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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. –more–>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 in determining whether the assumption is unmerited.

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.

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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 the educational system in the region; several educational 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.

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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.

Implications of relaxing the assumption of asymmetric information in Franco and Mitchell (2005 working paper).

Generally, an assumption of informational asymmetry often has two factors that seem to matter: the asymmetry itself, and the timing of the availability of information. In detail:

a) The case of no asymmetry- namely that it is common knowledge whether employee in question has learned something in his first period of employment or not – lies outside Franco and Mitchell’s exploration. It is clear that if their asymmetry assumption is being relaxed, then the implications of their theory change. One study has examined the nature of public information and optimal contracting by research institutions (Pakes and Nizan). In their inquiry they model publicly available information with ex post realization of learning outcome. The model implied that there are implementable first-best contracts (unlike the asymmetric information of Franco and Mitchell) that do not distort contracting equilibrium and profits. In that case, the CNCs (covenants not to compete) do not matter since they do not provide an additional enforcement mechanism necessary to move closer to the first best contract. However, Pakes and Nitzan use a mixed reward scheme composed of both monetary compensation and stock. It is seems that the stock compensation is necessary to produce a reward for the employee, which is high enough in order to prevent the employee from going to a competitor or forming a competing firm himself. This is mainly driven by uncertainty (according to Pakes and Nitzan) of invention’s future realization.

b) Relating to the timing of the available information, we will focus on two cases: 1) The ex ante information case and 2) the ex post information case.

1) In the case of ex ante information available to everyone, everything is simplified. One of the mathematical simplifications of the Franco and Mitchell model is that the incumbent firm’s profit is zero if the employee does not learn. Hence, hiring the employee which will not increase the profits will not be beneficial for any company and the optimal contract with this individual is no contract.

2) In the case of ex post information, both employer and the employee know that the employee learned in the first period. Thus, there would be some re-negotiation such that the employer offers a wage just high enough to prevent the employee from either going to the competitor or forming his own firm. In the instance that the re-negotiation is not possible the employer will offer a wage high enough to cover his (employer’s) expectation. Here, the stock scheme may be necessary (as in Pakes and Nitzan) to get to the contract that keeps the employee from leaving.

There are three possible extensions of this framework:

i) One may introduce uncertainty about the future realization of employee learning. This may cause a high enough distortion, such that the firm will not be able to offer the employee the optimal contract. In that case, CNCs may be necessary to force the employee to stay and we may very well see the results of Franco and Mitchell materialize again.

ii) The second extension is one in which the employer is not able to compensate the employee, not due to the uncertainty about future realization, but rather due to belief-differentials. Under that construction, the disagreements are likely to be a driving force behind employee’s choice to leave and future realization of these beliefs will provide different efficiency implications.

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iii) The third possible direction is to introduce degrees of learning or differing degrees of employee ability. Here, one might think of a standard signaling story in which ex ante only the employee’s ability is known with some precision. Hence, the learning will be a function of ability and will not be known to the employer (he would only be able to estimate it based on the signal). In that case, high enough variance in the signals may also drive back the results to resemble those of Franco and Mitchell. Here, yet again, there may be a need for CNCs to move closer to the unachievable, otherwise first-best contract.

Instances of Production Knowledge Information Symmetry

The hypothesis proposed by Franco and Mitchell implies that if information asymmetry (regarding the knowledge that an employee has of the production technology developed by the firm) were reduced in an industry, that industry should show less agglomeration. This result is based on the crucial assumption made by the authors that the employer does not know whether his employee has learned the technology developed at the firm. Thus, if the assumption of asymmetry is relaxed and CNCs are not allowed, the optimal contract becomes. If the combined employer’s and employee’s profits given by the employee having learned and left the firm is less than the employer’s profit given by the employee having learned and stayed with the firm , and if the employer’s profit given by the employee having learned and staying with the firm is greater than the profits given by the opposite case , the employer has the incentive to offer an incentive for the employee not to leave. As long as holds, the employer will be able to offer compensation that eliminates the employee’s incentive to leave the firm. Thus, greater symmetry should reduce the number of spinouts, and hence, reduce industry agglomeration.

However, observation of agglomeration effects in industries with arguably greater symmetry than the Silicon Valley industry does not always imply this. The shoe manufacturing industry is one that does not require highly skilled labor. One can deduce that workers higher in the management hierarchy or with better training can easily learn any innovation within the firm, and the employer will have knowledge of this. Thus, the greater symmetry of information in the shoe industry should result in less agglomeration, and one that is diminishing over time. However, Olav Sorenson and Pino G. Audia show in their paper entitled “The Social Structure of Entrepreneurial Activity: Geographic Concentration of Footwear Production in the United States” that the agglomeration in the shoe manufacturing industry is significant and persistent through time.

A similar case is that of the watch manufacturing industry. Historically, the best watches have been those made by hand. This process involved highly trained laborers who were involved in most of the manufacturing process. Again, it is reasonable to conclude that an experienced watchmaker in a firm will learn any innovations, and the employer will know it. In this case greater symmetry should result in less agglomeration; however, in Europe, for example, the watch making industry has historically been concentrated in Switzerland.

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One last case where we observe greater symmetry of information is in the automobile industry, particularly during its early period. During its initial stages, employees could easily learn innovations within the firm. One clear example of this was the set of knowledge spillovers relating to the assembly line. Henry Ford is usually credited with the invention of the assembly line; however, it was actually Ransom E. Olds, the founder of Olds Motor Vehicles Company, who invented it. Ford, who was actually a partner of Olds at some point and not an employee, copied the method and perfected it. Thus, we can conclude that at least in the early automobile industry, there was greater symmetry of information between employer and employees. However, as it is well known, the U. S. automobile heavily agglomerated in the Detroit area, and this agglomeration grew and persisted through time. Again, this fact contradicts the result that greater symmetry should result in less agglomeration.

Thus, we have that after relaxing the assumption of asymmetry made by Franco and Mitchell, less agglomeration should be the result in any industry, all other things being equal. However, three cases of industries with greater symmetry and highly agglomerated seem to contradict this result.

Data Source for Labor Mobility

Future investigation to test for the different attributes of agglomeration will require a rich data set, particularly with regards to labor mobility. Rebitzer (2006) uses the U. S. Census Bureau Current Population Survey, but it is merely a monthly survey and lacks the advantages of a longitudinal data set. Another data set, The Longitudinal Employer – Household Dynamics (LEHD) Program at the Census Bureau, has a collection of infrastructure files that provide detailed information of workers, employers, and their interaction in the US economy. Since 2003, the Census Bureau has published the Quarterly Workforce Indicators (QWI). This is a new collection of data series that offers details on the local dynamics of labor markets across industries (http://lehd. did. census. gov/led/datatools/qwiapp. html).

For each state, there are data on total employment, net job flows, job creation, new hires, separations and turnover sorted by industry, year, sex, and age group. For example, in California, the net job flows in software publishers industry was -1,090 in the fourth quarter of 2004. If more detailed information is needed, the variables can be narrowed down over particular subsets of the data. For example, in the California example, we can narrow the set down to only male workers aged from 25 to 34. The net job flow was then -269. Quite significantly for the purposes of studying agglomeration, the data have information for 20 industries under which there are a number of selectable sub-industries. The LEHD serves as an excellent data source from which to construct general panel data on labor mobility.

References:

Burdett, Ken, and Melvyn Coles, (Sep 2003): “Equilibrium Wage-Tenure Contracts,” Econometrica,Vol. 71, No. 5. pp. 1377-1404

Franco, April F. and Matthew F. Mitchell “Covenants not to compete, labor mobility, and industry dynamics,” working paper, University of Iowa.

Rebitzer, James (2006): “Job hopping in Silicon Valley: The microfoundations of a high tech industrial district. ” Review of Economics and Statistics, Vol. 88, No. 3, Pages 472-481.

Richard B. Freeman (1981) “The Effect of Unionism on Fringe Benefits”

Industrial and Labor Relations Review, Vol. 34, No. 4, pp. 489-509

James E. Long, Albert N. Link (1983) “The Impact of Market Structure on Wages, Fringe Benefits, and Turnover”, Industrial and Labor Relations Review, Vol. 36, No. 2, pp. 239-250

Olivia S. Mitchell (1983) “Fringe Benefits and the Cost of Changing Jobs”,

Industrial and Labor Relations Review, Vol. 37, No. 1, pp. 70-78

Dickens, F, Katz, A (1987) “Inter-industry Wage Differences and Industry Characteristics”, NBER WP: 2014

Pakes, Ariel and Saul Nitzan (1984), “Optimal Contracts for Research Personnel, Research Employment and Establishment of ‘Rival’ Enterprises,” Journal of Labor Economics, 1 (4), 345-65.

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