Land Use Regulation and Retail: Space Constraints and Total Factor Productivity

Paul Cheshire, Christian Hilber and Ioannis Kaplanis

ERES Conference, Milan 24

th

June 2010

•

This paper: hypotheses & intended contribution

Seems likely planning policy restricts land available for retail development: so increases costs of space: reduces retail TFP • Try to quantify the impact by: 1) estimating production function - including space 2) Investigating connection to differences in planning restrictiveness 3) Quantify impact on TFP and retail prices • Problem: Planning policy may negatively affect TFP via two distinct routes: 1)Restriction of land supply for retail raises prices and cause profit maximising retailers to substitute land out of production 2) ‘Town centre first policies’ may force to locate on smaller and less productive, higher cost ( for logistics, labour, customers) sites At this stage not distinguishing • Using microdata and detailed planning performance data

•

The issues….

Three factors of production: land labour and capital Forget land (unless agricultural economist) But land an input into production – in retailing: think Ikea!!

• In 1980s land for retailing in prosperous SE of UK 250 X land for retailing in comparable US location (Cheshire & Sheppard 1986) • • UK Planning system imposes (intentional) restrictions on supply of urban land via ‘containment’ & 60% brownfield • And restricts for each (legally classified) use Not surprising increases cost of housing: reduces supply elasticity => so increases volatility • • • • Nearly all work so far on housing; But Hilber & Cheshire 2008 – costs of office space Much higher in UK than continental Europe or New York • ‘tax’ on space in London West End equivalent of 800% over 1999-2005 ‘Town centre first’ + virtual prohibition on out of town large scale development => even higher cost for retail?

Another peculiarity of British planning – reliance on ‘development control’ => more politicised, less

planned

The issues….

• Increasing support for idea that planning policies reduce productivity in retail: McKinsey Global Inst. 1998; Barker, 2006; Haskel & Sadun, 2009 • Haskel & Sadun - first academic study: by preventing emergence of large format out of town stores estimates lost 0.4% p.a. TFP growth 1996-2006 • Also Competition Commission 2000; 2008 Well worth looking at: access to store level micro data for 4 main supermarket groups Strong finding larger stores more productive and profitable More local competition reduces store prices (CC 2008) And land for retail in UK x 5 to 10 in France (CC 2000)

•

Planning policy and its impact

Prior to 1988 relatively relaxed approach to retail as such – though clear evidence of overall space restriction via containment e.g.

Reading 1984 • 1988 PPG6 tried to steer out of town to ‘regeneration sites’ e.g.

Bluewater – but still not restrict competition • PPG6 1993: attempts steer to in-town sites because of belief free market might ‘under-provide’ in town shopping • • • • Big change – PPG6 1996 -More or less prohibited out of town development for all ‘town centre’ activities – i.e. not just retail but offices, leisure, restaurants -Introduced ‘Needs’ test + ‘Sequential’ test Fear - mainly a development control tool – ODPM (2004) Confirmed – even reinforced – by PPS6 2010 And implementation requires current local development plan – estimated less than half LPAs have them

Figure 1: Number of Applications for Major Retail Developments, 1979-2008

1980 1990 calendar year 2000 2010

Figure 2: Applications for Extensions to Foodstores, 1990 to 2001

Figure 3: Big 5 Supermarkets In- and Out of Centre Openings, 1990-2000

In-centre opening rise relative to out of centre.

But note

in-

of centre defined for planning purposes – Merryhill or

out-

Figure 4: Age of Building Stock by Use Category

And an aging stock of retail buildings….

Data, approach and some problems

• Store level data for all stores for major retailer – mainly food • Detailed development control data for all LPAs (so far collected only England): applications, refusals, delays & appeals • Stores geocoded - so also data for store catchment areas – population within given drive times, car ownership, competitor stores x distance, etc • Some summary statistics…

Table 2 Summary Statistics Variable

Sales/employment Sales Employment Net floorspace (sq.ft.) Gross floorspace (sq.ft.) Food floorspace Non-food floorspace Net/gross floorspace (ratio) Density (empl/1,000 Sq.ft) Non-food format (dummy) Mezzanine (dummy) Petrol station (dummy) Parking spaces Years since first opening Total opening hours Population within 10mins Car ownership share within 10 mins drive Competition variable

Obs

357 357 357 357 357 357 335 356 357 357 357 357 357 357 357 357 357 357

Mean

4246 921115 213 46710 81633 27819.6 18890.5 0.58 4.57 0.06 0.17 0.52 576 14.4 119 81226 0.70 4.97

Std. Dev.

544 406300 85 17352 31095 10144.7 9859.5

Min Max

2349 5706 73978 2056014 32 471 8313 15076 0 671 101091 180000 54290 52576 0.07 1.10 0.24 0.38 0.50 264 10.5 29 43706 0.33 1.01 0 0 0 82 1 64 5532 0.83 7.40 1 1 1 2000 43 168 229246 0.08 3.49 0.45 0.29 0.88 23.30

Data, approach and some problems

• • • How measure ‘planning restrictiveness’?

Use ‘refusal’ or ‘delay’ rate?

• Problem of endogeneity – developers’ behaviour may be influenced by LPA’s – the ‘discouraged developer’ effect So need instruments to identify: 1.

• • Exploit change in targets for delays more than 13 weeks – 2002 – separate for ‘minor’ and ‘major’ Expect more restrictive LPAs to both refuse more and delay more: not possible post-2002 =>So use

change

in delay rate pre- & post- 2002 2.

Or use political make-up of LPAs (Cheshire & Hilber, 2008: explicitly Haskel & Sadun, 2009, Hilber & Vermeulen, 2010); rise of NIMBYism

Figure 5: Plotting the coefficients from regressing refusal rate on delay rate: Residential (major) 1979-2008

435 1980 1985 Graphs by lacode_num 1990 1995 calendar year 2000 2005 2010

Figure 6: Plotting the coefficients from regressing refusal rate on delay rate: Retail (major) 1979-2008

435 Nos of major retail low relative to major resid.

- so more noise 1980 1985 Graphs by lacode_num 1990 1995 calendar year 2000 2005 2010

But are ‘Town centres’ actually town centres?

• • • • • The case of Merryhill; the comparative lack of current local development plans Town centre versus out of town may be planning definitions more than geographical, functional or economic!

Test 1) does size of store vary with ‘planning location’?

2) 3) does price of space vary with official locational classification?

are ‘planning locations’ strongly related to distance from town centre e.g. major rail stations?

Or do PPG6 1996 & PPS6 2010 really just more or less prevent all retail development and particularly large format retail development?

Done 1) & 2)

Table 3a Number of stores and average floorspace by ‘location type’

Location Type

Town Centre District Centre Suburban Centre Edge of Centre Out of Town Destination Retail Park

No of stores

46

Mean Net floorspace (sq.ft.)

42609 41 25 45564 44732 S. D. 15429 18053 10202 63 123 13 25 43598 50889 63760 52015 16527 17459 22824 14063

Non-food Format 21 28279 5086

Only ‘Destination’ stores clearly larger on average

Table 3b Floorspace costs by ‘location type’ Location Type

Town Centre District Centre Suburban Centre Edge of Centre Out of Town Destination Retail Park

Non-food Format

All stores Rateable value 2005/net floorspace 23.5 24.7 27.8 26.3 26.7 31.8 27.8 13.8 25.7 S.D 6.7 6.9 Rateable value 2005/gross floorspace 12.9 14.4 4.7 6.0 5.8 3.8 9.3 15.3 15.0 15.4 17.6 16.2 4.5 6.8 9.9 14.8 S.D. 3.6 4.6 Rateable value 2010/net floorspace 33.5 37.1 2.5 3.8 3.5 3.8 6.3 35.9 36.2 37.8 41.2 40.6 2.8 4.1 17.4 36.1 S.D. 8.9 9.5

No of stores

45 39 6.7 6.9 6.6 4.9 14.4 21 60 112 12 21 5.6 9.0 13 323 But unit price of ‘Destination’ stores highest: town centre cheapest - contrast with distance decay of price in Reading 1984

Simple Cobb-Douglas production function

Y = A F β1 L β2 K β3 e γX e u

ln

Y i = β 0 + β 1

ln

F i + β 2

ln

L i + β 3

ln

K i +

Xʹ (RTS=

β 1 + β 2 + β 3) i γ +

Xʹ

α δ + u

Y: sales of store

i

; or gross margins Y= PQ- P w Q w or Y= PQ - P w Q w - P m M F: floorspace; L: labour;

K

: capital for store

i X i

: vector of store specific controls

X α

: vector of area specific controls No detailed info on margins but assured they are constant by item across stores. So using

sales

as measure of ‘output’

Figure 7: Relationship of productivity (sales/employment) to net floorspace

0 20000 40000 60000 NET SALES AREA (SQ FT) Sales per employee 80000 Fitted values 100000

Table 4: Basic results from a TFP approach with Total Sales as ‘output’

VARIABLES

Net Floorspace Employment Mezzanine dummy Non-food format dummy Hours Constant Observations R-squared (1) 0.0472 (1.407) 1.083 (37.76) 7.405 (29.64) 357 0.958 (2) 0.0972 (2.665) 1.043 (35.42) -0.0594 (-2.815) 7.093 (26.25) 357 0.959 (3) 0.128 (2.719) 1.000 (22.15) -0.0499 (-2.408) -0.0815 (-1.091) 6.989 (23.45) 357 0.959 (4) 0.118 (2.542) 0.974 (20.27) -0.0547 (-2.685) -0.0775 (-1.052) 0.000915 (3.246) 7.126 (24.54) 357 0.961

• • •

Findings….

• Clear evidence productivity rises with store size Elasticity 0.1 to 0.13

• • Productivity also rises with number of hours open and employment Falls with non-food format and if mezzanine Non-food format stores have different production functions Add controls: Competition Characteristics of catchment area Age of store (date of opening) Test model only on English sample (availability of planning data)

Table 5 Add further store & area controls; UK&England VARIABLES

Net Floorspace Employment Mezzanine dummy Non-food format dummy Hours Years since opening Years since opening sq. Population within 10mins Car ownership share within 15m Competition variable Constant Observations (5) UK 0.135 (2.925) 0.936 (19.39) -0.0430 (-2.168) -0.105 (-1.433) 0.00106 (3.745) 0.00222 (3.402) 7.098 (24.78) 357 (6) UK 0.140 (3.107) 0.902 (18.86) -0.0393 (-2.025) -0.133 (-1.821) 0.00102 (3.653) 0.0106 (3.925) -0.0235 (-3.428) 7.183 (25.56) 357 (7) UK 0.102 (2.185) 0.918 (19.29) -0.0387 (-2.081) -0.135 (-1.839) 0.00101 (3.695) 0.00900 (3.335) -0.0201 (-2.957) 0.0444 (3.742) 7.024 (25.42) 357 (8) UK 0.103 (2.207) 0.913 (18.77) -0.0382 (-2.020) -0.140 (-1.891) 0.00104 (3.787) 0.00910 (3.377) -0.0203 (-3.021) 0.0491 (3.799) 0.0769 (1.050) 6.923 (22.57) 357 (9) UK 0.115 (2.538) 0.899 (18.94) -0.0391 (-2.110) -0.145 (-1.958) 0.00103 (3.807) 0.00942 (3.529) -0.0213 (-3.195) 0.0570 (4.164) 0.0945 (1.293) -0.00379 (-2.078) 6.783 (22.21) 357 (10) ENGLAND 0.144 (2.559) 0.846 (13.79) -0.0365 (-1.765) -0.257 (-2.870) 0.000905 (2.541) 0.0123 (4.074) -0.0272 (-3.705) 0.0509 (2.885) 0.0740 (0.835) -0.00415 (-2.236) 6.844 (19.33) 269 R-squared 0.962 0.963 0.965 0.965 0.965 0.965

Figure 8: Productivity by year of opening

Impact of store age is interesting/suggestive – using estimates from model (9) =>Oldest stores least productive (no surprise) but productivity falls cet. par. in stores founded from late 1980s And falls strongly thereafter. Looks like PPG6 …. productivity 6.9

6.88

6.86

6.84

6.82

6.8

6.78

6.76

6.74

productivity

Role of planning?….

• Is store size influenced by ‘restrictiveness’ of local LPA?

• Test against: 1.

Refusal rate – both major residential and major retail (note major retail numbers can be small and seem noisy) 2.

Instrument 1 – change in delay rate following new targets in 2002 - measured as change in mean delay rate 1994-98 & 2004-08 3.

Instrument 2 – % share of labour seats at the local elections (average over 2000-2007)

Table 6: Regressing floorspace on ‘planning restrictiveness’ (major residential projects refusal ratio); IV: share of Labour seats

VARIABLES Refusal rate (residential) Refusal rate (residential)

First Stage

% of Labour seats F excl.instr. Observations (1) All England

OLS

-0.485 (-1.508)

IV

-0.746 (-1.401) -0.192 (-12.37) 153.05 254 (2) >1980

OLS

-0.642* (-1.818)

IV

-1.024* (-1.782) -0.191 (-12.81) 164.22 221 (3) >1990

OLS

-1.058** (-2.255)

IV

-1.546** (-2.036) -0.198 (-12.25) 149.96 143 (4) >1997

OLS

-0.900 (-1.583)

IV

-1.371 (-1.466) -0.190 (-10.34) 106.85 114

Notes:

The dependent variable is log(net floorspace). The sample excludes non-food formats.

The sample is restricted to the stores that are located in England – only regulation data collected The refusal rate is calculated as the ratio of declined major residential projects applications to the total number of applications and averaged over 1979-2008 ; t-statistics in parentheses

Table 7: Regressing floorspace on planning restrictiveness- alternative measures

OLS regressions VARIABLES Refusal rate (retail projects) Change in delay rate (major residential) Observations (1) All England -0.0509 (-0.180) 0.0688 (0.565) 254 (2) >1980 -0.132 (-0.441) 0.0333 (0.255) 221 (3) >1990 -0.294 (-0.621) 0.371** (2.082) 143 (4) >1997 -0.223 (-0.426) 0.455** (2.029) 114

Notes:

The dependent variable is log(net floorspace). The sample excludes non-food formats. t-statistics in parentheses. The sample is restricted to the stores that are located in England – only planning data collected.

refusal rate: ratio of declined major retail project applications to the total number of applications and averaged over 1980-2008 (the period for which regulation data exist). delay rate: change in the average delay ratio of applications pending for more than 13 weeks between the period 1994-98 and the period 2004-2008.

• • •

Conclusions

1. Strong confirmation that productivity rises with store size So - restricting stores sizes by either direct constraints on sites/formats, or restricting supply of land so raising prices

=>

Increases resource use in retail and raises retail prices Clear welfare cost: but not yet quantified (possible) • • • 2. Clear evidence that more restrictive local planning policy causes stores to be smaller By implication planning policy responsible for lower retail productivity See impact of restrictiveness from late 1980s and esp. 1990s • Since poorer spend proportionately more of disposable income in stores (esp. food) this is distributionally regressive • Net costs? What are the benefits – esp. of ‘Town centre first’?

• • • • • •

Concluding Discussion …

Benefits? Claimed… Town centre sites ‘most sustainable’ because most accessible by alternative transport modes + allow ‘linked trips’ so ‘reducing need to travel’ But need to distinguish between what people ‘should do’ and what they actually do Continue to decentralise: use cars for shopping: car use continues to rise at about same rate – just more congestion So town centre locations likely: 1.

Separate households from shops – lead to longer & more congested trips 2.

3.

Reduce shop sizes – more trips plus more restocking Increase logistics costs To test - but seem likely ‘benefits’ = additional costs (+carbon)