1. THE UNIVERSITY OF BRITISH COLUMBIA CSR-Related Employee Relations and Innovation James A. Brander Sauder School of Business University of British Columbia Wei Zhang School of Finance Shanghai University of Finance and Economics And thanks Olena Ivus in advance for her very helpful comments.

2. THE UNIVERSITY OF BRITISH COLUMBIA Research Question Is there a relationship between corporate social responsibility (CSR)- related employee relations and innovation as measured by patenting and patent citations? We also say something about other dimensions of CSR (charitable giving, environmental CSR, etc. Potentially important given the importance of innovation for firm performance and general economic performance. There is a large literature on CSR and financial performance of firms, and a large literature on CSR-based mutual funds. The main finding is the absence of a negative relationship.

3. THE UNIVERSITY OF BRITISH COLUMBIA Why is this question interesting? Why might there be a relationship between employee relations (ER) and innovation? 1.CSR-related employee relations might affect employee motivation, either through financial incentives or other mechanisms. 2.Reverse causality or other forms of endogeneity. a.Successful innovation might generate ER spending b.A common factor might cause better ER and more innovation (unobserved heterogeneity).

4. THE UNIVERSITY OF BRITISH COLUMBIA ESG Data on Employee Relations Strengths 1 and 2 relate to financial incentives The other components relate to non-pecuniary motivations. The sum (strengths – concerns) = ER-total Firms are the 1000 largest firms traded on U.S. stock exchanges STRENGTHSCONCERNS i.cash profit sharing ii employee involvement (stock options, stock ownership, etc.) iiigood retirement benefitsretirement plan concerns iv.good union relationspoor union relations v.other strengthsother concerns

5. THE UNIVERSITY OF BRITISH COLUMBIA Patent Data We use the most recent version (which is not very recent) of the NBER Patent Citation file. It tracks patent grant data and citation data from up to 2006. We restrict attention to the “high-tech” sector of the U.S. economy using a set of 4-digt NAICS codes given by Heckler (2005). Sample period: 1991-2006

6. THE UNIVERSITY OF BRITISH COLUMBIA Regression Strategy The unit of observation is the firm-year, forming an unbalanced panel. For each firm we observe patents granted, patent citations received for those patents, ER variables and various control variables for each year it is in the data. We regress patent variables (patents granted and patent citations) on the ER variables and on various controls. Control variables include R&D spending, industry fixed effects, and time fixed effects. In robustness checks we use a battery of other control variables (Tobin’s q, leverage, etc.).

7. THE UNIVERSITY OF BRITISH COLUMBIA Patent Data Issues 1.Are patents a good measure of innovation? 2.Truncation bias 1: lag between when a patent is filed and when it is granted. A patent is assigned to year x if is applied for in year x and subsequently granted. It typically takes 2 or 3 years. Time fixed effects are often used as a partial correction. 3.Truncation bias 2: patents get more citations as time passes. One correction is to adjust citation counts based on citations compared to other patents filed in the same year.

8. THE UNIVERSITY OF BRITISH COLUMBIA Patent Production Functions There is a large literature on estimation of “patent production functions” – regressing patent counts or patent citations on variables thought to generate R&D. This work was pioneered by Zvi Griliches and important contributions include Hausman, Hall, and Griliches (1984) and Jaffe and Trajtenberg (2002). Our innovation is to use CSR-related ER variables as explanatory variables.

9. THE UNIVERSITY OF BRITISH COLUMBIA Summary Statistics 527 different firms for varying lengths of time over the 1991-2006 period. The firms with the most patents are IBM, Microsoft, Intel, and General Electric (more 1000 each per year). And even in the high tech sector, there are lots of zeros. VariableNMeanSD 5 th pc Median95 th pc Patent Grants439668.1234.902326 Adj. Citations 4396 80.7279.800383 ER-financial 4396 0.360.57001 ER-non-pecuniary 4396 -0.080.6101 ER total 4396 0.270.8702 R&D ($2005 billion) 4396 0.350.8600.071.82 Employees (‘000s) 4396 22.139.20.78.294.6

10. THE UNIVERSITY OF BRITISH COLUMBIA Functional Form? Y it = f(ER it, X it, e it ) Y it is patents granted or citations for firm i at time t. Use log-linear and generalized Poisson regressions. But there are still “excess zeros” – firms that no chance of patenting. This suggests using a mixture distribution or “zero inflation” (ZI) approach.

11. THE UNIVERSITY OF BRITISH COLUMBIA Stocks or Flows Current patent success reflects not just the current year’s effort but effort over several years. Therefore, arguably control variables should be stocks rather than current period values. In fact, this makes little difference but we use and R&D stock constructed using a standard depreciation rule (straight line depreciation over 5 years). We do not have luxury of using 5-year stocks for ER variables due to data limitations. We use a two-year moving average.

12. THE UNIVERSITY OF BRITISH COLUMBIA Correlation Matrix (1)(2)(3)(4)(5)(6) (1)Patents granted1 (2)Citations0.971 (3)ER financial0.20 1 (4)ER non-pecuniary0.14 0.091 (5)ER-total0.220.230.720.761 (6)R&D stock (real)0.570.540.240.120.241 (7)Employees0.550.510.07 0.100.60

13. THE UNIVERSITY OF BRITISH COLUMBIA Pooled Regressions: ER-total (based on clustering at firm level)

14. THE UNIVERSITY OF BRITISH COLUMBIA Main Result The Employees Relations variable has a significant effect in all specifications. A 1-unit increase corresponds to (roughly) a 20% to 40% increment in patenting. What about financial and non-pecuniary decomposition? Both have an effect as shown on the next slide.

15. THE UNIVERSITY OF BRITISH COLUMBIA Decomposing Financial and Non- pecuniary motivations

16. THE UNIVERSITY OF BRITISH COLUMBIA Industry-Specific Regressions Is the ER-innovation relationship general across high-tech industries?

17. THE UNIVERSITY OF BRITISH COLUMBIA What about endogeneity? 1.Endogeneity of the unobserved heterogeneity type. For example, suppose firms with “progressive managers” are more inclined to patent more and to more inclined to treat employees well. (Traditionally called third factor or omitted variable problems). 2. Endogeneity of the reverse causality type in that innovative success leads to better treatment of employees rather than the reverse. No particular to suspect major endogeneity problems but they are possible and we do what we can to address them.

18. THE UNIVERSITY OF BRITISH COLUMBIA Standard Responses A standard response to unobserved heterogeneity is panel data methods – typically fixed effects. This estimates the model on time series variation “within” a given firm or panel. But most of our variation is cross-sectional. It is possible to do some things but I will not discuss it here. It is possible to use instrumental variables (IV).

19. THE UNIVERSITY OF BRITISH COLUMBIA IV approach to deal with Reverse Causality. We would like a variable that identifies the exogenous component of employee relations. We use a technique pioneered by Berry, Levinsohn and Pakes (1995). Applied in this case, the proposed instrument is the average employee relations score for all other firms in a given state and year. The logic is that there are state-level variations (including in the legal and regulatory environment) that affect a given firm’s ER. If we regress a firms’ employee relations ER score on this exogenous variable the fitted values should be the exogenous component of a firm’s employee relations -- the part due to the exogenous state-level influence.

20. THE UNIVERSITY OF BRITISH COLUMBIA IV Regressions (log specification)

21. THE UNIVERSITY OF BRITISH COLUMBIA Robustness Checks 1.All firms instead of just high tech sector, with and without “zero- inflation” Poisson method. 2.More control variables 3.Include firms added to the ESG data in 2003. 4.Using contemporaneous or lagged ER and R&D variables instead of stocks. 5.Other regression functional forms (Tobit, negative binomial) The basic results are robust.

22. THE UNIVERSITY OF BRITISH COLUMBIA Other CSR Variables 1.Charitable Giving 2.Environmental CSR 3. Others

23. THE UNIVERSITY OF BRITISH COLUMBIA Concluding Remarks 1.Innovation as measured by patenting is positively related to employee relations. 2.Both financial incentives and non-pecuniary motives seem significant. Possibly the primary effects are non-pecuniary (morale and institutional loyalty)