Inter-regional Mobility of Entrepreneurial SMEsJames Foreman-Peck and Tom Nicholls

Welsh Institute for Research in Economics and Development , Cardiff University

URESG Cardiff 26 September 2013

Identifying entrepreneurial SMEs

• Entrepreneurial firms moving between early innovative and mature product life cycle phases are likely to be spatially mobile

• Expanding, well-managed enterprises will be prone to move from high cost (congestion, wages and rent) locations where they innovated to

• low cost (smaller, less agglomerated) places suitable for standardised production

• The spatial pattern: from core to periphery or v.v.?• Spatial consequences: ‘trickle out’ or boosting regional

disparities?

Data set

• Business Structure Database (BSD) which is intended to identify all but the very smallest enterprises.

• Consequently included in each year chosen for study here are about two million cases (Barnes and Martin 2002).

• Our selection from this data set consists of all enterprises employing fewer than 250 persons.

• These enterprises are the smallest combination of legal units that is an organisation producing goods or services and that benefits from ‘a certain degree of autonomy in decision-making’ (ONS 2006, p. 7).

Relative Labour Productivity Index (RLP)

• Turnover as the output measure (in the absence of firm level price deflators).

• To allow comparisons across different industrial sectors, estimate each SME’s productivity relative to the industry mean.

• This removes industry-specific factors -differences in the capital stock and bought in materials.

• An index number greater than one indicates higher than the industry average productivity. For enterprise i in industry j, and where LP is labour productivity and ALP is average industry labour productivity;

• RLPij = LPij / ALPj

• Industries are defined at the 3-digit level of the UK SIC 1992 classification

• We also construct an industry-relative enterprise-level employment index (rsizeE) as a measure of size.

Facts

• Inter-regional moves (affected 1.5% of SMEs 2004-6) are a small proportion of SME exits (23% )

• London the largest exporter- 2.9% net c 1.3% (The industrial core region exports employment to the periphery:Hypothesis )

• Wales and Scotland the smallest• All regions except London and North West were net

importers• ‘Neighbour’ effects; SMEs appear most likely to move to

regions that are adjacent. From Wales SMEs tend to move to the West Midlands and North West

Table 1 - SME relocation and exit frequencies by UK region 2004-6

Region 2004

No change or exit (2006)

Relocating (2006)

% relocating

(2006)

Net imports* (2006)

Net imports

as a % of 2004 total

Exit (2006)

% exiting (2006)

Total (2004)

Wales 67,453 643 0.75% 372 0.43% 17,565 20.5% 85,661 N. East 37,777 431 0.86% 59 0.12% 11,909 23.8% 50,117 York. & Hum. 107,658 1,366 0.97% 77 0.05% 31,966 22.7% 140,990 N. West 144,796 1,787 0.93% -130 -0.07% 44,542 23.3% 191,125 W. Mid. 122,789 1,830 1.14% 116 0.07% 35,720 22.3% 160,339 E. Mid. 101,855 1,891 1.41% 242 0.18% 30,375 22.6% 134,121 S. West 142,975 2,106 1.14% 857 0.46% 40,147 21.7% 185,228 Scot. 101,316 575 0.44% 289 0.22% 29,474 22.4% 131,365 E. Eng. 149,744 3,431 1.74% 617 0.31% 43,647 22.2% 196,822 S. East 243,532 5,503 1.69% 1,378 0.42% 75,874 23.4% 324,909 London 209,719 8,596 2.90% -3,877 -1.31% 78,296 26.4% 296,611

Total 1,429,614 28,159 1.48% 439,51

5 23.2% 1,897,288

*(Moving into region – moving out of region)

Source: ONS, authors’ calculations

Employment Expansion

• Entrepreneurial SME may be expected to be growing rapidly and so rather larger than this average; hence table limited to enterprises employing more than 10 in 2004.

• The growth of employment among this group between 2004-7 averaged just over 9 percent while that among enterprises that relocated by 2006 was more than double, an average of over 21 percent.

• Fastest growing movers originated in London and in the North West. Can afford to expand employment by relocating to lower wages and rents regions.

• Restricting the sample to those employing more than 20 the percentages are 8 for the region as a whole and 14.6 for movers.

Table 3 Growth of Employment 2004-7 (Enterprises employing >10, 2004)

Total Region Relocators Out 2006 Mean SE Mean SE North East 0.1203 0.0145 0.0181 0.1940 North West 0.0912 0.0192 1.9368 1.8811 Yorks 0.0913 0.0129 0.0673 0.0914 East Midl 0.1545 0.0756 0.0789 0.1038 West Midl 0.0500 0.0107 0.1111 0.1135 East Engl 0.0582 0.0081 -0.1046 0.0531 London 0.1211 0.0162 0.2785 0.1176 S East 0.0895 0.0195 -0.0649 0.0585 S West 0.0614 0.0093 0.0970 0.0991 Wales 0.0536 0.0114 0.0429 0.1529 Scotland 0.1162 0.0247 -0.0240 0.0978 N Ireland 0.0620 0.0115 -0.7590 0.0933 Total 0.0907 0.0075 0.2168 0.1377

Increased Relative Labour Productivity

• Performance of movers also on average is stronger (24 percent 2004-7) than among the region as a whole (16 percent).

• The geographical pattern is rather different from that for employment growth, however.

• Relocators from London and the North West are no longer the fastest, though they both grow more strongly than stayers in those regions. Moving to lower wage areas reduces profit-maximising labour productivity (hypothesis?)

• For enterprises employing more than 20, movers average 30 percent RLP growth and the regional average is 18 percent.

Table 4 Relative Labour Productivity Growth 2004-7 (Enterprises with more 10 employees 2004)

Total Region Relocators Out 2006 Mean SE Mean SE North East 0.1463 0.0103 0.4134 0.1456 North West 0.1654 0.0061 0.2111 0.0975 Yorks 0.1619 0.0067 0.2151 0.0998 East Midl 0.1460 0.0069 0.1406 0.1286 West Midl 0.1656 0.0066 0.3258 0.1026 East Engl 0.1461 0.0065 0.3151 0.0736 London 0.1880 0.0074 0.2260 0.0534 S East 0.1512 0.0056 0.2938 0.0630 S West 0.1474 0.0068 0.0804 0.1044 Wales 0.1697 0.0097 0.0774 0.2363 Scotland 0.1735 0.0075 0.3536 0.1713 N Ireland 0.1656 0.0103 0.2108 0.6066 Total 0.1615 0.0021 0.2453 0.0276

Relocation estimating equation Pr is probability, Rt+1 = 1 if the firm has relocated in year t+1 (2006), Φ(.) is the distribution

(probit) function;

• Pr(Rt+1=1) = F(g0 Location t-1+ g1Aget + g2ln(RLPt-1) + g21 ln(RLPt-1)*ln(rsizeEt-1) + g22(Locationt-

1*ln(RLPt-1))+ g3(rsizeEt-1) +g31ln(rsizeE

t-1)2 + g4(Legal form)t-1 + g5(Number of plants)t-1 + g6Takeoverst-1 +61(Takeovert*ln(RLPt-1)) + g7ln(Takeovert*Locationt-1) + g71(Takeovert*ln(RLPt-1)*Locationt-1) … (1)

• Hypothesis 1 is the probability of relocation falls with enterprise age, g1 < 0.

• Hypothesis 2a, mobility chances increase with productivity, is that g2 > 0, supplemented by a specification that allows the productivity effect to vary with the location and the size of the firm.

• Hypothesis 3, g3 > 0, is that larger firms are more likely to be spatially mobile, but if g31<0 the effect diminishes and may even be reversed as size (employment) increases.

• Hypothesis 4 (g4) is that more personal forms of ownership make for lower mobility. • Hypothesis 5 is that partial mobility is more likely than full mobility so when the number of plants in

the enterprise exceeds 1 relocation chances increase, g5> 0.

• Hypothesis 6 is that a takeover raises the chances of the firm subsequently moving, g6 > 0, also allowing that these chances vary with the productivity of the target (as for instance predicted by Q theory (Jovanocic and Rousseau 2002).

• Hypothesis 7, takeovers affect relocation differentially according to the target location, is tested by whether g7 ≠ 0. Takeover effects also vary with target productivity.

Table 5- Probit regression estimates of relocation

Variable Dep var relocation

Full sample

Takeover 0.1191***

Ln(RLP) 0.0291***

Ln(RLP)^2 0.0108***

Age 2 to 4 0.0101

Age 5 to 9 -0.0424***

Age 10 to 19 -0.1345***

Age 20+ years -0.2684***

Ln(local unit) 0.1007***

Ln(remp) 0.0191***

Ln(remp)^2 -0.0060***

Company 0.2045***

Partnership -0.0788***

Wales -0.2555***

Scot. -0.5263***

N. East

-0.2693***

N. West -0.2823***

York. & Hum. -0.2332***

E. Mid. -0.0887***

W. Mid. -0.1891***

E. Eng. -0.0390***

S. West -0.1734***

Wales*ln(RLP) 0.0424***

Scot.*ln(RLP) -0.0141

N. East*ln(RLP) 0.0213

N. West*ln(RLP) 0.0240**

York. & Hum.*ln(RLP) 0.0176

E. Mid.*ln(RLP) 0.0182*

W. Mid.*ln(RLP) 0.0159*

E. Eng.*ln(RLP) 0.0238***

S. West*ln(RLP) 0.0096

Wales*takeover 0.3401**

Scot.*takeover 0.5586***

N. East*takeover 0.4861***

N. West*takeover 0.2223***

York. & Hum.*takeover 0.1793**

E. Mid.*takeover 0.0454

W. Mid.*takeover 0.2568***

E. Eng.*takeover 0.0930

S. West*takeover 0.2182**

Takeover*ln(RLP) 0.1191

Industry Y

N 1,897,288

Pseudo R2 0.05

Log-likelihood -139,117

• Consistent with hypothesis 1 the coefficients indicate that older SMEs are less likely to relocate. A 2-4 year old enterprise is twice as likely to move as one that is over twenty years old. Likely to be due to life cycle effects or that newer firms have fewer local links, ensuring that relocation is less costly

• Hypothesis 2a is confirmed by the positive and significant coefficient on productivity (RLP) but to establish the full productivity impact on relocation the interactions terms g21 and g22 must be included. probability of relocation is around 0.4 to 1.8 percent over the three year period considered, depending on location and productivity.

• Relocation chances rise most strongly with productivity for firms in the East of England. Elsewhere the effect of productivity is rather mild.

• SMEs that are based either in London or the South East are most likely to relocate (including migration between these two regions). This is indicated by the negative coefficient for the region variables and is due to the effects of the firm/plant/product life cycle and the ‘core’ position of these regions.

• Regions with the next highest probability of relocation are the East Midlands and East of England. The region with the lowest predicted probability of relocation is Scotland at all levels of productivity.

• Confirming hypothesis 3, size (employment) is positive and significant in the model. The positive relationship between relative SME size and the probability of relocation has no tendency to diminish with enterprise size.

• Hypothesis 4, that more personal forms of business organisation are less likely to move, is borne out. The legal form coefficients of company and partnership are statistically significant . As expected, companies have the highest probability of relocating, followed by sole proprietors and then partnerships.

• Hypothesis 5, that partial relocation is more likely than full relocation for a firm is confirmed in Table 5. The number of plants has a positive relationship with the probability of relocating.

• The takeover variable in the relocation model is significant and positive (hypothesis 6).

• Some of the region-takeover variables are also significant and positive (hypothesis 7) but the productivity-takeover interaction is not. For all regions takeovers increase the chances of relocation .

• The largest effect is for SMEs in the North East (2.78 percent) and Scotland (1.97 percent). The smallest marginal effect is for SMEs in the East Midlands (0.45 percent), East England (0.49 percent) and London and the South East (0.55 percent).

• Whereas a takeover in Wales triples the chances of relocation away from Wales, a London takeover only increases the probability of movement out of London by one third. This may be a source of policy concern in the periphery. But takeovers also have other impacts on productivity and survival chances of SMEs, which act in the opposite direction to relocation effects for peripheral regions (Foreman-Peck and Nicholls 2013).

‘Differences in differences’ equation for impact of relocation on employment or productivity

• Where ∆ denotes the difference between the value in 2004 and 2007, and Y =employment or Relative Labour Productivity;

∆Y = β0 Relocation t-1+ β1Aget + β2ln(RLPt-1) + β22(Relocationt-1*ln(RLPt-1))+ β3(rsizeE

t-1) + β4(Legal form)t-1 + β5(Number of plants)t-1 + β6Takeoverst-1 + β7ln(Relocationt*Employmentt-1) + β71(Relocationt*Locationt-1) + β8 Location …(2)

• For expanding firms we expect β0>0 and β0 + β22 ln(RLPt-1) + β7ln

Employmentt-1 + β71 Locationt-1>0, H2b. • Also for growing SMEs, if London functions as the spatial core region we

expect positive contributions of SME net exports to employment so long as neoclassical ‘trickle down’ is occurring,

• H8b; β71Relocationt+ β8 >0,

when Location indexes London location in 2004 and London differs from the national economy as a whole, where relocation is concerned.

Productivity• Among SMEs with positive employment growth between 2004 and

2007 the direct effect for those that relocated was to expand labour productivity by 8.5 percent more than those that did not, in line with the lifecycle of the entrepreneurial firm.

• This conclusion must be qualified by the significant interaction terms- with employment and with productivity; larger employers that moved raised employment, though the opposite effect is found for more productive SMEs.

• Ignoring the London effect for movers, at the (growing) sample means the net effect of mobility is slightly larger at 10.2 percent, {(+.0822 +(.00194*5.4109) –(.0808*-.06292)) =) 0.098 and (100*(exp(.098)-1)=10.2.}

• But on average larger (employment increasing) firms – those employing more than 20- raised productivity when relocating by much more, by (100*(exp(0.258) -1)=) 29.4 percent (the interaction terms were not statistically significant). .

•

• Employment expansion proportionately bigger over the range of firm size tested.

• Relocating SMEs that have positive employment growth raise employment by (0.129→) 13.8 percent compared to growing firms that do not move.

• Those with more than twenty employees create (0.199→) 22 percent more jobs, while those employing more than 50 enhance employment by (0.301→) 35 percent apparently as a result of relocating.

Relocation Effects on Employment and Labour Productivity for SMEs Expanding Employment 2004-7:

OLS Regressions

Dep. Var.All Emp04>20 Emp04>50 All Emp04>20 Emp04>50

(1) (2) (3) (4) (5) (6)Relocation06 0.0822*** 0.258** 0.138 0.129*** 0.199*** 0.301***

(0.0106) (0.0886) (0.187) (0.0117) (0.0451) (0.0875)Reloc* emp04 0.00194*** -0.00021 0.000399 0.000344 -0.000688 -0.00147

(0.000581) (-0.00096) (0.00145) (0.000391) (-0.000511) (-0.000803)Reloc*RLP -0.0808*** -0.0944 -0.235** -0.022 0.06 0.0645

(-0.011) (-0.0569) (-0.0871) (-0.012) (0.0319) (0.0602)Reloc*lond -0.0908*** -0.221** -0.321* -0.0407* 0.137 0.123

(-0.0148) (-0.084) (-0.141) (-0.0188) (0.0745) (0.124)Reloc*nw -0.0522 -0.0407 -0.256 0.0023 -0.0216 0.0771

(-0.0273) (-0.195) (-0.159) -0.0419 (-0.0907) (0.145)

N 699428 40687 14172 334209 25866 9197adj. R-sq 0.205 0.225 0.248 0.159 0.104 0.095

Log Difference in Relative Labour Productivity 2004-7 by initial size

Log Difference in Employment 2004-2007 by initial size

Some specific regional effects

• London-based SMEs increased employment and productivity relative to all other expanding firms except Scotland’s .

• But contrary to the descriptive statistics, those London-based enterprises that moved did so by rather less.

• Nonetheless they boosted employment for the average mover by (0.129-0.0407=0.088→) 9.2 percent, which in view of the great volume of London’s SME net exports, must constitute a significant contribution to reducing regional disparities.

• This conclusion confirms hypothesis 8b• SMEs in Wales raised relative productivity between 2004 and

2007 by less than any other UK region.

Contraction

• Just as mobility helps expansion it also permits or is associated with stronger contraction.

• Among contracting firms those that move reduce employment by more (though this effect cannot be found for larger SMEs).

• Whereas cutting employment may be helpful for profits, reducing productivity is not.

• Presumably mobility for smaller enterprises - the majority - causes unanticipated problems of downward adjustments

• whereas in fact for firms employing more than 50 there is no statistically significant relocation effect on productivity.

Relocation Effects on Employment and Labour Productivity for SMEs Contracting Employment

2004-7: OLS Regressions

(1) (2) (3) (4) (5) (6)

Dep. Var. Log Difference in Employment 2004-7 by initial

size Log Difference in Relative Labour Productivity2004-7 by

initial size

All empl04>20 empl04>50 All empl04>20 empl04>50,

Relocation06 -0.162*** -0.497*** -0.412 -0.167*** -0.281*** -0.0912

(-0.0119) (-0.0875) (-0.211) (-0.0131) (-0.0765) (-0.0967)

Reloc*em04 -0.00330*** 0.000298 -0.000094 -0.000863 0.00167* 0.0000335

(-0.000634) (0.000854) (-0.00138) (-0.00047) (0.000743) (0.000792)

Reloc* RLP -0.0245** -0.0244 -0.0192 -0.0434*** -0.0607 -0.033

(-0.00837) (-0.0383) (-0.0552) (-0.013) (-0.0446) (-0.0819)

Reloc*lond 0.0816*** 0.205* 0.248 0.0395* -0.0934 0.0383

(0.0183) (0.0984) (0.166) (0.0198) (-0.116) (0.119)

Reloc*nw 0.00794 0.229 0.065 0.0712 0.0386 0.0286

(0.0362) (0.153) (0.239) (0.039) (0.094) (0.116)

N 244649 28655 10286 588016 21895 6643

adj. R-sq 0.068 0.171 0.213 0.071 0.101 0.109

Some specific regional effects

• For the average contracting SME, moving out of London is associated with employment and productivity expansion, consistent with achieving cost or other advantages from their new sites.

• In the North East of England and Wales contracting SMEs reduced employment by the least, other things being equal, suggesting competition was less pressing in these peripheral regions.

Conclusion• SMEs that relocate are more productive, relatively larger and younger. • Spatially mobile enterprises are more probably registered as

companies, taken-over and located in London and the South East (core locations).

• In addition these expanding businesses become more productive and employ more workers after moving than other growing SMEs that stay put.

• Relocation is also a strategy for contracting enterprises, but not necessarily a helpful one.

• Among contracting firms those that move reduce employment and productivity by more, though this effect only pertains to smaller companies.

• Since London and the South East is the highest income and productivity region, their net export of employment-expanding SMEs must tend to reduce regional disparities

• Acknowledgments This research was supported by the Economic and Social Research Council and Welsh Assembly Government (Grant Number PTA-040-2006-00004). The work contains statistical data from the Office of National

• Statistics (ONS), which is Crown copyright and reproduced with the permission of the controller of Her Majesty’s Stationery Office and Queen’s Printer for Scotland. The use of the ONS statistical data in this work does not imply the endorsement of the ONS in relation to the interpretation or analysis of the statistical data. This work uses research data sets which may not exactly reproduce National Statistics aggregates.