1. Innovation and Corporate Performance Andy Cosh & Carolos Georgallis with Anna Bullock Centre for Business Research University of Cambridge Preliminary Results: Not to be Quoted

2. Objectives of Study Analysis of alternative measures of innovation at the level of the firm. Investigation of the relationship between innovation activity and the firm’s financial results.

3. Research Approach Use several measures of innovation. Allow for lags in the relationship : the effects of innovation may not appear immediately in our measure of performance. Need to question the direction of the causal relationship. Draw the sample from those firms present in both CIS2 & CIS3. Utilise FAME dataset (more complete & objective financial accounts)

4. The UK Sample Selection Identify which firms are present in both the CIS2 and CIS3 survey (thus gathering innovation data from 1994-2000) – 785 firms Identify which of these firms are present in the FAME database (limited companies only) – 624 firms called the FAME dataset. Check how representative this set of firms is when compared with the complete CIS samples.

5. Possible Biases Size bias since the firm appears in both CIS2 and CIS3 surveys. Survival bias since our firms must be in both CIS2 and CIS3. For a firm to be in our sample it must be a limited liability company – a further potential bias. Coverage and response rate changes between the surveys also leads to potential size and sector biases.

6. Comparing datasets Compare innovation and financial profiles of firms in CIS2 and in CIS3 with the Fame dataset for the relevant periods. Find, as expected, firm size and sector differences between Fame dataset and the complete survey samples. Therefore we introduce a control set drawn from CIS3 which is identical in size and sector to the Fame dataset. We then compare the Fame dataset with the whole CIS3 dataset after allowing for size and sector differences.

7. Firm Size Comparisons BandsCIS2CIS3 FAMESET (CIS3 period) Freq.PercentFreq.PercentFreq.Percent 10-4987737.4476158.320833.33 50-24968029.0202324.822836.54 250-49926911.57228.88313.30 500-9992299.84024.9528.33 1000+28712.32643.2538.49 Total23421008172100624100

8. Firm Sector Comparisons IndustryCIS2CIS3 FAMESET (CIS3 period) Freq.PercentFreq.PercentFreq.Percent Manufacturing159868.2456755.945172.3 Services74431.8360544.117327.7 Total23421008172100624100

9. Firm Innovation Comparisons [1] Product Innovator Complete CIS2 FAMEset (CIS2 period) Complete CIS3 FAMEset (CIS3 period) Control Set (CIS3 period) Freq% % % % % No 11675130349496427797941366**45373** Yes 1121493215117452121134**17127** Total 22881006241008172100624100624100

10. Firm Innovation Comparisons [2] Process Innovator Complete CIS2 FAMEset (CIS2 period) Complete CIS3 FAMEset (CIS3 period) Control Set (CIS3 period) Freq% % % % % No142962376606685828244071**47476** Yes85938382484014871818429**15024** Total22881006241008172100624100624100

11. Defining financial performance We can now turn to our initial analysis of the link between innovation and firm performance. Several measures are available to us: Value Added Number of Employees Sales / employees Profit Margin Return on Total Assets Exports / Sales

12. Classifying Innovators –Classify firms based on innovation active periods: InnovatorNon innovator CIS 3 CIS 2 Innovator Non Innovator

13. Persistent Innovator Non Persistent Persistent Non-innovator InnovatorNon innovator CIS 3 CIS 2 Innovator Non Innovator Classifying Innovators

14. Prior Innovator Non Prior Innovator InnovatorNon innovator CIS 3 CIS 2 Innovator Non Innovator Classifying Innovators

15. Initial Work – Bivariate Analysis [1] Prior and Persistent Innovators segregated further according to their innovation patterns e.g.: –Size of firm effects –Novel or new-to-firm (or both) innovators –Product or process (or both) innovators –Hi-tech or Lo-tech sector innovators (CBR classification)

16. Initial Work – Bivariate Analysis [2] Investigation of financial performance variables across the innovator groups. Focus mainly on performance trends from 1996 – 2000. Analyse the effect of innovation on subsequent performance change. Analyse the performance superiority of persistent innovators compared with others.

17. Bivariate findings [1] Prior InnovatorsPersistent Innovators SmallLargeSmallLarge InnNonInnNonInnNonInnNon Profit Margin (%) No. of Cases 616616876329597147 Mean 2.15-2.33-1.270.503.914.275.904.97 Median 1.11 * -1.78 * -1.02-0.293.853.384.373.46 Turnover per employee No. of Cases 40405216776227096147 Mean 39.6412.4013.1078.3095461205237127024139033 Median 21.9 ** 4.30 ** 7.8413.1478456 * 99020 * 104476 ** 70876 **

18. Bivariate findings [2] Prior InnovatorsPersistent Innovators NovelNewNonNovelNewOtherNon ROTA (%) No. of Cases 112144170453663282 Mean -1.87-2.32-2.705.856.927.634.52 Median -3.31-2.66-2.715.95 * 6.71 * 8.44 * 4.57 * Value Added No. of Cases 94119134372953228 Mean -12.2790.8-32.2258572009219858144 Median -6.26-14.8-9.582697 ** 1147 ** 2199 ** 629 **

19. Bivariate findings [3] Prior InnovatorsPersistent Innovators Hi-techLo-techHi-techLo-tech InnNonInnNonInnNonInnNon Emp/eesNo. of Cases 4822182124343695211 Mean 15.79-0.0141.2339.75391.910511343622 Median 5.33 * -17.9 * 4.396.26149 * 118 * 312 ** 156 ** Exports to Sales (%) No. of Cases 58212271703940117280 Mean 3.47-1.070.16-1.2146.9235.7425.110.99 Median 1.590.860054.28 * 33.62 * 12.8 ** 0.81 **

20. Further Work The work is useful as a first look at the data and its inter-relationships. It is limited by being largely bivariate and it does not take account of the biases we discussed earlier. The next stage is to explicitly model the determinants of innovation and to take this into account in the assessment of its impact.