Nick Bloom Innovation

1. Nick BloomInnovation

2. Two approaches to innovation work • Macro: • Growth, but tough to come up with new angle, unless take an interesting new micro-macro angle • Business cycle – key question is does innovation change at a BC frequency (Comin & Gertler, 2006 AER) • Micro: • Labor: estimating innovation functions using patents, R&D as function of demand, taxes, laws etc.. • IO: Modelling industry systems as fully endogenous • Theory • Models of innovation • I have worked a lot on innovation historically but am doing less now (at least on R&D and patents) as it is a well trodden path

3. Philippe Aghion, Nick Bloom, Richard Blundell, Rachel Griffith and Peter Howitt “Competition and Innovation: An Inverted-U Relationship” Quarterly Journal of Economics (2005)

4. Overview • Estimates an Aghion-Howitt style model using micro-macro data • The fundamental idea was to: • Set up a stylized model with empirical predictions • Carry out non-linear estimation of innovation-competition relationship • Use a clean IV strategy and estimate on micro data • An important paper: • One first papers to estimate growth models using micro data • Very well stylized and accessible

5. Well cited paper (for its vintage)…

6. Although nothing compare to the Aghion & Howitt (1992)

7. Conventional wisdom, theory and empirics have conflicting views on competition & innovation link • Conventional wisdom provides mixed views “Competition effect”: “... from Adam Smith to Richard Caves: the belief is that competition is good...” (Nickell, 1996 JPE) “Schumpeterian effect”: “....anti-trust discourages innovation.......” (Bill Gate’s lawyers, frequently…) • Economic theory often supports the Schumpeterian effect of a negative competition effect on innovation • Empirical work typically finds a positive effect – i.e. Nickell (1996 JPE), Blundell, Griffith & Van Reenen (1999 RES)

8. Paper develops a stylised model of competition, innovation and growth across industries (1) • The economy contains many industries. Two firms in each • Industries are either: • “neck-and-neck” as firms have the same technology • “leader-follower” as firms have different technologies • Under low competition “neck-and-neck” firms earn moderate profits, so limited incentive to innovate, so: • “neck-and-neck” firms undertake little innovation, so equilibrium has mainly “neck-and-neck” industries • so increasing competition raises innovation as “neck-and-neck” firms increase innovation in response to more competition • “Escape competition effect”

9. Paper develops a stylised model of competition, innovation and growth across industries (2) • Under high competition “neck-and-neck” profits are low, so the rewards to innovating to become a leader are high, so: • “neck-and-neck” firms undertake a lot of innovation • equilibrium has mainly “leader-follower” industries • so further increases in competition lower the profits for followers to innovate and become “neck-and-neck” • “Schumpeterian effect” • This generates an inverted-U as competition first increases innovation (as mainly “neck-&-neck” firms) then reduces innovation (as mainly “leader-follower” firms)

10. Model Predictions The Model Provides Three Empirical Predictions • This higher the share of “neck-and-neck” industries the more positive the effect of competition • The share of “neck-and-neck” industries will decline as competition increases high Share of “neck-&-neck” industries low low high Competition high Innovation • Innovation and competition will have an inverted U-shape relationship low low high Competition

11. Estimate This Using (UK) Accounting Data • Measure innovation using patents (NBER file) matched to UK firm-level accounts data • Measure competition using a Lerner index (P-MC)/MC • Again use accounts data by assuming AC ≈ MC • Endogenous so instrument using policy changes of Privatizations and European Single Market Program (note: this was the justification for using UK data)

12. Competition and innovation – raw data (figure 1) • Like Syversson (2004) had a basic figure early in the paper Suggested by the editor (Glaeser), and very good idea

13. There are some fancy econometrics Basically, confirmed the inverted-U also held using splines Introduced splines so latter on could use non-linear IV

14. The control function approach for non-linear IV Non-linear IV is a good technique to use and control-function approach is a good way, easy & intuitive way to do this. Basic idea is two-step approach: Step 1: regress X (Competition) on the Z instruments (policy) in a non-linear fashion to soak out all the exogenous variation in X Xi,t = α + f(Zi,t) + vi,t Step 2: regress Y (innovation) on X plus the residuals vi,t (these contains the endogenous bit of X) to identify from the exogenous parts of X only Yi,t = α + β1Xi,t + g(vi,t) + ei,t

15. Including/excluding controls still get a inverted-U Note the standard results set-up. Start very simple on LHS and then slowly add junk as controls. Want to show robust in the simple and full specifications…

16. Including/excluding controls still get a perverted-U Note the full footnotes – always do this: (i) makes the paper clearer, (ii) signals you are more serious We actually forgot to mention why column (3) has only 67 obs

17. Also confirm the additional predictions that higher-dispersion associated with both higher level of competition, and higher response to competition One thing to note is the styling of the paper. The QJE editor (Glaeser) correctly requested we changed paper order from:“Theory then Empirics” To “Basic empirics, basic theory, additional theory, additional empirics” That is you can easily change the theory-empirics ordering around, or even integrate them in this case. Best way is to trial this out using presentations. Initial guideline is start with empirics if much stronger, otherwise start with theory

18. Innovation Stylized Facts Overview

19. Empirical tests typically use one of two data sources – R&D and patents • The standard (and more traditional) measure of macro, industry and firm-level innovation is R&D – best starting place: • Good: Measure $ of innovation inputs, time series • Bad: Often not available (small/private/European firms) • More recently large number of papers using the NBER patents database on around 6 millions patents plus their citations • Good: Huge amounts of details on innovation, plus citations (so can map out innovation process) • Bad: Patents very stochastic (cite weighting helps), hard to map to industries, hard to map to macro (patent office-cycles) • Note this data has been heavily used – “Blood out of stones”

20. Innovation has positive spillovers, particularly geographically locally and within technology fields • Using the patent data a couple of key papers showed: • Innovation is spills over locally (Jaffe, Trajtenberg and Henderson, QJE 1993). • Use patents data to show citations concentrated in State and Metropolitain area • Used a control group of patents in same technology class and year to control for agglomeration • Innovation spills over within industries (Jaffe, AER 1986) • Firms patents allocated to technology classes • Cross-firm correlation of allocation provides proxy of technology closeness (do they overlap in technology space) • R&D weighted by technology distance important in firm performance (spills-over most from technology neighbours)

21. The first paper is in the Econ top 100 cited papers

22. Market size is an important determinant of innovation – Schmookler (rather than Schumpeter) • Firms targets larger markets as rewards from innovation higher • Blundell, Griffith and Van Reenen (1999, RES) show that market share plays a very significant role in determining innovation: • Important to control for competition • Aghion and Linn (2006, QJE) show that market size plays an important role in determining pharmaceutical innovation • Instrument this with demographics to get identification • So in long-run growth models need something to offset size effects (Jones, 1995 QJE)

23. The direction of technological change is endogenous – Hick’s “induced innovation” Refinement of the market size story is technology is multi-dimensional and can alter course Acemoglu (1998 QJE, 2002 RESTUD ) model and estimate technology change responding to the increased supply of skills Newell, Jaffe and Stavins (QJE, 1999) show innovations in air-conditioners respond to energy prices Popp (2002, AER) shows a strong link between energy saving patents and energy prices