A frequent example used in the study of industry agglomeration is the hi-tech electronics agglomeration in Silicon Valley, California. The general problem to investigate relates to what advantages either the agglomeration in itself or Silicon Valley confers to businesses that result in agglomeration. The next-largest agglomeration in the same industries, Massachusetts’ Route 128, eventually fell far behind Silicon Valley. Franco and Mitchell (2005), citing the labor mobility-restricting legal tool of non-compete contracts (also known as covenants not to compete, or CNCs), support the earlier Gilson (1998) and Hyde (2003) argument that a legal prohibition on the enforcement CNCs in California was responsible for the differences between Silicon Valley and Route 128. Because of the innovation-dependent nature of the industry, employees working at one company could easily migrate to other companies or create their own new companies (“spin-outs” as opposed to “spin-offs”) as a result of the knowledge spillovers caused by their labor mobility. Non-compete contracts serve the function of allowing employers and employees to agree in advance to legally restrict such mobility.
Using an optimal contracting model, Franco and Mitchell compare their model’s predictions when CNCs are allowed and when they are prohibited. They conclude some important things: the model explains the higher turnover in places where CNCs are prohibited; enforcement of CNCs in a region encourages greater firm numbers in early industry stages, especially where innovation is a key factor, thus explaining the early advantage of Route 128 which was eventually overcome by Silicon Valley; and we should expect to see in the data that concentrated industries seek CNC-enforcing areas, while competitive industries should be less likely to seek out such protection.
Their model, however, makes two key assumptions: first, that wages can’t be “backloaded”- in other words, employees can’t agree to be paid less than they would in their alternative option (to form a spin-out) for one period, and then get paid more to compensate in a later period; and second, that the level of the employee’s knowledge of the production process is known only by the employee. These may be generous assumptions that, when changed, could possibly alter Franco and Mitchell’s results drastically. One way to test their “backloading” assumption is by exploring the ways in which companies (especially in CNC-prohibiting regions) create economic incentives to depress labor mobility, and how often they do so. If wage backloading plays a significant role in those companies’ hiring practices, Franco and Mitchell’s model may be leaving out an essential explanatory variable. Their information asymmetry assumption also requires testing. On one hand, its importance can be explored by relaxing it in their model and testing its implications; on the other, instances in which employers actually have information about what their employees know about the production process should also be helpful.
Substitutes for Non-Compete Contracts
If non-compete contracts are illegal, one alternative means of restricting labor mobility is by “backloading” wages. In order for a firm to keep its employees from moving to a competing firm, the firm may decide to backload the wages, often in the form of pensions, options, health insurance, and other benefits that could only be captured if the employee stays with the firm over a certain time period. Franco and Mitchell (2005) assume backloading as impossible in their model. Rebitzer (2006) overlooks it.
A non-compete contract can initially be helpful in the early stages of an industry. As described by Rebitzer (2006), if employees were to “hop” around to other firms, the likelihood that knowledge acquired in one firm would be employed in another firm increases. These knowledge spillovers can hurt innovation by reducing the rewards to investing in human capital. On the other hand, abolishing non-compete contracts can be more helpful to local firms in the long run. If non-compete contracts are unenforceable, their elimination can lead to more turnover and more competitive entrepreneurs, assisting local firms in competing with out of state industries. Comparing Silicon Valley to Massachusetts’ Route 128, Franco and Mitchell (2005) found that Massachusetts’ Route 128 was more productive initially, but was eventually overtaken by Silicon Valley.
If non-compete contracts were unenforceable in Silicon Valley, it would be interesting to see if firms in Silicon Valley tended to backload wages more than Massachusetts’ Route 128, since the non-compete contracts in Massachusetts’ Route 128 were enforceable. If this is the case, the results by Franco and Mitchell may not have come about because of different non-compete regulations.
Burdett and Coles (2003) model a similar scenario of non-compete contracts, but they instead use wage-tenure contracts that give employees an incentive to remain in the firm and not move to a competing firm. In their story, each firm offers a wage-tenure contract that implies any employee’s wage smoothly increases with tenure. We can also compare the tenure policies of firm in Silicon Valley and Massachusetts’ Route 128, and see if the results of the model by Burdett and Coles (2003) are consistent with the data.
Wage Structuring
Wage-based policies can also serve as an alternative to non-compete contracts for reducing labor mobility-based knowledge spillovers. During a training period, for example, a worker gets paid less than his outside option (moving to other firms). However, depending on the importance of the information and the enforceability of the non-compete contracts, promising satisfactorily high wages to workers after their training period will stop them from changing employers. This kind of policy seems to successfully prevent spillovers across companies in the industry, but it does not affect industry clustering or profitability. It may even be the case that these policies can be more efficient than enforcing a legal framework for non-compete contracts. Fosturi and Ronde (2002), in their study “High-tech clusters, technology spillovers, and trade secret laws” theoretically demonstrate that even though information spillovers caused by labor mobility are prevented, industry clusters and profitability remain undisturbed.
One avenue of investigation to pursue would be to find a data set including wages, labor mobility, and the density of cluster in a region to test the assumptions given above. There is an industry cluster of biotech companies in San Diego, which is also supported by educational system in the region; several education institutions provide education from the undergraduate to doctorate level in biotechnology, providing a local labor pool. There are a large number of high quality workers who are educated and trained by the companies, though labor mobility among firms is high in this industry.
Fringe Benefits
Different legal frameworks for labor mobility-reducing contracts in different states prompts a search for other strategies that might be undertaken by firms to prevent labor-related information leakages to competing firms. Facing a lack of legal framework (like non-compete contracts) that can prevent workers from quitting and working in competitor companies overnight, firms need to utilize economic incentives to reduce information spillovers. Though the spillovers have a second level benefit through clustering, they also may have first level costs manifested by forgone opportunities for new innovations, for example. For the workers, one of the costs of labor mobility is an income loss due to foregone fringe benefits (Mitchell, 1983).
Controlling for other variables like unionization (Freeman, 1981), market concentration, regulations setting minimum prices and restricting entry, and profit regulations (Long and Link, 1983), we find that a substantial amount of variation in individual wage and fringe benefits is accounted for by industry differences. Dickens and Katz (1987) argue that high wage industries have lower quit rates, high labor productivity, more educated workers, longer work weeks, a higher ratio of non-wage (fringe benefits) to wage compensation, high unionization rates, bigger initial sizes and bigger average size of firms, higher concentration ratios, and more profits. However, what the studies above do not address is the effect of specific industry characteristics on the endogenous choice of fringe benefit costs by firms. We would expect the industries with fast innovative processes – without a legal framework that restricts labor mobility -would reflect higher levels of fringe benefit-related costs, and likely have better-defined promotion structures that discourage labor turnover.