Information technology clusters and regional growth in America, 1970–1980

Abstract

We use county business patterns data for the 1970s to study the role of information technology (IT) clusters in fostering regional growth in America. We interpret the IT industry as made up of four components: computer hardware, computer software, semiconductors, and key computer customers. We pose two fundamental questions. First, did counties where the IT industry clustered in 1974 grow at a faster rate between 1974 and 1980? Second, did each individual component of the IT industry grow faster between 1974 and 1980 in counties where the other components clustered in 1974? We find that counties in which the IT industry clustered early on grew faster than those in which it did not, after controlling for a variety of county-specific characteristics and after addressing the potentially endogenous nature of the IT industry’s location. We also find that each individual component of the IT industry grew faster in regions in which the other components clustered. We interpret these findings as consistent with the tenets of the spillover theory of entrepreneurship.

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Notes

  1. 1.

    In order to check the robustness of these results, we estimated two additional versions of this model. First, we estimated several variants of Eq. (2A). In other words, we estimated variants of a model in which the log of the number of establishments belonging to a specific cluster component (say, computer hardware) located in a specific county in 1980 is the dependent variable. We found that the presence and concentration of the other components of the cluster in 1974 had a positive and significant impact in the context of this model. Second, we estimated a version of model (2B) but only for those counties in which the specific cluster component (say, computer hardware) was not located in 1974. We also found that the presence and concentration of the other components of the cluster in 1974 had a positive and significant impact in this context. These results are not reported in this article for space reasons but are available from the authors upon request.

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Correspondence to Daniel Garcia-Swartz.

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The opinions expressed in this article are exclusively the authors' and do not reflect or represent the views of Belly, Charles River Associates, Warwick University, or any other institution with which they may be affiliated.

Appendices

Appendix 1: An economic model of regional growth

In a classic study, Henderson et al. (1995) present a simple economic model of industrial growth, which is the foundation for the empirical model we estimate in this study. This is a model for an individual industry in location j at time t. For such an industry, the equilibrium number of workers is such that the local wage rate equals the value marginal product (VMP). That is,

$$W_{j,\,t} = A_{j,\,t} ( \cdot )f'(N_{j,\,t} ; \ldots )P_{j,\,t} ( \cdot )$$
(3)

Industry output is A j,t f(N j,t ;…), and N j,t is the number of workers for the industry in question in county j at time t. The term A j,t (·) represents the state of the industry’s technology in county j at time t, W j,t is the nominal wage rate, and P j,t is the price of the industry’s output. We use establishments as a proxy for workers for the reasons presented in the core of our study.

Static and dynamic externalities are introduced in the model as arguments of A j,t (·), so that the state of the industry’s technology in a county at a given point in time is a function of, for example, the degree of concentration of the industry at the starting point (MAR externalities) and the level of industrial diversity of the county in question (Jacobs externalities). Substituting into (3) and inverting generates an empirical model of the following form:

$$N_{j,\,t} = N\left( {W_{j,\,t} ,\,MC_{j,\,t} ,\,N_{j,\,0} ,\,\lambda_{j,\,0} ,D_{j,\,0} } \right)$$
(4)

This model, which can also be interpreted as a model for the growth rate of the number of establishments (i.e., a model for ln (N j,t /N j,0)), expresses such a growth rate as a function of the industry’s nominal wage in county j, the industry’s marginal cost in that county, the concentration of the industry in that county at the starting point and the level of industrial diversity in the county in question at the starting point. We estimate a modified version of such a model for the county as a whole and for the individual components of the IT industry. Note that when we estimate the model for counties (as opposed to industry–county combinations), we are treating counties as bundles or aggregates of industries. We use regional binary variables to proxy for differences in marginal costs across regions. The definition of the key variables included in the empirical models is presented in Table 11.

Table 11 Variable definition for econometric models (all variables defined at the county level)

Appendix 2: Entropy as a control for a county’s manufacturing diversity

The entropy concept as a measure of diversity was introduced by Shannon in the context of information theory [see Shannon and Weaver (1949)]. In our treatment of entropy, we follow Theil (1972). If n is the number of groups in the population under study, the entropy measure varies between zero and log(n), where zero is equivalent to no diversity and log(n) is equivalent to maximum diversity. When measuring the industrial diversity of a county, the higher the entropy the higher the manufacturing diversity of the county, and thus the higher the potential for Jacobs-style externalities.

As a measure of industrial diversity in a county, entropy is defined as follows:

$$H \equiv \sum\limits_{i = 1}^{n} {p_{i} \log \left( {\frac{1}{{p_{i} }}} \right) = H_{0} + \sum\limits_{g = 1}^{G} {P_{g} H_{g} } }$$
(5)

In Eq. (5), total entropy, (H), can be decomposed into between entropy (H 0) and the average within entropy. Further, p i is the share of the 4-digit SIC code i in the county as a whole, and P g is the share of the 2-digit SIC code g in the county as a whole. In entropy studies, these shares are measured in terms of either employment or number of establishments—in our study we use the latter. The formula also shows that the entropy of a county can be decomposed into between entropy and within entropy.

The decomposition is as follows: (a) between entropy focuses on diversity measured at the level of 2-digit SIC codes, whereas (b) within entropy focuses on diversity measured at the level of the 4-digit SIC codes within each one of the 2-digit SIC codes (Theil 1972, pp. 20–22):

$$\begin{aligned} H & \equiv \sum\limits_{i = 1}^{n} {p_{i} \log \left( {\frac{1}{{p_{i} }}} \right) = H_{0} + \sum\limits_{g = 1}^{G} {P_{g} H_{g} } } \\ H_{0} & = \sum\limits_{g = 1}^{G} {P_{g} \log \left( {\frac{1}{{P_{g} }}} \right)} \quad \left( {{\text{between}}\,\,{\text{entropy}}} \right) \\ H_{g} & = \sum\limits_{{i \in S_{g} }} {\left( {\frac{{p_{i} }}{{P_{g} }}} \right)\log \left( {\frac{1}{{p_{i} /P_{g} }}} \right)} \quad \left( {{\text{average}}\,\,{\text{within}}\,\,{\text{entropy}}} \right) \\ \end{aligned}$$

Table 12 presents the results from estimating our model with a focus on the growth of the individual components of the IT industry, after having replaced the HHI measure with the entropy measures. All of our key results remain unchanged, and both entropy measures have positive and significant coefficients, as expected (the results are similar if we restrict the estimation to urban counties only).

Table 12 Clustering of the individual components of the IT industry in 1974 and growth of the other components between 1974 and 1980, with entropy measures as controls

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Garcia-Vicente, F., Garcia-Swartz, D. & Campbell-Kelly, M. Information technology clusters and regional growth in America, 1970–1980. Small Bus Econ 48, 1021–1046 (2017). https://doi.org/10.1007/s11187-016-9808-8

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Keywords

  • Information technology clusters
  • Regional growth
  • Urban growth
  • Knowledge spillover theory of entrepreneurship

JEL classifications

  • J24
  • L26
  • M13
  • O3
  • R11