Paul Cheshire, Christian Hilber and Ioannis

Kaplanis

ERES Conference, Milan

24th June 2010

Land Use Regulation and Retail:Space Constraints and Total Factor

Productivity

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 space2) Investigating connection to differences in planning restrictiveness3) 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 production2) ‘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 groupsStrong finding larger stores more productive and profitableMore 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

23

45

appl

ica

tion

s fo

r re

tail-

ma

jor

1980 1990 2000 2010calendar year

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- or out-of centre definedfor planningpurposes – Merryhill

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…

Variable Obs Mean Std. Dev. Min Max

Sales/employment 357 4246 544 2349 5706 Sales 357 921115 406300 73978 2056014 Employment 357 213 85 32 471 Net floorspace (sq.ft.) 357 46710 17352 8313 101091 Gross floorspace (sq.ft.) 357 81633 31095 15076 180000 Food floorspace 357 27819.6 10144.7 0 54290 Non-food floorspace 357 18890.5 9859.5 671 52576 Net/gross floorspace (ratio) 357 0.58 0.07 0.33 0.83 Density (empl/1,000 Sq.ft) 357 4.57 1.10 1.01 7.40 Non-food format (dummy) 357 0.06 0.24 0 1 Mezzanine (dummy) 357 0.17 0.38 0 1 Petrol station (dummy) 335 0.52 0.50 0 1 Parking spaces 356 576 264 82 2000 Years since first opening 357 14.4 10.5 1 43 Total opening hours 357 119 29 64 168 Population within 10mins 357 81226 43706 5532 229246 Car ownership share within 10 mins drive 357 0.70 0.08 0.45 0.88 Competition variable 357 4.97 3.49 0.29 23.30

Table 2 Summary Statistics

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

-.4

-.2

0.2

.4

1980 1985 1990 1995 2000 2005 2010

435

b co

effi

cien

t (m

ajor

res

iden

tial p

roje

cts)

calendar yearGraphs by lacode_num

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

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

-.2

0.2

.4

1980 1985 1990 1995 2000 2005 2010

435

b co

effi

cien

t (m

ajor

ret

ail

pro

ject

s)

calendar yearGraphs by lacode_num

Nos ofmajor retail lowrelative to major resid.- so morenoise

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) does price of space vary with official

locational classification?3) 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)

Location Type No of stores Mean Net floorspace (sq.ft.) S. D.

Town Centre 46 42609 15429

District Centre 41 45564 18053

Suburban Centre 25 44732 10202

Edge of Centre 63 43598 16527

Out of Town 123 50889 17459

Destination 13 63760 22824

Retail Park 25 52015 14063

Non-food Format 21 28279 5086

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

Only ‘Destination’ stores clearly larger on average

Table 3b Floorspace costs by ‘location type’

Location Type

Rateable value

2005/net floorspace

S.D

Rateable value

2005/gross floorspace

S.D.

Rateable value

2010/net floorspace

S.D.

No of stores

Town Centre 23.5 6.7 12.9 3.6 33.5 8.9 45 District Centre 24.7 6.9 14.4 4.6 37.1 9.5 39 Suburban Centre 27.8 4.7 15.3 2.5 35.9 6.7 21 Edge of Centre 26.3 6.0 15.0 3.8 36.2 6.9 60 Out of Town 26.7 5.8 15.4 3.5 37.8 6.6 112 Destination 31.8 3.8 17.6 3.8 41.2 4.9 12 Retail Park 27.8 9.3 16.2 6.3 40.6 14.4 21

Non-food Format 13.8 4.5 9.9 2.8 17.4 5.6 13 All stores 25.7 6.8 14.8 4.1 36.1 9.0 323

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 Lβ2 Kβ3 eγX eu lnY i = β0 + β1 lnFi + β2 lnLi + β3 lnKi + Xʹ i γ + Xʹ α δ + u (RTS= β1 + β2 + β3) Y: sales of store i; or gross margins Y= PQ- PwQw or Y= PQ - PwQw - PmM F: floorspace; L: labour; K: capital for store i Xi: vector of store specific controls Xα: 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

2000

3000

4000

5000

6000

0 20000 40000 60000 80000 100000NET SALES AREA (SQ FT)

Sales per employee Fitted values

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

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

• Test model only on English sample (availability of planning data)

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

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

R-squared 0.962 0.963 0.965 0.965 0.965 0.965

Figure 8: Productivity by year of opening

productivity

6.74

6.76

6.78

6.8

6.82

6.84

6.86

6.88

6.9

1966

1969

1972

1975

1978

1981

1984

1987

1990

1993

1996

1999

2002

2005

2008

productivity

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 1980sAnd falls strongly thereafter. Looks like PPG6 ….

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

(1) (2) (3) (4) VARIABLES All England >1980 >1990 >1997 OLS OLS OLS OLS Refusal rate -0.485 -0.642* -1.058** -0.900 (residential) (-1.508) (-1.818) (-2.255) (-1.583) IV IV IV IV Refusal rate -0.746 -1.024* -1.546** -1.371 (residential) (-1.401) (-1.782) (-2.036) (-1.466) First Stage % of -0.192 -0.191 -0.198 -0.190 Labour seats (-12.37) (-12.81) (-12.25) (-10.34) F excl.instr. 153.05 164.22 149.96 106.85 Observations 254 221 143 114

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 collectedThe 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 (1) (2) (3) (4)

VARIABLES All

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

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 trips2. Reduce shop sizes – more trips plus more restocking3. Increase logistics costs

• To test - but seem likely ‘benefits’ = additional costs (+carbon)