4.2.3 DECLINE AND REGENERATION OF INDUSTRIAL
VARIABLE (SD) (SD) (SD) (SD)
Site characteristics
Accessibility 6.12 6.40 7.12 ** 5.70
(5.13) (5.34) (6.16) (4.31)
Land use residential (ha) 1.41 1.65 ** 1.31 ** 1.89
(1.28) (1.42) (1.19) (1.54)
Land use open space (ha) 2.61 1.99 ** 2.10 1.98
(1.87) (1.69) (1.70) (1.67) Age (0/1) 1990s or after 0.16 0.05 * 0.04 0.05 1980s 0.27 0.12 ** 0.16 ** 0.09 1970s 0.19 0.21 0.19 0.23 1960s 0.19 0.24 0.24 0.24 Pre 1960s 0.18 0.38 ** 0.37 0.39
Type of industrial site (0/1)
Mixed use 0.24 0.39 0.41 0.37 Consumer services 0.05 0.04 0.03 0.05 Financial services 0.07 0.04 * 0.03 0.04 Logistics 0.12 0.09 0.09 0.10 Manufacturing 0.49 0.43 0.43 0.43 Miscellaneous 0.02 0.01 * 0.01 0.01 Number of jobs 924.31 1326.36 ** 1742.43 926.00 (2217.41) (2200.96) (2747.17) (1378.21) Number of companies 46.96 73.48 ** 91.29 ** 56.28 (73.13) (101.24) (119.59) (75.63)
Jobs per hectare 68.15 56.18 55.00 57.34
(245.95) (74.32) (74.38) (74.25) Environmental impact class (0/1) Class 5 0.08 0.16 ** 0.22 ** 0.11 Class 4 0.41 0.48 ** 0.49 0.47 Class <4 0.50 0.36 ** 0.29 ** 0.42 Sea port (0/1) 0.02 0.01 0.01 0.02
Mean property value 664.739 594.303 ** 590.636 597.823
(564.628) (393.576) (354.307) (428.193)
Table 4.1 – continued Regional characteristics Scarcity 0.93 0.93 0.93 ** 0.94 (0.04) (0.04) (0.04) (0.03) Urbanisation rate (0/1) Metropolitan agglomeration 0.21 0.23 0.19 * 0.26 City region 0.17 0.18 0.17 0.18
Outside city region 0.62 0.59 0.64 * 0.56
Province (0/1) Groningen 0.03 0.04 0.07 ** 0.02 Friesland 0.07 0.06 0.07 0.05 Drenthe 0.03 0.02 0.04 ** 0.01 Overijssel 0.09 0.09 0.11 * 0.07 Flevoland 0.02 0.03 0.04 ** 0.01 Gelderland 0.14 0.13 0.10 ** 0.16 Utrecht 0.04 0.06 * 0.11 ** 0.02 Noord Holland 0.07 0.13 ** 0.10 ** 0.17 Zuid Holland 0.02 0.15 ** 0.14 0.16 Zeeland 0.04 0.04 0.07 ** 0.01 Noord Brabant 0.13 0.17 * 0.08 ** 0.26 Limburg 0.10 0.07 0.07 0.07 Performance characteristics Growth of companies (%) 44.1 32.3 * 17.7 34.9 (262.6) (2.39) (0. 9) (215.5) Growth of jobs (%) 57.3 49.8 * 25.5 40.0 (293.8) (629.0) (124.3) (248.0)
Change in property value (%) 124.0 130.4 123.0 118.1
(136.5) (213.7) (126.1) (81.8)
4 COUNTERING DECLINE OF INDUSTRIAL SITES
The group of targeted (round 1 and round 2) sites consists of 905 sites (of which 446 are targeted in round 1) while there are 817 non-targeted sites in the total dataset of 1,722 industrial sites. From the mean values for site characteristics it can be noticed that on average, the problem sites are older. Almost 40% of the targeted sites is from the pre-1960s period, whereas the largest group of non-targeted sites is from the 1980s. Interestingly, 5% of targeted and declined sites was developed in the 1990s and is thus relatively young to already experience problems related to decline. Industrial sites dominated by manufacturing make up the largest group for both targeted and non-targeted sites. Mixed use is the second largest for both categories, although the group of mixed use sites represents 39% of the targeted and only 25% of non-targeted sites. Targeted sites are bigger, as measured by the total number of jobs and number of firms. The number of jobs per hectare however is higher for non-targeted sites. Average property values are 11% higher on non-targeted sites. The regional characteristics do not show large differences. The industrial sites in the dataset are quite evenly spread among the regions and areas of urbanisation distinguished. The mean values for the provincial dummies show the distribution of industrial sites over the Netherlands. Large, traditional industrialised provinces such as Noord Brabant and Gelderland house many industrial sites. The large cities Rotterdam and The Hague (Zuid Holland) and Amsterdam (Noord Holland) also have high shares of industrial sites.
The values of the performance characteristics in Table 4.1 indicate that targeted industrial sites show slower growth than non-targeted sites. Interestingly, property value change is higher for industrial sites that are targeted. Two further analyses of these differences in which the influence of the other variables is controlled for, are presented in the next section. In the first analysis it is expected that the results will show that a negative growth of the performance indicators will increase the chance of being targeted for regeneration. In the second, multinomial logistic regression analysis it is expected that all three of these variables will show a similar pattern: industrial sites that are targeted in the second round show negative results compared to the group of non-targeted sites. Industrial sites that are targeted for regeneration in the first round are expected to show more negative results compared to the industrial sites that are targeted in the second round.
4.4 RESULTS: DIFFERENCES BETWEEN TARGETED AND
NON-TARGETED SITES
T
he results of the logistic regression analysis are presented in Table 4.2.49 Non-targeted sites are the reference category, so the coefficientsshould be interpreted as the probability that an industrial site belongs to the group of targeted sites. Accessibility does not show a significant result. Residential land use does not influence the chance of being targeted, other than open space, which negatively influences the probability of being targeted. The categories of age show the expected results: older industrial sites are more likely to be targeted than younger ones, with industrial sites that were developed before the 1960s showing the largest coefficient as compared to all other age cohorts and the reference category 1990s. An expected result is the non-significant coefficients for types of industrial sites. Compared to the reference category of mixed-use industrial sites, no type of industrial site has a significantly larger probability of being targeted. One possible interpretation is that all industrial sites are evenly prone to targeting. The same can be concluded for size, as the coefficients for number of jobs and number of firms do not show significant results. The coefficients for environmental impact classes show that industrial sites that house potentially polluting or hazardous companies are more probable to be targeted. The variable for property values also shows the expected negative coefficient. Under regional characteristics, urbanisation rate does not show significant results. Scarcity unexpectedly shows a positive sign, indicating that the often-suggested relation between readily available land and decline does not exist for our dataset. An alternative explanation is that high levels of scarcity might lead to a larger policy effort to regenerate industrial sites in the existing built up space and thus targeting occurs more.
The results for the performance characteristics do not show any significant relations between these variables and the probability of being targeted for regeneration. The coefficients for all variables are insignificant, indicating that, having controlled for the other variables in the model, there appears to be no significant relation between economic performance and targeting. The second analysis is performed to find differences within the group of targeted industrial sites and compare these two groups with non-targeted sites. Hypothetically, and according to the theory laid out by Bartik (1994) among others, there are differences to be found for the performance indicators within the group of targeted sites as industrial sites targeted in round 1 are assessed as in need of regeneration before the sites targeted in round 2.
49 With all VIF scores below 5, multicollinearity does not appear to influence the results of the multivariate analyses performed.
4 COUNTERING DECLINE OF INDUSTRIAL SITES
Table 4.2
Results of the logistic regression. Probabilities for site characteristics, regional characteristics and performance characteristics on targeted sites. Reference category: non-targeted sites.
Note: results for provincial dummies are not reported for brevity.
** significant at the 1% level * significant at the 5% level.