Rich Appelbaum Position Statement

POSITION STATEMENT

What are your research interests in the areas of inequality and equity, and what spatial dimensions do you currently or potentially see in them?

My research focuses on the opposing processes of the global dispersion of production processes along global commodity chains, and the localization of production in industrial districts.

In buyer-driven commodity chains, labor costs are a prime determinant of industrial location. In labor-intensive, low-wage manufacturing such as apparel and consumer electronics, retailers and manufacturers place orders with independent contractors, in whose factories the goods are made. Businesses seek low-cost production sites, although the friction of space remains a consideration: longer shipping times, lower productivity, the absence of full-package production, and other logistical considerations may partly offset the labor cost differential. Retailers and manufacturers are able to utilize production sites on an as-needed basis: since they do not actually own the factories that make their goods, they are free to sign contracts with particular factories when it is economically desirable, and refrain from doing so when it is not. One consequence of this arrangement is a global competition for the cheapest wages and lowest labor and environmental standards possible � what critics have termed the "global race to the bottom." Economic inequality is the result: not only between low- and high-wage countries, but within the latter as well. For example, these dynamics have contributed to the growth of an enormous apparel manufacturing sector in Los Angeles, where sub-minimum wage apparel workers must compete with their counterparts across the border in Mexico, driving their wages downward.

Globalization is not the only dynamic that obtains in these industries, however. Although the inherent flexibility of buyer-driven commodity chains provides manufacturers with a competitive edge, it also entails costs: organizationally (and frequently geographically) dispersed commodity chains create problems of coordination and control. One way in which such problems are overcome is through the creation of geographically interdependent networks of small firms, factories, and specialized local labor markets. Geographically dense industrial concentrations minimize transaction costs by providing 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. The presence of a strong support infrastructure � for example, business associations, supplier clubs, and private or state-supported 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.

Thus, the interesting questions are: How can segments of the commodity chain be "trapped in space" in a fashion which benefits localities? What are the particular spatial synergies of well-conceived industrial districts that would lessen, rather than increase, wage inequalities? How can the globalizing tendencies of buyer-driven commodity chains, with their competitive drive for cheap labor, be offset by policies which promote local concentration? Which activities on the commodity chain are most likely to contribute value to the local economy, both directly and through forward and backward linkages, thereby contributing to a process of industrial upgrading (the reverse of the "race to the bottom")?

What kinds of spatial models, techniques, software, etc. do you use or have considered using in your research? Which of these work well for you? Where do you see problems and/or shortcomings?

Thus far my usage of GIS technology has been primarily heuristic � to visually capture global processes of trade flows (as a proxy for global commodity chains), as well as to map local patterns of industrial concentration. Thus, in one set of papers, I mapped trade flows between countries, scaling arrows to the relative size of the flow. This research was used to estimate a gravity model of trade (in apparel) as a function of the characteristics of trading countries: such factors as relative wages, market size, and distance between the countries. In another set of papers, I constructed a GIS of every garment factory in Los Angeles that was registered with the state of California (approximately 3,500 licenses factories at the time of the study), noting its location on a street map of Los Angeles county, as well as the ethnicity of the factory owner (this was accomplished by a combination of a computerized ethnic name look-up algorithm, and manual coding). The intention was to see whether or not factories of different ethnicities clustered in different ways (for example, Korean-owned factories, which are larger than average and employ half of Los Angeles� 120,000 garment workers, tend to be clustered in or near the downtown garment district; Latino-owned factories tend to be dispersed, often along lines of Latino residence).

The problems of using GIS to map global commodity chains are daunting. What are required ideally are data on internal transactions of firms; yet data are collected as trade data for countries. When a clothing retailer (like The Gap) contracts with factories in China to make shirts for sale to American consumers, the shirts are recorded as exports from China and imports to the US � even though they are in fact internal transactions to The Gap (this is important because only a small fraction of the export value recorded as a US trade deficit actually remains in China as direct labor payments). What would be useful would be to somehow think of the commodity chain as "touching down" geographically at different points, with different effects for the locale where it did so. For example, the decision to locate a design office or marketing agency in a particular locale has a very different impact on equality/inequality than the decision to source a garment factory in a particular locale. Modeling the commodity chain as a set of locational decisions, tracing the impact of such a decision on particular locales, then looking at the feedback effect on subsequent location decisions would go a long way towards simultaneously modeling the pressures for global production, localization, and the corresponding impact on inequality. But it is not clear how one would gather data to operationalize such a model.

Can you point out any "best practice examples" of spatially-oriented research in your field? Do you have any suggestions for learning resources CSISS might provide? Workshops we might offer?

How about a workshop on decision modeling from the standpoint of a firm trying to determine where to locate its design offices, marketing operation, and contract factories? The workshop would model dynamic feedback loops between firm locational decisions and characteristics of the local, considering the economic effects on both locale and firm of the decisions that are made. Identifying data that might be useful for this exercise would be especially helpful.

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Rich Appelbaum
Rich Appelbaum Institute for Social, Behavioral & Economic Research University of California, Santa Barara
POSITION STATEMENT What are your research interests in the areas of inequality and equity, and what spatial dimensions do you currently or potentially see in them? My research focuses on the opposing processes of the global dispersion of production processes along global commodity chains, and the localization of production in industrial districts. In buyer-driven commodity chains, labor costs are a prime determinant of industrial location. In labor-intensive, low-wage manufacturing such as apparel and consumer electronics, retailers and manufacturers place orders with independent contractors, in whose factories the goods are made. Businesses seek low-cost production sites, although the friction of space remains a consideration: longer shipping times, lower productivity, the absence of full-package production, and other logistical considerations may partly offset the labor cost differential. Retailers and manufacturers are able to utilize production sites on an as-needed basis: since they do not actually own the factories that make their goods, they are free to sign contracts with particular factories when it is economically desirable, and refrain from doing so when it is not. One consequence of this arrangement is a global competition for the cheapest wages and lowest labor and environmental standards possible � what critics have termed the "global race to the bottom." Economic inequality is the result: not only between low- and high-wage countries, but within the latter as well. For example, these dynamics have contributed to the growth of an enormous apparel manufacturing sector in Los Angeles, where sub-minimum wage apparel workers must compete with their counterparts across the border in Mexico, driving their wages downward. Globalization is not the only dynamic that obtains in these industries, however. Although the inherent flexibility of buyer-driven commodity chains provides manufacturers with a competitive edge, it also entails costs: organizationally (and frequently geographically) dispersed commodity chains create problems of coordination and control. One way in which such problems are overcome is through the creation of geographically interdependent networks of small firms, factories, and specialized local labor markets. Geographically dense industrial concentrations minimize transaction costs by providing 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. The presence of a strong support infrastructure � for example, business associations, supplier clubs, and private or state-supported 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. Thus, the interesting questions are: How can segments of the commodity chain be "trapped in space" in a fashion which benefits localities? What are the particular spatial synergies of well-conceived industrial districts that would lessen, rather than increase, wage inequalities? How can the globalizing tendencies of buyer-driven commodity chains, with their competitive drive for cheap labor, be offset by policies which promote local concentration? Which activities on the commodity chain are most likely to contribute value to the local economy, both directly and through forward and backward linkages, thereby contributing to a process of industrial upgrading (the reverse of the "race to the bottom")? What kinds of spatial models, techniques, software, etc. do you use or have considered using in your research? Which of these work well for you? Where do you see problems and/or shortcomings? Thus far my usage of GIS technology has been primarily heuristic � to visually capture global processes of trade flows (as a proxy for global commodity chains), as well as to map local patterns of industrial concentration. Thus, in one set of papers, I mapped trade flows between countries, scaling arrows to the relative size of the flow. This research was used to estimate a gravity model of trade (in apparel) as a function of the characteristics of trading countries: such factors as relative wages, market size, and distance between the countries. In another set of papers, I constructed a GIS of every garment factory in Los Angeles that was registered with the state of California (approximately 3,500 licenses factories at the time of the study), noting its location on a street map of Los Angeles county, as well as the ethnicity of the factory owner (this was accomplished by a combination of a computerized ethnic name look-up algorithm, and manual coding). The intention was to see whether or not factories of different ethnicities clustered in different ways (for example, Korean-owned factories, which are larger than average and employ half of Los Angeles� 120,000 garment workers, tend to be clustered in or near the downtown garment district; Latino-owned factories tend to be dispersed, often along lines of Latino residence). The problems of using GIS to map global commodity chains are daunting. What are required ideally are data on internal transactions of firms; yet data are collected as trade data for countries. When a clothing retailer (like The Gap) contracts with factories in China to make shirts for sale to American consumers, the shirts are recorded as exports from China and imports to the US � even though they are in fact internal transactions to The Gap (this is important because only a small fraction of the export value recorded as a US trade deficit actually remains in China as direct labor payments). What would be useful would be to somehow think of the commodity chain as "touching down" geographically at different points, with different effects for the locale where it did so. For example, the decision to locate a design office or marketing agency in a particular locale has a very different impact on equality/inequality than the decision to source a garment factory in a particular locale. Modeling the commodity chain as a set of locational decisions, tracing the impact of such a decision on particular locales, then looking at the feedback effect on subsequent location decisions would go a long way towards simultaneously modeling the pressures for global production, localization, and the corresponding impact on inequality. But it is not clear how one would gather data to operationalize such a model. Can you point out any "best practice examples" of spatially-oriented research in your field? Do you have any suggestions for learning resources CSISS might provide? Workshops we might offer? How about a workshop on decision modeling from the standpoint of a firm trying to determine where to locate its design offices, marketing operation, and contract factories? The workshop would model dynamic feedback loops between firm locational decisions and characteristics of the local, considering the economic effects on both locale and firm of the decisions that are made. Identifying data that might be useful for this exercise would be especially helpful. [TOP]