards fulfilling the first scenario than China’s. India has, of course, developed some
internationally renowned institutions for R&D innovation – but they may not represent skill
development as systemic spread.
What nurtures systemic ability to apply technical skills creatively to solve real world problems is
an elusive and historically debated question (Mokyr, 1990, 1995; Grubler, 1998). It is clear this
ability was more prevalent in some circumstances than others, for example, 17th century Britain
(Grubler, 1998) or during the last few decades in Silicon Valley (Graham, 2008). Institutionalized
connection between technical education and industry is a prerequisite (Yusuf and Nabeshima,
2007) but the evidence above suggests it is insufficient alone for China. Little is yet understood
about favourable and unfavourable local circumstances for spreading innovative capacity in the
current high-‐tech market; economic models such as the Industrial Worker Hypothesis assume it
is an acultural result confined to highly developed countries. However ethnography can provide
different answers. Clearly defining different skill sets is the first step, understanding their
relative importance is the second, identifying the circumstances in which they propagate is the
third.
Routine and creative skills
Distinguishing between the different economic roles of routine and creative technical skills
helped unravel the neoclassical teleology that technological progress was both cause and effect
of growth. Routine skills are those that result from spread of existing technology – ‘learning by
doing’ fits this category. Creative skills, crucial for innovation, require local knowledge and
38
application; they cannot be predicted by economic models.11 Creative technical skills are those
that cause growth beyond the current technology paradigm12 (Kuhn, 1962) and lead to
sustainable long-‐term development.
Gereffi and Wadhwa incorporate this distinction into two ideal types, ‘transactional’ and
‘dynamic’ skills at either end of a spectrum (2005 and 2008). Transactional engineers possess
“solid, technical training” and are “responsible for routine tasks in the workplace” (2005, p21).
They typically hold technician awards or diplomas. They graduate from “lower-‐tier universities”
with less emphasis on research or interdisciplinary opportunities. Dynamic engineers “are
individuals capable of abstract thinking and high-‐level problem solving using scientific
knowledge, and are most likely to lead innovation.” They “thrive in teams, work well across
international borders, have strong interpersonal skills and are capable of translating technical
engineering jargon into common language” (ibid). They are more likely to graduate from top
international institutions.
Gereffi and Wadhwa’s definition is a step up from the majority of analyses of outsourcing, which
refer to ‘quality’ as the main variable for success, without exploration or qualification. However,
their research addressed reform of the high-‐tech graduate curriculum in the US, and their
description is somewhat imprecise. Their one sentence definition of ‘dynamic’ engineers
(above) includes cognitive ability, knowledge acquisition, proven behaviour, social skills,
professionalism and familiarity with certain environments, technical translation, language skills
and international exposure. Their distinction is useful as a starting point, but does not address
causes. More dynamic skills are perceived to represent better quality education – but there is
no analysis of what that means and how those skills are attained.
The differentiation between routine and creative skills underlies the ‘quality’ debate in
outsourcing discourse (see section 2.1). However, the discourse assumes the move from routine
to innovative skills is incremental, and that more creative technical skills are learned by
progression up the qualification ladder. Conceptualising the difference in this way relegates
11 Endogenous Growth Theory models technological innovation as part of the growth cycle, but does not attempt to quantify it. 12 Although Kuhn’s theory of paradigm shift was exogenous, his ideas extended beyond exogenous neoclassicism.
39
learning to formal education and disregards other learning processes. This thesis suggests the
locally specific contribution of informal learning to innovative technical ability makes the
difference between scarce, haphazard innovation, and its systemic embededdness.
English language
Although English language competence is not yet common amongst the populace, Chinese
educational policy now prioritises it. High school graduate exams for university entrance include
English as compulsory. To attend the top universities, students must obtain near perfect scores
on their exams — including English; “the result is that top university graduates in China do have
good English language skills,” (Hong, 2009).
English is widely spoken in India and is the common language for education and business.
English competency is commonly cited as a benefit in marketing to attract outsourcing to India
(e.g. Machrotech, 2009). English competence in India, versus deficiency in China, is cited as the
primary reason that India will maintain its rank as the top services outsource destination (GSLI,
2009). Anecdotal evidence from interviewing company recruiters in China says that language
deficiency is the most pressing issue in China, versus “the overall quality of the education
system in India” (Farrell et al, 2008, p31). Is it really that important?
Possibly it is; possibly not. It has not been measured in relation to other skills, so we do not
know exactly how much it matters to high-‐tech recruiters and whether its desirability varies per
sector or level of technical competence required. We also do not know what role it might play,
if any, in innovation in China. There is no doubt that English competency has fuelled India’s
success in outsourcing. However, Ireland’s fall from one of the most desirable offshore
locations in servicing, to one of the last five in the top fifty in 2009 (GSLI) suggests English
language ability alone is not enough to influence companies’ offshore location decisions.
Until there is further research to measure the relative importance of different skills, policy
makers will not know which to prioritise to increase technological competence.
40
Formal and informal learning
The fact that China has a greater proportion of formally qualified high-‐tech graduates than India,
but less success in global offshore recruitment is sometimes accounted for by suggestions that
Chinese technical qualifications are ‘lower quality’ (Bulkeley, 2007). Given well-‐established
connections with universities internationally (OECD, 2007), the availability of international
curricula, the high-‐level of technical and scientific publishing (GSLI, 2009) this seems improbable.
This thesis suggests a gap between informal and formal learning is likely – but to understand
causes, we need to research skill gaps and how individuals learn to bridge them.
Formal education can spread universal technical skills but informal learning is culturally specific.
However, assuming that the distinction between formal and informal learning is the same as
that between technical and ‘soft’ skills is problematic. Technical skills may be learned through
universal processes, but using technical knowledge creatively is particular. Creative technical
ability must be distinguished from the acquisition of social, management, business and other
skills for recruitment, but elements of both are only acquired experientially.
Creative application required for innovation is most likely acquired ‘on the job’ through trial and
error processes, through contact with successful innovators and working in teams that inspire
experiment. How creativity is learned is a difficult question to answer, but empirical studies of
successful and unsuccessful attempts to innovate – for example, of learning journeys of people
who make successful start-‐ups in China and those who fail – will go some way towards
understanding this.
We need more evidence for how the Chinese high-‐tech workforce acquires particular skills in
using technology innovatively. Ethnography is the only available method to do this.
4. ii The way forward
An ethnography of high-‐tech informal learning in China is not yet established, but there are
studies of informal learning in Chinese societies, and of high-‐tech workplaces in the West and
the East, that establish methodological precedent. In this section I pose some questions that
41
might be asked of informal high-‐tech learning in China, and cite examples of ethnographers who
have asked similar questions in different contexts.
What values are most influential in high-‐tech learning environments in China? Stafford (1995,
2008a and 2008b) studies the difference between explicit, didactic learning and informal,
community based learning in identity formation in Chinese children. His approach explains the
transmission of values and their impact on learning; for example he shows how moral values of
filial piety are embedded in the ways that children learn and eventually come to make economic
decisions.
How do particular cultural manifestations of transmission, continuity and change meet, or
affect, the informal transmission of technical creativity? There is a contradiction inherent in the
business environment that reflects a social dialectic of continuity and displacement (Lave and
Wenger, 1991; Bourdieu, 1977). But high-‐tech learning is, by definition, the introduction of the
new at both societal and individual levels; one might conceptualise this as a bias towards
continual displacement in high-‐tech environments. In what circumstances is technical creativity
supported, hampered, or simply missing? How, where and why is it introduced and successfully
embedded in learning processes?
How does the confluence of social and working roles impact high-‐tech learning in Chinese
contexts? Fuller and Narasimhan (2006, 2007) studied the impact of a Science and Technology
Industrial Park (STIP) in Chennai on changing familial roles, in particular the empowerment of
women as workers, the effect of profession on social prestige and changing power relationships
between generations. Their study gives insight into the nature of shifting social frameworks in
India that form the environment in which high-‐tech learning takes place.
What are the power relationships encountered in learning situations and the channels that
afford or prevent interchange among communities of practice? Anthropologists Lave and
Wenger working in Silicon Valley in the 1980s developed a social-‐practice oriented model,
“Situated Learning” to study workplace participation. Their method, Legitimate Peripheral
Participation (LPP), avoids assumptions that top-‐down knowledge acquisition is embedded in
high-‐tech workplaces. It challenges understanding learning as either the cognitive acquisition of
42
propositional knowledge, or purely behavioural ‘learning by doing’ (Lave and Wenger, 1991), by
identifying discrepancies between prescribed learning techniques and actual practice.
An example is Orr’s study (Seely Brown and Duguid, 2000) of Xerox technicians. Formal
corporate training and approved procedures were mainly useless in learning to diagnose
problems in sophisticated machinery. Technicians came to know their machines “as shepherds
know their sheep,” (ibid, p1); creative solutions to problems were developed through
collaborative improvisation, and transmitted via storytelling around the coffee machine.
Individuals earned social and work prestige through their creativity in solving the hardest
technical problems.
Zhou’s (2008) study of Zhonggwancun (ZGC) is a good concrete example of the need for the
ethnographic research I propose. He suggests ZGC has the infrastructure and technical expertise
to be as successful as Silicon Valley, and then analyzes why it isn’t. He proposes young
enterprises’ “business management and marketing ability lags significantly behind their R&D
capacity” (p89). But some firms have successfully overcome these obstacles. An ethnographer
might follow several indigenous start-‐ups to discover what informally learned skills contribute to
success, how they were acquired, and which obstacles to individuals’ learning prevent progress.
Business and social networks in ZGC, established routes for the dissemination of best practice,
are thriving (p95). Through them, ethnographic case studies might contribute to the
development of a globally competitive Chinese centre of entrepreneurial excellence.
The value of each of these examples is their demonstration of informal learning as
environmentally and socially particular. This thesis has proposed that understanding the
particular circumstances conducive to informal learning of high-‐tech skills, and enabling their
cultural spread, is the missing link that will enable China’s high-‐tech workforce to reach its full
potential and surpass India in technical innovation.
43
Conclusion:
This thesis assessed China’s technological potential compared to India. It found that, through
the combination of quantitative relevant graduate statistics and policy orientation, China is
institutionally better positioned to compete in high-‐tech than India, but under-‐performing in the
acquisition of informal skills for high-‐tech development. The final chapter showed how
ethnography can help China reach its potential and surpass India in the global market for high-‐
tech resources.
For the most part, learning high-‐tech skills is understood as individually and culturally uniform,
due to economic modelling developed for industrial manufacturing and accumulation of
technological know-‐how at the national level. But regarding high-‐tech skills only in the
aggregate remains problematic. One effect is that the relative impact of different types of skills
has not been measured. Another is that particularly effective informal learning is not
understood and propagated. This may lead to loss of efficiency in learning at local and national
levels.
Ethnography of local paths of informal and creative high-‐tech learning can provide the missing
link, discovering and disseminating local stories of practice that are conducive, or obstacles to
innovation in China.
44
Data sources for graduate statistics
China data For 2003 doctorate and masters awards and all awards 2004 to 2007 the China Statistical Yearbook was used: China Statistical Yearbook, 2008, Tables: 20-‐9, Number of postgraduate students by field of study, 2007, p781 20-‐12, Number of students in adult institutions of higher education by field of study, 2007, p783 20-‐14, Number of students enrolled in Internet based courses by field of study, 2007, (engineering) p784 20-‐16, Students in secondary vocational schools by field of study, 2007 (IT graduates, with certificate of professional competence), p785
China Statistical Yearbook, 2007, Tables: 21-‐9, Number of postgraduate students by field of study, 2006, p791 21-‐12, Number of students in adult institutions of higher education by field of study, 2006, p793 21-‐14, Number of students enrolled in Internet based courses by field of study, 2006, (engineering) p794 21-‐16, Students in secondary vocational schools by field of study, 2006 (IT graduates, with certificate of professional competence), p795 China Statistical Yearbook, 2006, Tables: 21-‐9, Number of postgraduate students by field of study, 2005, p802 21-‐12, Number of students in adult institutions of higher education by field of study, 2005, p804 21-‐14, Number of students enrolled in Internet based courses by field of study, 2005, (engineering), p805 21-‐16, Students in secondary vocational schools by field of study, 2005 (IT graduates, with certificate of professional competence), p806 China Statistical Yearbook, 2005, Tables: 21-‐9, Number of postgraduate students by field of study, 2004, p694 21-‐12, Number of students in adult institutions of higher education by field of study, 2004, p696 21-‐14, Number of students enrolled in Internet based courses by field of study, 2004, (engineering), p697 21-‐16, Students in secondary vocational schools by field of study, 2004 (IT graduates, with certificate of professional competence), p698
45
China Statistical Yearbook, 2004, Tables: 21-‐9, Graduates of institutions of higher education by field of study, p782 Chinese data for 2001 to 2002, and 2003 diplomas and bachelors awards relied on secondary source, Gereffi and Wadhwa 2004.
India data PhDs 2001 to 2002: Chatterjee and Moulik, 2006. PhDs 2003 and 2004: Gereffi and Wadhwa 2005 (Sourced through NASSCOM and AICTE) PhDs 2007: Department of Science and Technology, Ministry of Science and Technology, Govt of India, R&D stats at a glance. October 2008 Masters degrees, 2004 to 2006: Statistical Abstract, India, 2007: 31.4: Number of scholars by courses and stages in recognized institutions, p572 – 573
Statistical Abstract, India, 2005 & 2006: 33.4: Number of scholars by courses and stages in recognized institutions, p480 – 481
Masters 2001 to 2004 and bachelors degrees 2003 and 2004: Gereffi and Wadhwa, 2005 (sourced through All India Council for Technical Education (AICTE))
Bachelors, 2001 and 2002: All India Council for Technical Education Annual Report for 2001 – 2002: p15 to 17. All India Council for Technical Education Annual Report for 2002 – 2003: p16 to 18. Bachelors 2003 and 2004: Gereffi and Wadhwa, 2005 (sourced from India’s National Association of Software and Service Companies (NASSCOM) and from Department of Education tables). NASSCOM statistics are compiled from several sources, private and public. The most heavily relied on are The Institute of Applied Manpower Research and the Ministry of Human Resources. They also use IndiaStat for private consultancy.
46
Masters and bachelors 2007: Taken from Indiastat enrolment figures for 2007, less average drop out, cited by percentage in Gereffi and Wadhwa 2008. http://www.indiastat.com/education/6370/enrolmentinhighereducationclassesabovexii/366801/enrolmentforbachelordegree.d.d.sc.d.phil.inindia/449448/stats.aspx Population data is taken from World Bank development indicators for China: http://web.worldbank.org/WBSITE/EXTERNAL/COUNTRIES/EASTASIAPACIFICEXT/CHINAEXTN/0,,contentMDK:20601872~menuPK:318976~pagePK:141137~piPK:141127~theSitePK:318950,00.html and for India: http://ddp-‐ext.worldbank.org/ext/DDPQQ/report.do?method=showReport
47
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