are interpreted improperly, assumed to be more authoritative than they are, or applied to policy without confi rmation via other methods.
In summary, cluster-based planning typically consists of a quantitative analysis of the way individual industries interact through trading, and a qualitative assessment of what industries seem to be emerging. The units of analysis are Standard Industrial Code (SIC) or North American Industrial Classification System (NAICS) groupings and, usually, metropolitan regions. Both the quantitative and qualitative approaches are subject to technical challenges. Most pertinent to this chapter, they fail to account for the growing inappropriateness of SIC/NAICS categories as meaningful aggregates of businesses, they treat regions as undifferentiated spatial units, where all constituent communities have the same workforce and infrastruc- ture capacity, and they do not address the growing need to coordinate among different cluster foci.
Table 7.1 Subregional variation in economic outcomes, RTRP
Unemployment Median Percent of population Percent of population rate (percent) household >25 years old with >25 with college County Sub-region 2002 income* 2000 high school diploma, 2000 degree 2000 Chatham Non-core metro 4.8 $42 851 78.5 27.8
Durham Core metro 5.7 $43 337 83.2 40.2
Edgecombe Non-core metro 12.6 $30 983 65.7 8.5 Franklin Non-core metro 6.4 $38 968 74.0 13.3
Granville Non-metro 6.6 $39 965 72.7 13.0
Halifax Non-metro 12.0 $26 459 65.3 11.1
Hamett Non-metro 8.7 $35 105 75.5 12.8
Johnston Non-core metro 4.9 $40 872 76.0 15.9
Lee Non-metro 7.9 $38 900 76.3 17.2
Moore Non-metro 6.8 $41 240 82.7 26.9
Nash Non-core metro 8.7 $37 147 75.3 17.2
Northampton Non-metro 10.5 $26 652 62.4 10.8
Orange Core metro 3.2 $42 372 87.8 51.6
Person Non-core metro 9.2 $37 159 75.0 10.3
Sampson Non-metro 7.8 $31 793 68.8 11.0
Vance Non-metro 12.4 $31 301 67.7 10.7
Wake Core metro 5.3 $54 988 89.4 43.9
Warren Non-metro 10.0 $28 351 67.1 11.5
Wilson Non-metro 8.3 $33 116 69.2 15.0
Core metro counties 5.1 $49 899 87.7 43.9
Non-core metro counties 7.2 $37 997 74.4 15.9
Non-metro counties 8.7 $133 288 72.2 15.0
Note: * Sub-region’s median household income is arithmetic average of component counties. Other entries are weighted.
Source: N.C. LINC electronic database – http://data.obsm.state.nc.us/pls/linc/dyn_linc_main.show.
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core, though obviously, the intensity of commuting decays somewhat with distance; see Table 7.2.
We see, for example, that 40, 51 and 30 per cent, respectively, of Chatham, Franklin, and Person county workers (adjacent counties) commute to the core, and 35, 23 and 11 per cent, respectively, from Granville, Harnett and Warren counties (non-adjacent).
The main point of Table 7.1 is at the bottom: employment rates, income and educational attainment are progressively worse as we move away from the core of the region. Not shown in the table is that the differences have increased since the last census. Underlying those differences are signifi cant variations in infrastructure and services by county. The widening dispari- ties between more- and less-endowed places, played out within relatively large economic regions/commuting sheds, and between different regions, is a common feature of the new economy (see, for example, Luger, 1998).
In short, knowledge businesses require locations with well-developed knowledge resources, and relatively footloose knowledge workers prefer to reside where there is a high level of amenities.
The tradition in regional economics is to consider the metropolitan area as the unit of analysis, and researchers are quite familiar with the appropri- ate data at that level from the bureaus of the census and labor statistics.
Conceptually it makes sense to regard the commuting shed as an integrated whole. And, if we did not construe the region that broadly in impact analyses of policy interventions, we would end up with almost no multiplier effect, since there would be massive leakage to ‘the rest of the world’. Finally, in practical terms, it is simply cumbersome to work with individual county data, especially in large regions consisting of many counties.
This tradition of regional analysis undermines the validity of cluster- based planning at that level, given the realities illustrated in Table 7.1. It is unlikely for any target cluster to be appropriate for all locations within a region. For example, a cluster that has advanced skill requirements would be hard to develop in the non-core counties of the Research Triangle region.
The Clusters of Innovation study (Porter, 2002) did not acknowledge that.
The authors recommended particular emphasis on the following four clusters: environmental sciences, biotechnology and information technology, telecommunications and medicine, and biotechnology and agribusiness.
That may have resonated with business and government leaders in the core counties, but those outside the core recognized only agribusiness as an appropriate target and, therefore, began to lose interest in the regional planning effort.
A second characteristic of the new economy that poses a challenge for cluster-based planning is the blurring of boundaries between and within clusters. To modify a phrase from a well-known article by Aaron Wildavsky
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Table 7.2 Cross-commuting in 2000
County of work
County of residence
Chatham Durham Franklin Granville Harnett Johnston Lee Chatham 44.69% 11.11% 0.09% 0.18% 0.11% 029% 5.73%
11 018 2739 21 45 26 71 1413
Durham 031% 74.94% 0.19% 125% 0.00% 0.36% 0.16%
349 84 262 211 1410 409 178
Franklin 0.21% 4.27% 34.93% 2.77% 0.13% 1.27% 0.09%
47 951 7772 616 28 282 21
Granville 0.06% 22.49% 1.16% 53.46% 0.01% 0.40% 029%
12 4609 238 10 957 2 82 60
Harnett 0.61% 1.35% 0.06% 0.04% 39.20% 3.75% 11.16%
248 547 24 18 15 916 1521 4530
Johnston 0.21% 2.80% 0.16% 0.18% 2.38% 45.97% 0.32%
124 1645 92 107 1399 26 971 187 Lee 6.04% 1.68% 0.07% 0.14% 137% 021% 71.56%
1383 384 17 31 313 47 16 382
Moore 1.24% 0.18% 0.03% 0.05% 0.16% 0.02% 4.50%
398 57 10 17 51 8 1441
Orange 1.30% 27.06% 0.14% 0.32% 0.01% 0.17% 0.15%
792 16 470 83 196 9 105 91
Person 0.26% 23.83% 0.05% 3.40% 0.00% 0.10% 0.00%
43 3939 8 562 17
Vance 0.00% 3.03% 2.10% 13.10% 0.06% 0.12% 0.06%
542 377 2347 10 22 10
Wake 026% 12.80% 0.72% 0.42% 027% 1.20% 0.34%
873 43 351 2430 1422 916 4050 1167
Warren 0.10% 2.90% 2.74% 4.13% 0.00% 0.21% 0.00%
7 207 196 295 15
% commuting out 55.31% 25.6% 65.07% 46.54% 60.80% 54.03% 28.44%
# commuting out 13 639 28 171 14 476 9537 24 683 31 704 6511 Notes: Cells with no values entered had no workers listed: percentages are rounded.
Actual number of workers is shown beneath percentage.
Source: Data taken from the US Census Bureau website at http://www.census.gov/
population/www/cen2000/commuting.html.
Moore Orange Person Vance Wake Warren Total county workforce Working outside RTRP Total county workers from RTRP 0.61% 17.06% 0.03% 0.00% 11.12% 0.00% 8.99% 91.01%
150 4206 8 2743 24 657 2217 22 440 0.00% 824% 024% 0.12% 12.39% 0.01% 1.79% 98.21%
9262 270 130 13 929 8 112 433 2015 110 418%
0.00% 0.24% 0.13% 4.37% 46.51% 0.27% 4.80% 95.20%
54 29 973 10 347 60 22 248 1068 21 180 0.03% 1.21% 1.08% 5.01% 12.15% 0.08% 2.57% 97.43%
7 249 221 1026 2489 16 20 494 526 19 968 125% 0.21% 0.00% 0.04% 21.78% 0.00% 20.55% 79.45%
508 84 18 8841 40 599 8344 32 255 0.01% 0.42% 0.01% 0.05% 40.27% 0.02% 7.19% 92.81%
8 246 8 30 23 628 9 58 675 4221 54 454 3.62% 1.03% 0.00% 0.03% 9.15% 0.00% 5.12% 94.88%
828 236 7 2094 22 893 1171 21 722 76.10% 0.14% 0.00% 0.01% 0.96% 0.00% 16.60% 83.40%
24 365 44 4 308 32 018 5315 26 703
0.00% 57.60% 0.23% 0.02% 6.92% 0.01% 6.06% 93.94%
35 053 142 12 4212 8 60 860 3687 57 173 0.00% 4.06% 58.13% 0.44% 3.71% 0.00% 6.02% 93.98%
671 9609 73 614 16 531 995 15 536 0.00% 0.33% 0.16% 70.13% 6.56% 1.84% 2.51% 97.49%
60 29 12 561 1175 329 17 911 449 17 462 0.04% 1.05% 0.05% 0.14% 80.46% 0.01% 225% 97.75%
145 3552 166 478 272 432 18 338 602 7602 331 000 0.00% 0.70% 0.06% 24.58% 3.85% 44.87% 15.58% 84.12%
50 4 1757 275 3208 7149 1135 6014 23.90% 42.40% 41.87% 29.87% 19.54% 55.13%
7653 25 807 6922 5350 66 170 3941
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(1976), ‘if clusters are anything, maybe they are nothing’. It is bad enough that the technique of cluster analysis does not produce ‘defi nitive’ industry groupings, but in addition, the raw material used in the analyses (SIC/NAICS industries) does not tell the full story of what the constituent business does.
This point is illustrated in Table 7.3. The input–output linkages that generated this loading of industries are from national data. The procedure we used created the grouping shown in the table, which we then had to interpret. Somewhat arbitrarily, we named the set of linked industries the
‘chemicals and plastics cluster’, recognizing that many non-plastics and non-chemical businesses were included. The table also indicates that the industries included vary in terms of their technology intensiveness.
Economic developers do not seek to recruit clusters to a region, but rather, individual businesses. They like cluster analysis because it can be used to generate a list of industry codes that help them decide which businesses to concentrate on. But there is no guarantee that the particular business they land, even if it belongs to one of the industries in Table 7.3, will have any synergies with other business in the region. Conversely there are likely businesses not included in the table that would be highly linked in the region.
What this means is that industry codes are not the only – and perhaps not the best – business attribute to use when ascertaining the likelihood of business synergies within a region. Some analysts (for example, Feser, 2003) group businesses by the labor they use, understanding that those businesses value the same kinds of education and training, and can draw on a larger labor pool locally, including both unemployed and already employed workers who can move laterally. That is consistent with the volatile nature of the labor market in the new economy referred to by Atkinson above.
In the work conducted for the Research Triangle’s cluster task force, summarized in the next section, a third basis was used to group businesses together: their common technologies or applications areas. That is consistent with the role ascribed to science and technology (S&T) in the rapid transformation of new economy businesses, also in the Atkinson quotation above.