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The analysis in this paper uses the index of geographic concentration proposed by Ellison and Glaeser (1997) due to some reasons. It has some useful properties, as has been mentioned by the authors, such as being comparable across industries in which the size distribution of firms differ and comparable across countries irrespective of divergences in the level of geographic aggregation. The nature of the data also leads us to explore EG index as providing establishment level information on the employment level and location. However, for the same reason, the data prevents us using continuous indices since it lacks fine locational information on the establishments, as it is available at NUTS-2 level.
Agglomeration index of plants presented by Ellison and Glaeser (1997) compares the observed geographic distribution of plants with a random distribution. The plants are defined to be geographically randomly distributed by considering their expected distribution in the lack of agglomerative forces. In their model, plants cluster either to benefit from natural advantages or spillover externalities from other plants. They start with a simple location model and first define a raw geographic concentration index:
(1)
where sim is the share of industry i's employment in area m, and xm is the the share of
aggregate manufacturing employment in area m. "As Gi measures concentration relative to
total manufacturing employment; as long as an industry mimics the pattern of aggregate employment it is not considered as being concentrated" (Alecke et al., 2006, p. 21).
However, Ellison and Glaeser (1997) note that, geographic concentration is not neutral to industrial concentration. Ceteris paribus, "industrially concentrated sectors will tend to exhibit a higher Giindex because there are fewer plants and consequently employment has
to be concentrated in fewer places" (Bertinelli and Decrop, 2005, p. 569). To address this issue, they take into account industrial concentration by employing Herfindahl index of the industry's plant size distribution in building EG index.
(2)
where j = 1, … , N number of plants in industry i, and zij is the employment share of jth
plant in industry i. Hi is a function of the number and size distribution of plants in industry i. Higher indices imply industries with a small number of plants and with an uneven size distribution. When plants make location decisions in compliance with the location choice model built by Ellison and Glaeser (1997), they suggest that expected value of Gi is related
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3.2. How much are industries agglomerated?
By making use of AISS dataset based on local units, EG index is calculated for Turkish manufacturing industries for both years. In our sample, there are 228 and 229 4-digit industries in 2003 and 2008 respectively in terms of NACE Rev.1.1. Industries with less than three plants are dropped due to the fact that EG index tends to be biased upwards, as mentioned before. In the simple dartboard model of EG in which plants choose their location in a random manner, in the absence of natural advantages or spillovers, expected value of the raw geographic concentration should be proportional to the industrial concentration. To state algebraically in terms of equation (3), Ȗ equals 0 and . So, as a first step it should be tested whether observed geographic concentration G, is statistically significantly different from For the year 2003, the mean values for the above expressions across 228 manufacturing industries are calculated to be 0.17 and 0.09 respectively and the difference between these measures is significant at 5 % significance level. Regarding 2008, these values are calculated to be 0.16 and 0.08 respectively across 229 manufacturing industries and the difference is again highly significant6.
6. Ellison and Glaeser (1997, p.97) provide a lengthy formula for the variance of G under the null hypothesis of
no natural advantages and spillovers, as follows:
Under this formula, the standard deviation of the sample mean under the null is calculated as 0.004 and 0.003, as regards 2003 and 2008 respectively.
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