Contemporary structure of software employment in metropolitan Tel Aviv

The diminishing impact of distance on the spatial organization of employment

Nurit Alfasi, Shaul Krakover

Department of Geography and Environmental DevelopmentBen-Gurion University of the Negev

Software firms by size, 2006

425 out of 552 software firms in Israel are located at the Tel Aviv metropolitan area

Employees Firm numbers Percentages

1-10 175 41.2

11-26 98 23.1

26-50 57 13.4

51-100 29 6.8

101-250 19 4.5

251-500 10 2.4

501-1000 5 1.2

1001-2000 4 0.9

> 2000 3 0.7

Unknown 25 5.9 Source: Dan & Bradstreet report, 2006

Objective

Examination of the distance factor as a spatial organization power Utilizing five nodes:

Old CBD

New CBD

The location of the three largest firms employing more than 2000 workers each.

Old CBD

New CBD

Ness IT2450 employees

Matrix2150 employees

Team2077 employees

Methods employed to examine relationships with distance:

1. Major rings and sectors using LQ.

2. Aggregation by 5 km.-wide rings.

3. Single node – log-linear regression

4. Piecemeal linear regression

5. Piecemeal log-linear regression

6. Multi-focal regression

Software firms, 2006Location quotients of employees in software firms

Location in metropolitan area

Employees in software firms

LQ Employees, excluding the three largest firms

LQ, excluding largest firms

Core (Tel Aviv Yaffo) 6,290 1.4 3,840 1.1

Inner ring 10,584 1.4 8,434 1.4

Northern Section 4,556 3.3 2,406 2.2

Eastern Section 5,963 1.9 5,963 2.4

Southern Section 65 0.0 65 0.0

Middle ring 11,071 1.1 8,994 1.2

Northern Section 1,811 0.8 1,811 1.1

Eastern Section 8,349 3.4 6,272 3.3

Southern Section 911 0.2 911 0.2

Outer ring 1,593 0.2 1,593 0.3

Northern Section 1,222 0.4 1,222 0.5

Eastern Section 366 0.2 366 0.3

Southern Section 5 0.0 5 0.0

The software industry is concentrated at the north-eastern parts of the metropolitan area, mainly in the core area and in the inner and middle rings.

Software firms, 2006

Location quotient < 1

1 < Location quotient < 3

3 < Location quotient

Distance Bands Old CBD New CBD Team Matrix Ness-IT

0-5 4,013 17,208 17,215 5,416 16,850

5-10 15,245 6,319 8,653 17,548 6,769

10-15 6,489 3,785 2,122 2,833 3,951

15-20 2,685 1,095 125 3,049 1,485

20-25 92 1,085 1,380 252 413

25-30 976 38 35 405 67

30-35 30 5 5 32 -

35-40 5 - - - -

Beta -0.702 -0.837 -0.827 -0.660 -0.886

Adjusted R2 0.41 0.64 0.62 0.32 0.73

Prob. 0.052* 0.019 0.022 0.106* 0.019

Aggregation by distance bands

* Not significant

Aggregation by distance bandsNumber of employees by distance bands to the nodes

Distance decay curves of total employment originated from New CBD and Ness-IT produce statistically significant linear relationship with distance.

0

5,000

10,000

15,000

20,000

0-5 5-10 10-15 15-20 20-25 25-30 30-35 35-40

Distance bands

Em

plo

ye

es

Old CBD

New CBD

Team

Matrix

Ness-IT

N.S. – Not significant *** Significant at the 0.01 level

Distance to Intercept B Significance Adjusted R2

Old CBD 2.687 0.065 N.S. 0.000

New CBD 1.786 -0.023 N.S. 0.000

Team 3.177 -0.206 *** 0.055

Matrix 3.269 -0.221 *** 0.022

Ness-IT 3.418 -0.339 *** 0.049

Single Node analysisLog employees with distance

Three segments of firms: 1) larger than 750, 2) larger than 100 to 750, and 3) 1 to 100. Each segment fitted independently

Piecemeal linear regressionDistance to

Intercept Slope- b significance Adjusted R2 N Size of firms

Old CBD 1983 -36.081 N.S. 0.000 8 Large343 -5.288 N.S. 0.019 33 Medium

19 0.049 N.S. 0.000 357 Small

New CBD 1860 -32.573 N.S. 0.000 8 Large341 -6.429 * 0.054 33 Medium

20 -0.011 N.S. 0.000 357 Small

Team 1867 -48.427 N.S. 0.004 8 Large332 -6.807 * 0.081 33 Medium

22 -0.258 N.S. 0.002 357 Small

Matrix 2023 -46.587 N.S. 0.011 8 Large374 -8.811 ** 0.136 33 Medium

22 -0.254 N.S. 0.003 357 Small

Ness-IT 2041 -79.999 N.S. 0.198 8 Large340 -7.845 ** 0.111 33 Medium

22 -0.270 N.S. 0.003 357 SmallOnly for medium size firms there is a slight decreasing slope indicating a decrease in the number of employees with distance.

N.S. – Not significant * Significant at the 0.10 level ** Significant at the 0.05 level.

Piecemeal analysisLog employees with distance

The old and new CBD does not show signs of organizational power. All three large firms exercise a small but significant amount of organizational power on other large and medium firms.

N.S. – Not significant * Significant at the 0.10 level ** Significant at the 0.05 level.

Distance to Intercept Slope- b Significance Adjusted R2 N

Large and medium firms – more than 100 employeesOld CBD 5.344 0.048 ** 0.099 41

New CBD 6.224 -0.041 * 0.062 41

Team 6.169 -0.044 ** 0.089 41

Matrix 6.324 -0.045 ** 0.085 41

Ness-IT 6.200 -0.048 ** 0.100 41

Small firms – 1-100 employeesOld CBD 2.532 -0.006 N.S. 0.000 357

New CBD 2.560 -0.011 N.S. 0.016 357

Team 2.648 -0.021 ** 0.010 357

Matrix 2.687 -0.021 ** 0.011 357

Ness-IT 2.655 -0.023 ** 0.011 357

Multi-focal analysisLog employees with distance

Distance to Intercept B Sig. B Sig. B Sig. Adjusted R2

Two nodesTeam + Matrix

3.576 -0.199 *** -0.202 .004 0.072

Team + Ness-IT

3.466 -0.147 *** -0.222 .007 0.070

Matrix + Ness-IT

3.652 -0.149 ** -0.299 .00008 0.057

Three NodesTeam + Matrix + Ness-IT

3.727 -0.155 *** -0.166 .021 -0.172 .040 0.080

N.S. – Not significant ** Significant at the 0.05 level *** Significant at the 0.01 level

1. Proximity to specific nodes appears to have diminutive organizational power on employment in software.In particular, the Old and New CBDs do not display organizational power on the location of the software firms when analyzed by exact location.

However, significant though weak regression results were produced for the location of the firms with respect to their distance to the three largest firms. Team, located in the suburb of Petah Tiqwa has the most powerful organizational effect as reflected by the highest R squares.

2. Medium and large software firms (100+ employees) show some organization patterns around the three largest firms.Whereas, analysis did not procure any distance profiles for small firms. Thus, while large and medium firms are somewhat sensitive to the impact of distance, small firms are less organized.

Conclusions

Conclusions

4. Clearly there is a need to employ other techniques to reveal the structure of agglomeration in metropolitan areas.

3. Do we face new locational decision realities within urban spaces? Israel’s software firms tend to concentrate in the TA metropolitan area in the north-eastern sectors of the inner ring. However, the impact of distance within this area seem to be marginal and there is no central node that organizes the spatial distribution.This corresponds with Frenkel’s, Shefer’s and Roper’s (2003) findings about the tendency of innovative hi-tech businesses in Israel to locate in metropolitan areas. With diminishing distance relationships does the pattern correlates with Porter’s (1990, 1998, 2000) notion of clusters?