CBI Regional Trends Survey:

Innovation Question Analysis

Ciaran Driver, Tanaka Business SchoolImperial College, University of London

Christine Oughton, Management Department, Birkbeck College, University of London

CBI Regional Trends Survey Analysis: Objectives

• Are there significant differences between regions in the way in which innovation responds to its determinants?

• Are the effects of one region’s activities on another observable, and do these linkages vary across regions?

CBI Regional Trends Survey: Innovation Question – Data Source

• The data source is the CBI-Experian Regional Trends Survey.

• Quarterly data on innovation and other variables, with the innovation data dating from 1990.

• Data allows us to observe changes in innovation over time and across regions.

CBI Regional Trends Survey: Regional Data

Individual responses are aggregated to the regional level using a regional weighting matrix of value added weights for six industrial groups, and three size groups, with the simplification that the size distribution is assumed similar across the UK.

Innovation question (17a):

“Do you intend to authorise more or less expenditure in the next twelve months than was authorised in the past twelve months on product and process innovation?”

These “balance” replies are interpretable as rates of growth of the underlying variable (Driver and Urga 2004)

CBI Regional Trends Survey: Innovation Question

Ino for South_East, Scotland and East-Midlands

Figure 1 Innovation Variable (Q.17a) for Three Regions

X56SE

X56SC

X56EM

Quarters

-10

-20

-30

-40

0

10

20

30

40

50

1988Q3 1989Q4 1991Q1 1992Q2 1993Q3 1994Q4 1996Q1 1997Q2 1998Q3 1999Q4 2001Q1 2002Q2 2003Q3 2004Q4 2006Q1

• Product and process research

+

• Innovation through “Market Research and Design”

Thus it is a broad innovation indicator

What does the Innovation Question Capture?

0.75 EM 0.74 EE 0.55 NE 0.69 NW 0.72 SC 0.87 SE 0.79 SW 0.80 WA 0.86 WM 0.78 YH

Regional correlation between:Investment and Innovation intentions

• Granger causation

• from investment intentions

• to innovation intentions

• at 10% significance

• for six out of ten regions

Regional correlation between:Investment and Innovation Intentions

• Optimism Regressor

• “Are you more, or less, optimistic than you were four months ago about the general business situation in your industry?”

• We define the variable opt as the balance statistic

(% responding more minus % responding less)

Explaining Variation in Innovation Responses

Demand Interest rate Political conditions Exchange rate

“Optimism” Captures…

(Junankar 1989)

Specification

titit eOptInoIno 12,10

R_bar_sq

EM 0.41** 0.30** 0.44

EE 0.29** 0.20** 0.41

NE 0.60** 0.09 0.31

NW 0.14 0.25** 0.25

SC 0.39** 0.10 0.34

SE 0.46** 0.17** 0.59

SW 0.38** 0.20** 0.40

WA 0.27** 0.21** 0.29

WM 0.62** 0.20** 0.74

Y&H 0.31** 0.22** 0.35

11

Extended Specification

1

tjtjitjj

tjjtjjjtj

eUKTraTra

OptInoIno

,3,2

,11,0,

coefficient 2B 3

EM 0.09 0.50** EE 0.31** 0.37** NE 0.27** -0.05 NW 0.30* 0.35* SC -0.02 0.46** SE 0.24* 0.35** SW 0.18 0.43* WA 0.22+ 0.83** WM 0.42* 0.19 Y&H 0.04 0.18

Effects on Ino of regional and national training for each region

Sub Panel Results for Extended Specification

Panel (number of regions) Own-Region training ,lag1 UK training ,lag 2 Higher technical skills (6 ) 0.16 (0.008)** 0.33(0.000)*** Lower technical skills (4 ) 0.14 (0.06)* 0.34(0.006) ** Higher General education (4) 0.10(0.17) 0.34(0.000)*** Lower General education (6) 0.16(0.01)** 0.31(0.000)***

• Dominance of national-level training

• In regions that have high levels of general qualifications (A-levels or degrees), own-region training is not significant

Panel Results: Interpretation

• National training variable may not be stationary

• ADF tests and autocorrelation function suggest non-stationarity

Caveat 1

Significance for Own-Region Training when National Training Excluded

coefficient 2B Variable

Lag stationarity

EM 0.29** 2 SBC only EE 0.43** 1 YES NE 0.21* 1 YES NW 0.33+ 1 SBC only SC 0.23+ 2 YES SE 0.37*** 2 NO SW 0.17 1 YES WA 0.34* 1 SBC only WM 0.46** 1 NO Y&H 0.20+ 2 YES

Caveat 2

•Basic equation potentiallymis-specified in that innovation experiences a downward step break around 1999

•Introducing the regional training variable removes the need for a break dummy

•But is regional training truly exogenous?

CBI Regional Trends Survey Analysis: Objectives - Recall

• Are there significant differences between regions in the way in which innovation responds to its determinants?

• Are the effects of one region’s activities on another observable, and do these linkages vary across regions?

Effects of regional innovation on unit cost

• A regional model of unit cost

• Is national or regional innovation more important?

• Does innovation affect unit cost with a lag?

• We use the survey data to form a dependent variable of change in unit cost

• Change in unit cost is then regressed on:

• Lagged innovation intentions

• Survey-based capacity utilisation

A regional model of unit cost: innovation effects

Average Cost Specification

tjitjitjjtjjtjjjtj eUKInoInoCUAVCAVC ,3,2,11,0,

No regional innovation effects found

Significance for National Innovation on Regional Unit Cost AVC (SURE)

coefficient 3 Significance

Omitting own-region Ino

EM -0.33 EE -0.10 * NE -0.74** ** NW -0.52* ** SC -0.40* * SE -0.11 * SW -0.04 WA -0.31 * WM -0.36+ Y&H -0.51*** **

• Strong external effects?

• Errors in variables?

Why no Regional Innovation Effects?

Confirmation of National Innovation Effect in Panel Estimation of AVC

Variable Coefficient z-statistic Constant 7.62 7.27 AVC(-1) 0.40 8.46 AVC(-2) 0.22 5.10 LCU 6.04 4.45 INO_Region(-6) 0.01 0.87 INO_UK(-6) -0.34 -4.46 Hausman favours RE (Chi_2(5)=0.84) No of obs(groups) 580(1) Overall R_squared =0.40

Sub Panels: Effects on Average Cost

Panel (number of regions)

Own-Region innovation ,lag 6

UK innovation ,lag 6

Overall R_sq

Higher technical skills (6 )

-0.01 (0.91) -0.32(0.000)*** 0.45

Lower technical skills (4 )

0.03 (0.71) -0.37(0.003) ** 0.35

Higher General education (4)

-0.11(0.17) -0.17(0.07)+ 0.44

Lower General education (6)

0.05(0.42) -0.42(0.000)*** 0.40

Note that for Higher General Education regions, both variables arejointly significant and significant when entered singly

• Innovation in the CBI survey mainly seems to be associated with capital investment

• A reasonable first-cut model is obtained with a lagged dependent variable and the index of business optimism

• There is similarity across regions to the extent that the optimism elasticities can not be shown to be different across the 10 main regions

Conclusions_1

• National training affects innovation strongly but the variable may be non-stationary

• When national training is excluded, regional training is significant (at least at 10%) in all but one region

Conclusions_2

• Innovation intentions at national level affects perceived change in unit cost in each region

• There is little support for regional innovation affecting unit cost

• An exception is for the panel of regions with higher general education where both regional and national innovation variables are jointly significant or significant when entered singly

Conclusions_3