Commodity Chains and Economic Development:
One and a Half Proposals for Spatially-Oriented Research
Richard P. Appelbaum
working paper prepared for CSISS/IROWS Specialist Meeting,
Globalization in the World System: Mapping Change Over Time
Session 1: Commodity Chains and Labor in the Global Economy
University of California at Riverside
February 7-8, 2004
The purpose of this paper is
propose two vastly different approaches to studying the role
of commodity chains in the
global economy. Both use the commodity chains framework to
analyze the possibilities for
industrial upgrading. The first proposes to develop an index of
industrial upgrading in
individual countries, and then use the index as the dependent variable in
causal models incorporating
various predictors of industrial upgrading. The second, somewhat
more adventurous strategy,
proposes a commodity chains-based decision approach that would
attempt to model the complex
interactions between the commodity chain and its regional
environment. The first
approach is developed considerably more extensively than the second
(which is barely developed at
all), both because it builds on former work I have done with others
(including David Smith, who
is part of this workshop), and because it seems reasonably possible
to accomplish empirically.
The second approach is developed more briefly and speculatively,
mainly because I really have
no idea how to proceed further.
Before proceeding to the two proposed
approaches, it is important to review the
underlying theoretical
frameworks, along with some recent changes in global production systems
that are consequential for
both approaches.
SOME THEORETICAL
CONSIDERATIONS
In this section we briefly
review the concept of global commodity chains, discuss the
importance of social networks
in an increasingly globalized economy, and briefly review the
possible role of state
policies in development.
Global Commodity Chains
This notion of an
increasingly integrated global economy – where countries come to
occupy distinct export niches
and where industrial upgrading is a key strategy – can be fruitfully
understood through the notion
of global commodity chains, “network[s] of labor and production
processes whose end result is
a finished commodity” (Hopkins and Wallerstein, 1986: 159).
Global commodity chains
consist of a number of ‘nodes’ or operations that comprise pivotal
points in the production
process: raw materials supply, production, export, and marketing, taking
us “across the entire
spectrum of activities in the world-economy” (Gereffi, 1992: 94). The study
of global commodity chains,
which originated with the work of sociologist Gary Gereffi and his
colleagues has spawned a
major cottage industry in the sociology of development.
As originally conceived by
Gereffi, global commodity chains have three main
dimensions: an input-output
structure comprised of a set of products and services linked together
in a sequence of value-adding
economic activities; a territoriality that identifies the geographical
dispersion or concentration
of raw material, production, export, and marketing networks; and a
governance structure of power
and authority relationships that determines how financial,
material, and human
resources, as well as economic surplus, are allocated and flow within a
chain. While there is a large
and growing body of empirical work on all three of these
dimensions, that work has
consisted entirely of case studies of specific industries, most notably
low-wage industries such as
apparel and electronic assembly.
Gereffi has also
distinguished between two distinct types of global commodity chains –
those that are controlled by
producers, and those that are controlled by buyers (see Gereffi, 1994,
for the original formulation).
Producer-driven commodity chains refer to those industries “in
which large integrated
industrial enterprises play the central role in controlling the production
system (including its forward
and backward linkages)” (Appelbaum and Gereffi, 1994: 44). This
is most characteristic of
capital- and technology- intensive industries dominated by transnational
corporations. Buyer-driven
commodity chains, on the other hand, refer to those industries
in which large retailers,
marketers and branded manufacturers play the pivotal roles in
setting up decentralized
production networks in a variety of exporting countries, typically
located in developing
countries. This pattern of trade-led industrialization has become
common in labor-intensive,
consumer-goods industries such as garments, footwear, toys,
handicrafts and consumer
electronics. Tiered networks of third-world contractors that
make finished goods for
foreign buyers carry out production. Large retailers or marketers
that order the goods supply
the specifications (Gereffi and Memedovic, 2003: 3)
This pattern of trade-led
industrialization is common in labor-intensive, consumer goods
industries such as garments,
footwear, toys, and consumer electronics. In the current phase of
globalization, abetted by revolutions
in information technology and logistics, there has been a sea
change in global industrial
organization: Producer-driven commodity chains, which dominated
during an era of Fordist
production, are rapidly giving way to buyer-driven commodity chains in
which giant retail
conglomerates call the shots. Wal-Mart, not General Motors, is the world’s
largest corporation.
In buyer-driven commodity
chains, profits “derive not from scale, volume, and
technological advances as in
producer-driven chains, but rather from unique combinations of
high-value research, design,
sales, after-sales services, marketing, and financial services that
allow the buyers and branded
merchandisers to act as strategic brokers in linking overseas
factories and traders with
evolving product niches in their main consumer markets” (Gereffi,
1994: 99). In other words,
the highest value-added activities are often more closely associated
with consumption than
production. Because constant design changes for customized markets is
the primary source of
competitive advantage, products have become increasingly aestheticized,
emphasizing elements of
style, fad, and mystique, all of which increases the contribution of
design to the value of the product.
Thus, design-intensive activities have increased their
proportion of value generated
relative to manufacture and assembly activities. So one aspect of
the shift to buyer-driven
commodity chains is the creation of competitive advantages through
product differentiation and
customization for distinct market segments, rather than merely by
cutting labor costs: it is no
longer possible to complete exclusively on the basis of low-cost labor.
The economic success of newly
industrializing nations will largely depend on their firms’ ability
to “move up” into these
higher value-added economic activities.
A handful of peripheral
countries have engaged in industrial upgrading, shifting from
commodities like textiles,
apparel and footwear to higher value-added, technologically
sophisticated production that
requires a strong and well-integrated industrial base. This was the
pathway followed by the East
Asian newly-industrializing economies (NIES) during the 1980s
and 1990s, when regional
growth rates averaged 7-8 percent annually despite escalating wages,
labor shortages, and currency
appreciation that threatened competitiveness in the very laborintensive
industries upon which they
built their economic successes. Their pattern involved
continuous technological improvement
of production processes, the production of new products
and the provision of new
services, and otherwise engaging in higher value-added economic
activities. East Asian firms
were able to move up from what Gereffi terms “captive networks” (in
which producers are limited
to assembly of cut fabric following detailed instructions) into
“relational value chains”
entailing “more complex forms of coordination, knowledge exchange,
and supplier autonomy,”
permitting full-package production, the ability to go beyond simple
assembly and supply the
client with a completely finished product by providing designing,
sourcing, cutting, sewing,
assembling, labeling, packaging, and shipping (Gereffi, Humphrey,
and Sturgeon, 2003: 12).
The number of leading global
apparel exporting countries has increased sharply between
1980 and 2000, with many
formerly lower-tier countries “moving up” the commodity chain into
higher value-added
activities. Countries whose apparel exports exceeded US$1 billion in 1980
included only the East Asian
NIEs (Hong Kong, Taiwan, and South Korea), along with China
and the U.S. A decade later,
the list also included Indonesia, Thailand and Malaysia; India and
Pakistan; Turkey (which had
emerged as the world’s fifth-largest apparel exporter); and Tunisia.
By 2000, the list included
the Philippines and Viet Nam; Bangladesh and Sri Lanka; Morocco
and Mauritius; four East
European countries; and of course Mexico, who apparel exports had
grown from virtually nothing in
1990 to $9.3 in 2000. In that year the top five apparel exporters
were China ($39.2 billion),
Hong Kong ($24.7 billion), the United States $9.3 billion), Mexico
($9.3 billion), and Turkey
($7.0 billion). Yet there remains substantial variation in the degree to
which apparel remains a
principal export item among the world’s 25 largest apparel exporters:
In Northeast and Southeast
Asia, [apparel] has declined in importance, except in China
where it remains the top
export item, and in Indonesia and Viet Nam where apparel has
climbed to third place.
However, in South Asia, Africa, the Caribbean Basin and Central
and Eastern Europe, apparel
is the leading export, and frequently has been for a decade or
more. (Gereffi and Memedovic,
2003: 26)
If one looks at changing
geogrpahical patterns for U.S. apparel imports (see Figure 1)
during the past decade, it is
clear that Northeast Asian countries are declining in importance,
South and Southeast Asia have
stabilized, and China, Mexico and to some extent the Caribbean
Basin have increased; only
China and Mexico are core suppliers, however. For most countries
there was little change
between 1990 and 2000 (Mexico being the principal exception, thanks in
large part to NAFTA). The
countries that have been most successful in exporting to the U.S. are
those that do not engage in
simple assembly, but have developed, or are developing, full-package
production capabilities –
Hong Kong, Taiwan, South Korea in the first instance, China and
Mexico in the latter.
Figure 1: Shifts in the
regional structure of United States’ apparel imports, 1990-2000*
*Note: The 2000 position corresponds to the ring where the
country’s name is located; the 1990
position, if different, is
indicated by a small circle. The arrows represent the magnitude and
direction of change over
time. Source: Gereffi and Memedovic, 2003: p. 18
Social Networks:
Personal Ties and Spatial Proximity
Although labor costs often
are a crucial component of the calculations of businessmen
and investors, other factors
(such as market proximity, access to skilled labor, and trade barriers)
also figure in
decision-making about industrial location (Dicken, 2003). One sent of important
factors has to do with social
networks. Two different (although often overlapping) types of
social networks haved receive
prominent attention in the development literature: those stemming
from personal ties and
connections, and those stemming from spatial proximity.
Personal Ties: The ability of firms to
create informal business networks in service of
global production has
received extensive attention in the development literature, and is believed
by some scholars to be a key
ingredient in East Asia’s economic success. Chinese businesses in
particular are said to
prosper as a result of their reliance on informal personal networks and
connections – guanxi obligations of mutual
obligation and reciprocity that are frequently
mediated through family or
community ties. Integration tends to be horizontal and informal,
rather than vertical and
contractual, with horizontal coordination based on short-term needs
rather than long-term
obligations. Firms can therefore remain small and more responsive to
quickly changing market
conditions, while at the same time gaining access to the large capital,
resource, and information
pools of the business group. Such informal alliances between firms in
business groups allow the
network as a whole, rather than individual firms, to organize and
manage a large portion of the
commodity chain. Rather than using vertical integration to solve
problems of opportunism and
information flow, these problems are managed through interfirm
trust and communication.
Firms can therefore remain small and more responsive to quickly
changing market conditions,
while at the same time gaining access to the large capital, resource,
and information pools of the
business group (Orru, Biggart, and Hamilton, 1992; Hamilton and
Kao, 1990; Smart and Smart,
1991; Lui, 1998; Gerlach, 1992; Whitely, 1992, 1996; Appelbaum,
1998; Cheng, 1993; Chan,
1993; Walton, 1993; Birnbaum, 1993; Appelbaum, Felstiner, and
Gessner 2001).
Spatial Proximity: The agglomeration effects
associated with spatially concentrated,
tightly integrated
metropolitan regions (“industrial districts”) are believed to confer
competitiveness by permitting
a quick and flexible response to rapidly-changing market
demands. Such flexibility,
which results from the transactions-intensive production and supply
networks, results in a shift
away from standardized assembly-line mass production to much more
flexible, segmented
production. Industrial districts confer competitive advantage through
externalities resulting from
the physical presence of numerous suppliers and producers,
concentrated in geographically
interdependent networks of small firms, factories, and specialized
local labor markets.
Information flow is facilitated by family connections, personal relationships,
professional and
community-based ties, trade associations, tight lines of communication between
neighboring suppliers, and
common culture. Such flows permit a highly flexible organization of
production, with quick
response to shifts in market demand. Transaction costs are lowered
through proximity to markets,
the ability to quickly acquire producer goods and services, lowered
transportation and
communications costs, access to suppliers, and in general the rapid exchange
of information and knowledge
(Scott, 1988; Storper and Walker, 1989). The presence of a strong
support infrastructure – for
example, business associations, supplier clubs, and private or statesupported
research and development
facilities – can also contribute to globally competitive firms.
There is also some evidence
that small- and medium-sized enterprises may be better able to
respond flexibly to changing
market conditions than large ones, particularly if informally
networked into strong
business groupings (Doner and Hershberg, 1996).
The Role of State Policy
Firm and industry
characteristics by themselves do not account for successful upgrading.
Both unique historical
circumstances and state policy also contribute to economic growth. In East
Asia’s rapid development
during the 1970s-1990s, for example, the Cold War funneled vast
amounts of foreign aid into
the region, while the “long boom” in the core economies during the
1950s and 1960s provided
markets for exports (Appelbaum and Henderson, 1992).
Developmentally-oriented
state bureaucrats sought legitimacy by pursuing policies intended to
raise overall living standards.
As Evans (1995) has demonstrated with regard to the South
Korean information technology
industry, becoming a global competitor can benefit from the
interventions of an activist
state (what Evans refers to as ‘entrepreneurial bureaucrats’) that is
strongly connected to social
and political groups committed to development.
Examples of state policies
that promoted development include maintaining low wages
through the labor repression
in South Korea, Taiwan, and Singapore; large-scale underwriting of
a social wage in the form of
extensive public housing schemes in Singapore and Hong Kong;
investment in education and
training throughout the NIEs; and various forms of industrial policy
during the latter phases of export-led
growth and secondary import substitution in South Korea,
Taiwan, and Singapore.
Examples of industrial policy included credit control and price-rigging as
a means to prod companies
into higher value-added, higher wage and more technology-intensive
forms of production; enforced
savings, as exemplified by Singapore’s Central Provident Fund;
public investment in the
creation and refinement of new technologies, such as government R&D
centers whose results were
made available to private companies; state creation of industrial
sectors that did not
previously exist either through state companies or through the supply of
credit and financial
guarantees to private companies; and state discouragement of speculative
domestic or overseas
investment, thus indirectly ensuring its flow into manufacturing.
Occasionally developmental
policies even called for direct state ownership of key industries – for
example, banks in South
Korea, or airlines, armaments, ship-repairing in Singapore (see the
writings in Appelbaum and
Henderson, 1992; Henderson and Appelbaum, 1992; Henderson,
1993; Evans, 1987, 1995;
Amsden, 1989; Wade, 1990).
RECENT CHANGES IN GLOBAL
PRODUCTION
There are two relatively
recent developments in global production that have must be
taken into account in any
effort to model the possibilities for economic development, because
both modify the prospects for
industrial upgrading through movement up the commodity chain.
The first is the growing
power of large retail multinationals; the second the emergence of a
stratum of giant
multinational factories that are increasingly playing the role of
intermediaries
between manufacturers and
retailers on the one hand, and labor on the other.
The Growing Importance
of Large Retailers
One of the principal changes
in global apparel commodity production has been the
growing economic power of
giant retailers, who exert growing control over prices and sourcing
locations, both through price
pressures they exert on the independent labels they carry, and
through their growing volume
of private label production (now estimated to encompass as much
as a third of all U.S. retail
apparel sales). As Hamilton and Kotha (2003: 2-3) describe it,
the event of crucial
historical importance was the “retail revolution” of 1965-1980 which
created mass merchandising
giants such as Wal-Mart, K-Mart, and Target; and, later,
specialty retailers such as
Home Depot, Best Buy, Circuit City, and Office Depot, which
today, together with the earlier
established Sears, Penney's, and major grocery chains,
procure a substantial amount
of products sold to final consumers. The success of these
discount general
merchandisers and “category killers” also provided a context for the
success of specialized
distributors, marketers, and assemblers such as Nike, The Limited,
Dell, and Gateway; as well as
for an increasingly intermediary position of major
manufacturers and technology
innovators such as AT&T, GE, Compaq, and HP. Internetbased
retailing, which took off in
the last five or so years, most likely represents another
“revolution” in distribution
with profound effects on the consumer-oriented industries.
Giant retailers have grown in
size to surpass the largest manufacturers in terms of
revenues. Among retailers,
the U.S. dominates the world, and Wal-Mart dominates the U.S. The
four largest U.S. retailers
account for about a tenth of total U.S. retail sales. The world’s 40
largest retailers accounted
for nearly $1.3 trillion in revenues in 2001, nearly 4 percent of the
world GDP (derived from
Fortune, 2002). Among the top forty, twelve
are based in the U.S.
accounting for nearly half (43%) of total sales; almost all
the rest are from the EU (accounting for
46%). The only Asian firms in the top forty are five
Japanese retailers (accounting for 11%). Wal-
Mart accounts for nearly a
fifth of the combined sales of the top 40, more than three times those
of its nearest competitor,
France’s Carrefour. In fact, Wal-Mart’s 2002 revenues of $246 billion
made it the world’s 18th largest economy, roughly tied
with Switzerland. In the last few years the
giant retailer has surpassed
Exxon, General Motors, British Petroleum, and Ford Motors in
revenues, signaling the
rising power of retailers in the world economy. This suggests an
important emerging dynamic in
the global economy: the US and EU overwhelmingly control the
retail end, at a time when
retailers in general are exerting increasing control over the global
economy (Appelbaum,
forthcoming 2004).
In terms of labor, the
dominance of giant retail transnationals poses a significant
challenge to working class
organization, since their buyer-driven commodity chains are
characterized by extreme
post-Fordist production involving networks of global outsourcing and
high levels of capital
mobility. In the classical global buyer-driven commodity chain formulation,
retailers have
disproportionate control over the manufacturers who design the goods they sell
and
the factories where those goods
are made (Appelbaum and Gereffi, 1994; Gereffi, 1994, 2001).
The Gap, to take one example,
sources from 4,000 factories in 55 countries; Disney, to take
another, from 30,000
factories. Because these giant firms can place their orders anywhere on the
planet they choose, their
contractors are seen as relatively powerless price-takers, rather than
partners and deal-makers. The
effects on labor of this arrangement are mixed: one outcome is
the “race to the bottom,”
where retailers and manufacturers play off competing contractors to
force prices (and wages) down
and thwart unionization drives. Another outcome, however, is that
if large retailers and
manufacturers can be made to pressure their suppliers by consumer pressure,
gains for labor can also be
achieved – as occurred in Mexico’s Kukdong (Mexmode) factory and
the Dominican Republic’s
BJ&B cap company.
Large retailers
characteristically have large volume requirements, leading them to only
consider large producers
(1000+ workers) as potential suppliers. In the words of one African
supplier, success requires
“never deviating from a chosen product type, not trying to be versatile,
seeking efficiency on single
styles and going for longer and longer runs” (Gibbon, 2003: 33).
Related to these trends,
since the mid-1980s, there has been a move toward “lean
retailing,” particularly in
the U.S. but also in Europe and Japan. Traditionally, apparel producing
firms and retailers were
relatively independent of one another. Led by Wal-Mart and other large
U.S. retailers, and enabled
by technological changes that permitted a high degree of data sharing
and other electronic
interchanges, retailers increasingly brought their suppliers under much more
direct control, requiring
them to “implement information technologies for exchanging sales data,
adopt standards for product
labeling, and use modern methods of material handling that assured
customers a variety of
products at low prices” (Abernathy et al, 1999: 3). Such changes in
retailing favor Hong Kong,
Taiwanese, and South Korean garment firms (Gereffi, 2003), who are
well positioned to manage
triangle manufacturing (so-called because a foreign buyer places an
order with an East Asian firm which manages the production,
completing the triangle by shipping
the goods to the foreign buyer; see Gereffi and Pan, 1994:
127). As Thun
(2001: 15) notes in his
study of Taiwanese firms,
small, local firms in
Southeast Asia or mainland China may be able to undercut a
Taiwanese firm on labor costs,
but they are unlikely to be able to make the investments in
electronic data interchange
that make rapid response possible. In short, being able to
handle electronic orders from
buyers, effectively forecast, plan, track production, and
manufacture apparel quickly
and flexibly, are skills that provide a far more enduring form
of comparative advantage for
Taiwanese firms than constantly scouring the globe for the
lowest cost labor.
One study of European
retailing (focusing on Britain, France, and Scandinavia) found that
Scandinavian retailers tended
to concentrate their purchases among a relatively small number of
foreign suppliers, while
French retail sourcing was more dispersed (British retailers were in
between). The study
identified three different models of supply base management (Palpacuer,
Gibbon, and Thomsen, 2003):
_ a rules-based UK model emphasizing rationalization
of the supply chain through formal
supply chain management (SCM)
doctrines, with specialized functions centralized at
corporate headquarters
_ a market-based Scandinavian model emphasizing concentrated
sourcing networks,
achieved by establishing
strong personal relations with overseas manufacturers
_ a socially-embedded French model emphasizing more open,
informal, and dispersed
sourcing networks
The growing size and
dominance of larger EU and U.S. retailers suggests an important
dynamic in the world economy:
the experience of Hong Kong, Singapore, Taiwan, and South
Korea – newly-industrializing
economies that relied on apparel and textile production as integral
parts of successful
development strategies – may prove difficult to replicate in a world where the
retail end is much more
tightly controlled today than it was 20-30 years ago.1 Only countries with
sizeable internal markets,
such as China and India, may prove capable of moving up the apparel
chain into higher value-added
activities, insofar as they are able to capitalize on their internal
markets in developing
indigenous retail capabilities.
1 There are
other factors which make it less likely that other countries will be able to
replicate the
original
East Asian experience. For a more complete discussion, see Henderson and
Appelbaum (1992).
The Growing Importance
of Major Producers
This system of retail dominance
is being challenged somewhat by the rise of global
contractors, typically from
South Korea or Taiwan, many of whom began as small local
producers in their home
countries, using their know-how to go multinational. A handful of these
have grown to giant size,
where they often have as much power as all but the largest retailers,
constituting still another
layer of price-making and profit-taking. Consider, for example, the
following examples of giant
global contractors:
_ Nien Hsing Corporation, a Taiwanese
multinational that employs more than 20,000
workers in five Central
American factories, as well as thousands of workers in a Mexican
factory and two in Lesotho.
Founded in 1986, Nien Hsing is currently the world's largest
jeans maker, with an output
of 40 million pairs in 2000, making jeans for Wal-Mart, JCPenny,
K-Mart, the Gap, Sears and
Target. It is also the sixth-largest denim maker,
producing 60 million yards
per year.
_ Yupoong, Inc., a South Korean multinational,
which is the world’s second largest cap
manufacturer, exporting their
“flexfit” hats (motto: “worn by the world”) to some 60
countries. Yupoong (2003)
operates the BJ&B hat factory in DR, the scene of the second
recently successful labor
struggle that we will consider, as well as Dhakarea Ltd. in
Bangladesh.
_ Boolim, a South Korean multinational that was
founded in 1994 by Y.S. Lim, who had
headed up Macy’s in South
Korea for 14 years. Boolim makes athletic, casual wear, and
knitwear in some countries,
including China, Indonesia, Sri Lanka, Bangladesh, Saipan,
Thailand, Philippines,
Malaysia, Myanmar, Guatemala, Mexico, Dominican Republic,
Nicaragua, Honduras, El
Salvador and Vietnam Its clients include Nike, Polo Ralph
Lauren, Kenneth Cole, Calvin
Klein, and NBA Properties.
_ Pou Chen, a Taiwanese multinational, is 50%
owner of Tue Yen Industrial, a Hong
Kong-listed shoe manufacturer
that is the world’s largest, employing 150,000-170,000
workers worldwide. Yue Yen,
which makes shoes for Nike (about half of its total
production), as well as
adidas-Saloman, Reebok, New Balance, Asics Tiger, Converse,
Puma, Keds, Timberland, and
Rockport, controls 17% of the world market. Most of its
shoes are made in low-cost
factories throughout southern China; its Yue Yen II factory
complex in Dongguan, China,
employs more than 40,000 workers. The company is
Nike's biggest supplier,
providing 15% of Nike’s shoes, with one Indonesian factory
turning out a million shoes a
month for Nike. The company’s Huyen Binh Chanh megafactory
in Vietnam will be the
largest footwear factory on the planet, employing 65,000
workers (Bailey, 2003; Boje,
2000).
One study of changing
patterns of imports to Britain, France and Scandinavia concluded
that as recently as the late
1980s, southern Europe (mainly Portugal and Italy) was by far the
leading source of imports to
the three countries combined. Today the picture is far different:
…by 2000, this picture
changed so that Asian and ‘greater European’ producers were of
roughly equal significance,
ahead of their Southern European counterparts…. Importing
countries’ increasing
dependence on a combination of ‘low price’ and ‘medium
price/short lead time’
producing countries lends support to the idea that there are now
commonly acknowledged ‘global
production centres’… Factors to do with history,
language and proximity play a
role in determining the weight that specific supplying
countries and regions enjoy
in specific end-markets, even within this framework
(Palpacuer, Gibbon, and
Thomsen, 2003: 7-8).
Finally, it should be noted
that the growing importance of giant producers may
paradoxically be facilitating
worker organizing, since the large factories are vulnerable to
pressure from the large
retailers and manufacturers that use them. A number of successful
unionization drives have
occurred in such factories in recent years, including the Kukdong (now
Mexmode) apparel factory in
Mexico, the BJ&B hat factory in the Dominican Republic (owned
by Yupoon); and Hien Hsing
factories in Mexico (Chentex) and Lesotho. In these examples,
pressure on the factories and
their clients (which included Nike, Reebok, the Gap, and other
major U.S. companies) by
local independent labor unions, supported by U.S. and EU unions and
NGOs, have caused the parent
companies to allow the formation of independent unions.2
ESTIMATING THE
DETERMINANTS OF INDUSTRIAL UPGRADING
One approach would
empirically estimate the circumstances under which labor-intensive
industrialization – which played
a key role in the early development of the growing economies of
East Asia – contributes to
economic development. It builds on my earlier work with David
Smith, Brad Christerson, and
Herbert Wong (see, for example, Appelbaum, Smith, and
Christerson, 1993; Appelbaum,
Smith, and Wong, 1998).
Measuring Industrial
Upgrading
Appelbaum, Smith, and Wong
(1998) suggested developing an index of industrial
upgrading in individual
countries, estimating causal models using the index as the dependent
variable. We proposed
analyzing exports from all non-core developing countries to the United
States for 35 period
1965-2000, at the broad (two-digit) SITC level, in order to discern different
paths of industrial
transformation, as well as conducting a more nuanced analysis of highly
specific trade flows for two
commodities, apparel and consumer electronics.
‘Moving up the value chain’
is typically taken to mean that producers adopt more
capital-intensive processes
and techniques, while at the same time switching to the production of
more sophisticated and
expensive ‘high-end’ goods. Measuring this type of change would
capture an important
component of industrial upgrading. Fortuitously, international trade data
are available on a yearly
basis from the United Nations that provide standardized comparable
information across a range of
countries. Data are coded using the hierarchically ordered Standard
International Trade
Classification (SITC), which allows us to examine a level of detail ranging
from either very broad (one- or
two- digit categories) or extremely specific (seven- to nine-digit
categories). These data also
include information on the unit volume and dollar value of the
international commodity
flows.3 Smith
and Nemeth (1988) attempted to empirically sort
commodities into ‘bundles’ of
exports which flow together in the circuits of world trade. By
factor analyzing all
bilateral trade for every country with a population greater than one million
which provides complete
import and export data, they identified five major groups or “bundles”
2 For more
detailed discussion see Espenshade, 2003, forthcoming.
3 For a
general discussion of the data see Nemeth and Smith, 1985; Smith and White,
1992; for specific
examples see
Appelbaum, Smith, and Christerson 1993.
of two-digit commodities
(from food products and low wage/light manufacture to hi tech/heavy
manufacture; see Smith and
Nemeth, 1988: Tables 2 and 3).
The Smith/Nemeth strategy
could be replicated, but using international commodity trade
data for all countries in the
most recent year available (the Smith/Nemeth analysis relied on 1980
data). This would provide one
measure of the level of upgrading that characterizes a country’s
exports. It is important to
note that this operationalization of upgrading is partial. One of the key
insights of the commodity
chain approach is the importance of considering non-production
aspects such as design,
distribution and marketing of final products. Data classifying
manufacturing output, even if
it is by very specific product types, does not offer direct evidence
about the extent to which
there is a move to local design or brand name marketing.
Measuring Changing
Export Profiles
The analysis of commodity
trade from non-core nations to the US between 1965 and 2000
would yield a detailed image
of how each country’s export profile has changed over the last 35
years, revealing differences
in the path of industrial transformation between countries. This in
turn would provide a gauge of
changing commodity export mixes that reflect the ebbs and flows
of technologically-driven and
fashion-related product cycles. There are a number of possible
measures that tap into
dimensions of the production side of industrial upgrading, which can be
arrayed from the simplest to
the most complex:
a. Changing average unit
value of trade in all products.
b. Changing average unit
value amount for major product groups. A simple analytic
strategy would be to compare
the changing production levels of different commodities
(at either grouped, generic,
or very specific-levels of classification) by calculating
autocorrelation models of
changes in either volume or value over the 35 year period
(or any shorter periods). The
coefficient of the time variable estimates the annual
growth rate for that type of
export (cf. O’Hearn 1994).
c. Changing index of
dissimilarity, calculated from the largest fifteen two-digit SITC
categories in each country.
This measure gauges export diversification: countries
undergoing industrial
upgrading should have a higher degree of dissimilarity over
time. Both weighted and
unweighted measures could be constructed in a range
between 0 and 100.
d. Changing concentration
measures, also calculated from the largest fifteen two-digit
SITC codes for each country. This
measure gauges export specialization: countries
undergoing industrial
upgrading are likely to have a lower degree of concentration
over time. This measure also
ranges from 0 to 100.
e. Changing index of
industrial transformation, calculated using recalibrated Smith-
Nemeth “bundles.” This
measure is defined as the total value of export in hitech/
heavy manufacture to low
wage/light manufacture. For countries undergoing
industrial upgrading the
index should increase over time.
There should be major differences
between countries on these indices. In particular, the
established East Asian NIEs
are likely to stand out with a steady pattern of upgrading over almost
the entire period. Has the
upward arc slowed or stagnated in light of the East Asian slowdown of
1997-8? One would expect the
second-tier East and Southeast Asian NIEs to begin this process
later and to score more
modestly, with latecomers like China and Vietnam starting their
upgrading even later (but, perhaps,
to have particularly steep recent increases). It will be of great
interest to determine whether
the various latecomers simply follow a trajectory that replicates the
initial group of NIEs,
whether their upgrading is more rapid and skips stages. Finally, it should
be possible to determine
whether there is a distinctive “Asian model” that is distinguishable from
less-developed countries in
other regions, like Latin America or Africa.
Analysis of Upgrading in
Apparel and Consumer Electronics
A more fine-grained analysis
of upgrading is possible using seven- and nine-digit SITC
categories, focusing in
particular on apparel and consumer electronic assembly. Data could be
analyzed for the period
1965-2000 for all non-core countries, in order to facilitate a comparison
with the East Asian NIEs,
since both of these industries served as critically important motors of
export-led industrialization
in that region.4 In
apparel manufacture, Hong Kong and Taiwan
moved from sewing, to
sourcing offshore production for U.S. and European designers; they are
now moving up into designing
and marketing branded labels themselves. A similar process has
occurred in South Korea and
Singapore’s consumer electronics industries, where the movement
has been from component assembly
to engineering and design. It seems reasonable to assume
that these two industries are
playing the same role throughout East and Southeast Asia, and may
potentially play this role in
other countries.
Yet apparel and consumer
electronic assembly differ in significant ways as well: unlike
electronic assembly, apparel
production remains greatly resistant to technological upgrading
(Taplin, 1989, 1994; Bonacich
and Appelbaum, 2000; Waldinger, 1986; Dicken, 2003). The
principal technological
changes have been in automated fabric cutting, specialized operations
such as embroidering and
button-holing, and electronic point-of-sales (EPOS) inventory systems.
Organizationally, a few
factories have replaced the bundling system with unit production, thereby
reducing the time spent on
handling. Second, both industries are characterized by flexible
production systems, which are
themselves viewed by many theorists as an important key to
global competitiveness
(Storper and Walker, 1989; Scott, 1988; Malecki, 1991). Insofar as
flexibility calls for
simultaneously minimizing production costs while rapidly responding to
frequent demand, it has
strong appeal in industries with tight coordination between design,
production, and marketing
(Dicken, 2003). In both industries, the need for flexibility translates
into layers of subcontracting
in which manufacturer-designers contract to numerous factories,
resulting in an uncoupling of
the various components of manufacturing. This disintegrated form
of flexible accumulation greatly
increases the importance of personal networks, which is another
feature of economic
development we wish to investigate.
One approach would therefore
be to construct 35 year sequences of export profiles to the
United States for all
countries, with special attention given to those in East and Southeast Asia.
One would expect varying
degrees of upward movement across different countries, as well as
across specific commodities.
Previous research suggests that export-oriented manufacturing
4 The
simplicity of this equation and the wide availability of worldwide
cross-national on trade and GDP
make this
feasible. The ten East and Southeast Asian countries include the Four Tigers
(Hong Kong,
Singapore,
South Korea and Taiwan) and six latecomers (China, Indonesia, Malaysia, the
Philippines,
Thailand,
and Vietnam).
economies, particularly as
they move beyond the most labor intensive, low value-added ,
manufacturing, are likely to
move toward more specialized export niche production to bolster
international competitiveness.
This sort of commodity-specific pattern, likely to be obscured by
the aggregation of products
into broad export categories, should manifest itself in this finergrained
analysis. It is also likely
that the rate of upgrading in either of these specific sectors will
vary over time within each
country. A careful examination of such patterns would make it
possible to discern the
developmental sequences that each country has followed. These
sequenced paths of upgrading,
graphed across the years, could be used to make some interesting
comparisons between
countries. For instance, a retrospective look at patterns of apparel or
electronics upgrading in
South Korea or Taiwan from the 1970s could be compared to more
recent changes in China or
Vietnam.
The use of time-series data
permits thus makes it possible to quantitatively assess the
determinants of upgrading.
One strategy would involve pooled panel regression in order to
estimate models that control
for the initial values of the dependent variable while assessing the
impact of the independent
variables over time.5 Based
on the preceding discussion, the principal
independent variables for
this analysis might include:
1) Firm competitiveness, as
indexed by average measures of labor cost and productivity,
quality, reliability, etc
(some of these ratings may have to be subjectively based on the
perceptions of experts
familiar with the industries of different countries)
2) Time-to-market (this would
be one principal spatial component of the model –
estimating the relative
importance of spatial propinquity in commodity flows,
looking, for example, at
changing regional patterns of import-export relations)
3) The degree to which highly
networked, spatially concentrated industrial districts exist
that reduce transaction costs
and enable firms to engage in all aspects of production
(measuring this and
estimating effects would provide another spatial component of
the model)
4) The social organization of
a country’s firms into mutually supportive networks of
producers and suppliers, in
particular the presence of informal (e.g., Chinese) business
networks (operationalizing
this could be difficult; at worst, dummy or simple ordinal
variables could be developed
as subjective measures based on existing research)
5) The role of retailers
relative to manufacturers as the principal customer for exports
from each countries
(suggestions for estimating this would be welcome; I can find no
systematic source of data on
this, although information could possibly be gleaned –
laboriously – from the annual
reports of publicly-traded retailers and manufacturers)
6) The relative importance of
transnational producers in each country’s factory sector
(this would require a
country-by-country survey of knowledgeable experts)
7) Changing trade barriers,
including preferential trade agreements such as the North
American Free Trade Agreement
(NAFTA), the African Growth and Opportunity Act
(AGOA), the Caribbean Basin
Trade Partnership Act (CBTPA), and the Andean
5 The
dependent variable, Yt, is regressed on itself at the earlier point in time, Yt-1, as well as
on the
independent
variables, Xi(t-1).
Trade Preferences Act (ATPA).
These could be incorporated by means of dummy
variables, or perhaps ordinal
measures reflecting experts’ perception of their impact
on trade
8) State role, as indexed by
the proportion of government spending in business,
infrastructure, and
education; the extent of national industrial policy (most likely
these would be dummy
variables intended to capture the degree of marketization vs.
central planning, based on
the characterization of these economies in the literature)
9) The role of labor, in
particular the presence of an independent labor movement,
strikes and work stoppages,
etc.
Secondary variables of
substantive interest could include:
10) Flexibility/adaptability,
as indexed by the average manufacturing firm size; market
concentration; and percent of
GDP generated by SMEs
11) Human capital
development, as indexed by the percentage of the adult population
with secondary education or
more; the percentage of the population with tertiary
education; and the percentage
of the population with technical/engineering education
12) Foreign penetration:
ratio of FDI/DBI
13) Domestic economic
conditions, as indexed by the absolute size of the economy (an
index of the size of the
domestic market), domestic savings rates; the unemployment
rate; the ratio of public to
private investment ratio; the percent GDP that is generated
by exports
14) Entrepreneurship, as indexed
by the percent of the working population that is selfemployed;
and the new business start-up
rate
15) Demographic
characteristics, such as the age structure of the population which could
effect workforce
participation
Contrasts between Hong Kong,
Taiwan, Singapore, and South Korea, China and
Vietnam, and the other East
Asian countries are of obvious interest. These sorts of comparisons
(and separate
country-by-country analysis) can be carried out either by using dummy coding or
comparing parameter estimates
from separate equations of sub-samples.
A COMMODITY CHAINS-BASED
DECISION APPROACH TO MODELING
INDUSTRIAL UPGRADING
The global commodity chains
framework lends itself to a decision model approach to
understanding the
determinants of a firm’s locational decisions (i.e., to move production to – or
out of – a particular
location), the regional impacts of those locational decisions, and the impact
of any resulting regional
changes on subsequent decisions. To my knowledge this approach has
never been attempted (which
probably says something about its feasibility, if not its merit). In
this final section I
schematically outline such an approach, in hopes that someone will discern a
plausible modeling and
research strategy. The basic logic would be as follows:
1. Construct a hypothetical
global commodity chains for a product, modeled on the actual
structure of any existing
firm - for example, the U.S.’s largest apparel retailer, The Gap
(with 2003 sales of $14.5
billion). One would begin by mapping out all of the networks
on the commodity chain. In
the apparel commodity chain, for example, on Gereffi and
Memedovic (2003) have
identified five categories of networks: those having to do with
raw material inputs (both
natural and synthetic fibers), components (yarn, fabrics,
petrochemicals, synthetic
fibers), production (divided into different geographic regions),
export (branded companies,
overseas buying offices, trading companies), and marketing
(department stores, specialty
stores, mass merchandise chains, discount chains, and offprice
outlets).
2. Conceptualize each of
these networks as a set of decisional nodes. For example, if the
hypothetical firm is engaged
in making a part cotton / part synthetic blouse, it needs to
make decisions about where to
source the cotton; where to source the synthetics; where to
acquire bolts of fabric;
where to assemble the blouse; etc.
3. Each hypothetical
decision-maker then conducts an “environmental scan,” looking at
different locational options
for the activity in question. Should garment assembly be done
in a contract in Los Angeles?
Mexico? Bangladesh? China? Model the determinants of
this decision, based on what
we know about such decisions. The model would incorporate
such factors as labor costs
and productivity, labor militancy, the presence (or absence) of
labor unions, production
quality, transportation costs, time-to-market (including
reliability), preferential
trade treatment, the presence (or absence) of large producer
transnationals, the presence
of supportive social networks and viable industrial districts,
state policies, etc. In other
words, all of the predictors that are considered in the previous
approach to estimating the
impact of industrial upgrading.
4. Assume a set of locational
decisions, based on the foregoing considerations, for each
decisional node. Begin by
focusing on assembly, which is the most crucial node from an
economic development
perspective. Model the impact of each decision on the location
that is chosen. One set of
impacts would have to do with industrial upgrading – for
example, prospects for the
development of indigenous full-package production
capabilities, the development
of local backward and forward linkages in the commodity
chain, including developing
textile suppliers at one end, and original brand manufacturing
(OBM) capabilities on the
other. The other set of impacts would have to do with labor –
for example, the effect on
wages, inequality, and labor militancy.
5. Taking these impacts on
the chosen location into account, what is the likely feedback on
the decision-maker in that
particular node of the commodity chain? At what point does
the retailer or manufacturer
decide to move production elsewhere? What are the
determinants of such a
decision (for example, local labor shortages, that result in rising
wages)? What could be done
locally to discourage such a decision?
6. Calibrate the accuracy of
the model by comparing its results with actual results in
comparable production systems
over the past decade.
7. Repeat with other
commodity chains – for example, in a capital intensive industry (e.g.,
automobile manufacture), or a
labor-intensive industry that is more capital-intensive than
apparel manufacture (e.g.,
footwear). Aggregated across firms, what does this approach
tell us about the prospects
for industrial upgrading?
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