Innovation Activities and the Incentives for Vertical Acquisitions and Integration

Transcription

1 Innovation Activities and the Incentives for Vertical Acquisitions and Integration Laurent Frésard (Maryland) Gerard Hoberg (USC) Gordon Phillips (USC and NBER) March 2015

2 Background Long-standing economic question: What shapes the boundaries of the firm? Key in practice: how to organize the firm s activities? Vertical integration or market relationships? Key in theory: control (residual) rights Relationship-specific investment (RSI) Incomplete contracts Ex-post View ( Rent seeking view ) Vertical integration (VI) reduces risk of hold-up (ex-post) Williamson (1971,1979) or Klein, Crawford, Alchian (1978) Ex-ante View ( Property Rights Theory ) Costs and benefits associated with VI VI reduces (ex-ante) incentives for non-contractible investment by the party that loses control rights Grossman and Hart (1986), Hart and Moore (1990)

3 What we do Examine the determinants of VI and vertical acquisitions using a large panel dataset. Measures of VI are limited (industry-focus) Measuring investment incentives is hard Novelty: Use textual analysis to construct a new measure of vertical relatedness between firms (pairs) Combine text in firms 10K with text in input-output tables New identification of vertical acquisitions New (time-varying) firm-level measure of VI Innovation activities play an important role for VI Realized vs Unrealized innovation distinction is important. Distinction is related to investment incentives Supply chain Maturity/stability is also key to explain variations in VI

4 Our View - Hypothesis Incomplete contracts + multiple stage innovation Unrealized (R&D) vs Realized innovation (patents). Predict high R&D => less integration When innovation is unrealized, ownership of unrealized innovation should go to the party whose effort is most important => incentivize ex ante RSI Predict high patents => more integration. When innovation is realized, ownership of the asset (property rights) transfer to entity that will use and commercialize the patent. Incentive for RSI is less important once explicit property right has been developed.

5 Why focus on innovation? History of linking R&D to RSI (non-contractible part) Grossman and Hart (1986) and Hart and Moore (1990) Atalay, Hortascu, Syverson (2014) conclude intangible investments important for VI. Technological investments are non-contractible and non-verifiable: Control rights are key. Acemoglu (1996): Search frictions make such technological investments partner specific. Allen and Phillips (2000) and Kale and Shahrur (2007): R&D increases with firm interaction with alliance partners, customer and supplier contracts (RSI). Stage of innovation: Proxy for changes in parties incentives.

6 Illustrative Example: Apple In 2008, Apple Computers bought PA Semi Firm was experts in chips Specialized in making fast and low-power processors Now used in the ipad In 2010, Apple further bought Intrinsity Firm was specialized in designing (tiny) low-power processor Now used in the iphone Key: Products were patented (when acquired) Need for ex-ante investment by PA Semi/Intrinsity in RSI was low Commercialization investment by Apple becomes more important. Risk of rent-seeking by these chip makers potentially high Apple gains ex post property rights of new technology (along the supply chain)

7 Contribution Distinction between realized and unrealized innovation matters for firm boundaries Existing evidence on incentives for VI is limited Fan and Goyal (2006) [stock returns] and Ahern and Harford (2012) [shocks and waves]. (IO/Strategy) Existing evidence is mostly single industry [e.g. Joskow, Monteverde and Teece] Importance? Acquisitions and contracting both central issues in IO and Corp. Finance Past studies: Hard to measure VI Measurement of VI (Compustat Segment?) NAICS industries quality: processes not products Census - just manufacturing across plants [Atalay, Hortascu, Syverson (2014, AER)] All have limitations especially in tech or innovative sectors

8 Illustrative Model of the Integration Decision - Sketch Repeated game - model integration as a real option. Buyer and seller cooperate to produce a product Sold at price P s (P 0, P 1,..., P N ) Beginning of each period firms can integrate or not. If integrated, one firm gains control rights. After integration decision, upstream firm can conduct R&D to try to improve the product (RSI). If successful, firm with control rights gains patent (P ). Downstream firm can spend (deterministic) money to commercialize the product (P ). Higher gains to commercialization with higher price. Bargaining to split total surplus (V ) Non-contractible RSI as courts cannot verify. Firms again decide whether or not to integrate.

9 Key Result of the Model There exists a triggering state s for integration, where marginal product of R&D is < marginal product of commercialization. The effect is illustrated in the following figure. Figure: Marginal product of R&D Separation Integration 0 Marginal product of Commercialization

10 Plan 1. Introduction - Motivation 2. Measuring Vertical Relatedness 3. Properties of Vertical Network 4. Vertical Acquisitions 5. Vertical Integration

11 Existing measure(s) of vertical relatedness? Input-Output Table (Bureau of Economic Analysis BEA) Define some thresholds for V i,j (1% or 5%) with i and j being NAICS industries All the firms in NAICS i are vertically related to All the firms in NAICS j Same vertical intensity between (fixed) industries Two networks: NAICS-1% and NAICS-5% (We relabel later)

12 Firm 10-K vocabulary Hoberg and Phillips (2014) technology (R&R at JPE) Automated methodology to read 50,000+ firm 10-Ks from , and extract product descriptions Web crawling based in PERL, SEC Edgar website. APL based text parsing and similarity processing Clean the text (common words or vertical relations - used in or supplied to ) 10-K Item 1: Business Description 10-K Has many sections, Business Desc. is just one Business Description is usually Item 1 or Item 1A and can be many pages long. Our study focuses on text in the Business Description Section. Form text-based industry groups based on product words.

13 BEA vocabulary Example Photographic and Photocopying Equipment Still cameras (hand-type cameras, process cameras for photoengraving and photolithography, and other still cameras) Projectors, except rear screen viewers 72.4 Still picture commercial-type processing equipment for film 40.5 All other still picture commercial type processing equipment 18.3 Other still picture equipment, parts, attachments, and accessories (excluding projection screens) Photocopying equipment, including diffusion transfer, dye transfer, electrostatic, light and heat sensitive types, etc Microfilming, blueprinting, and white-printing equipment 20.7 Motion picture equipment (all sizes 8 mm and greater), excluding projection screens and processing equipment Projection screens (for motion picture and/or still projection) Motion picture processing equipment, all types, excluding motion picture still type equipment and interchangeable types 23.0 Provides a direct link to firm products using 10-K text Overlooked resources

14 Text-based approach (I) (i) Input-Output Table (Bureau of Economic Analysis BEA) Semiconductor and related device manufacturing (# %) Coated and laminated paper, packaging paper and plastics film manufacturing (#32222A 1.4%) Paperboard container manufacturing (# %) Photographic and photocopying equipment manufacturing Support activities for printing (# %) Electronic and precision equipment repair and maintenance (#33331A 0.2%) Vending, commercial, industrial, and office machinery manufacturing (# %)

15 Text-based approach (II) (ii) Commodity vocabulary (BEA) - NEW laminate film gelatin plastic semiconductor resin paper wax Coated and laminated paper, packaging paper and plastics film manufacturing (#32222A 1.4%) projector photocopy Screen picture microfilm Blueprinting Photographic and photocopying equipment manufacturing edition periodical lythography books library binding edition Support activities for printing (# %)

16 Text-based approach (III) (iv) Firm-pairs vertical network (text-based) Vertical relatedness between all pairs (and years) Different vertical intensities between pairs Each firm has customized vertical peers (unlike existing network) Upstream and downstream links We consider two level of granularity 10% and 1% (Text-10% and Text-1%)

17 Text-based approach (IV) (iv) Firm-level measure of Vertical Integration More VI if a firm offers products & services at different stages of the chain chain (e.g firm 2) Measure the intensity of VI at the firm level Changes over time as the firm changes product offerings

18 Key innovation: crossing BEA and 10K vocabularies Triple Product BVB is central to our identification M (~5000) Firms B X V X C x C BEA Matrix V [Only BEA data] M (~5000) Firms is a C x M matrix C (~400) BEA Commodities Matrix B : Cosine Similarities Firm 10-Ks and BEA Commodity Text Overall, B V B is an M x M directional vertical relatedness network

19 Plan 1. Introduction - Motivation 2. Measuring Vertical Relatedness 3. Properties of Vertical Network 4. Vertical Acquisitions 5. Vertical Integration

20 Vertical Integration distribution Vertical integration is at the product level. Can have multiple products that span the supply chain. Supply chain is (highly) fragmented

21 Vertical Integration over time Clear trend away from VI (-15% in 12 years) Great Diversification and Technology Orientation of the economy

22 VI An industry example: Network equipment Firms: Cisco, Broadcom, Citrix, Juniper, Novell, Sycamore and Utstarcom 2014 IBISWORLD industry report: players in this industry seek to offer end-to-end and all-in-one solutions.

23 Properties of the Vertical Networks Network: Text-10% Text-1% NAICS-10% NAICS-1% TNIC Panel A: Granularity and Overlap Granularity 10% 1% 9.48% 1.37% 2.33% % of pairs in TNIC 1.33% 2.39% 2.67% 2.89% 100% % of pairs in the same SIC 0.74% 1.03% 0.35% 0.20% 38.10% % of pairs in the same NAICS 0.59% 0.66% 0.30% 0.18% 38.11% % of pairs in the same SIC or NAICS 0.81% 1.09% 0.35% 0.20% 41.24% % of pairs in Text-10% 100% 100% 10.59% 12.90% 4.80% % of pairs in Text-1% 10% 100% 1.10% 1.12% 1.00% % of pairs that include a financial firm 9.20% 1.80% 48.44% 34.31% 58.72% % of (no fin.) pairs in Text-10% 100% 100% 19.90% 19.29% 11.63% % of (no fin.) pairs in Text-1% 10% 100% 2.14% 1.71% 2.44% Text and NAICS: low overlap with horizontal network NAICS: contains a lot of financial firms (48% and 34% of pairs)

24 Validity Test Explicit VI Search each 10K for vertical integration or vertically integrated Create dummy variable VI search Predict VI search with our new measure VI Dep. Variable: Prob(VI search = 1) (1) (2) (3) (4) (5) (6) VI a a a a (0.013) (0.013) (0.019) (0.019) VI segment a a a a (0.012) (0.012) (0.014) (0.014) Year FE No No No Yes Yes Yes Industry FE No No No Yes Yes Yes Obs. 45,198 45,198 45,198 45,198 45,198 45,198 Pseudo R

25 Validity Test Trade credit correlation Vertically related firms: AR of firm i AP of firm j Negative correlation between AR i and AP j Run the following regression for all pairs i, j: ( AR AP) i,t = α + γ( AR AP) j,t + η t + ɛ i,t Network: Text-10% Text-1% NAICS-10% NAICS-1% TNIC Granularity 10% 1% 9.48% 1.37% 2.33% Panel B: Validity Test (Trade Credit Shocks) γ (unconditional) (t-statistic) (-3.37) (-4.57) (-0.93) (-0.03) (15.91) γ (if AR i,t > AP i,t ) (t-statistic) (-2.40) (-3.71) (-0.90) (-0.92) (12.37) γ (if AR j,t < AP j,t ) (t-statistic) (-2.47) (-3.83) (-0.051) (-0.06) (8.38)

26 Plan 1. Introduction - Motivation 2. Measuring Vertical Relatedness 3. Properties of Vertical Network 4. Vertical Acquisitions 5. Vertical Integration

27 Vertical Acquisitions - Sample Description Measure: All Text-based (10%) NAICS-based (10%) Vertical Non-Vertical Vertical Non-Vertical #Transactions 3,460 1,368 2, ,000 % Vertical 39% 61% 13% 87% #Upstream #Downstram CAR(0) 0.49% 0.65% 0.38% a 0.46% 0.49% CAR(-1+1) 0.86% 0.97% 0.79% 0.55% 0.91% #Transactions 3,256 1,301 1, ,829 Note: Restricted to deals where acquirers and targets are public

28 Vertical Acquisitions: Actual and Matched Targets Why Targets? Party that loses control right! Variable: Industry(R&D/ R&D/ Industry(#Patents/ #Patents/ sale) sales Assets) Assets Panel A: Whole Sample Vert. Targets Non-Vert. Targets Non-Merging Firms Panel B: Matched Targets I (i) Vert. Targets (ii) Matched Vert. Targets t-statistic [(i)-(ii)] (-9.64) a (-4.01) a (0.94) (3.44) a (i) Non-Vert. Targets (ii) Matched Non-Vert. Targets t-statistic [(i)-(ii)] (4.15) a (2.66) a (-0.62) (-0.52)

29 Vertical Targets: Event-time evidence Solid line: Vertical Targets - Dashed line: Non-Vertical Targets Note: Averages across all Vertical and non-vertical targets

30 Determinants of Vertical Acquisition Specification: Pr(Target i,j,t ) = η t + βx i,j,t + ε i,j,t Consider both vertical and non-vertical transactions cross-sectional source of variation Focus on Industry R&D/Sales and #Patents/Assets as main regressors Use Text-based Network Industry Classification (TNIC) for more precision Mitigate endogeneity concerns Incentives for VI depends on product market specificity. Include maturity proxies

31 The Determinants of Vertical Targets: Text Based Dep. Variable: Prob(Target) Deal Type Vertical Non-Vertical Vertical Non-Vertical Ind.(R&D/sales) a a a a (0.055) (0.031) (0.082) (0.045) Ind.(#Patents/assets) a a a (0.030) (0.039) (0.034) (0.047) Ind.(R&D/sales) Ind.(#Patents/Assets) a (0.034) (0.026) log(assets) a a a a (0.037) (0.029) (0.037) (0.029) log(age) a a (0.036) (0.026) (0.036) (0.026) MB a b a b (0.052) (0.029) (0.052) (0.029) Other controls Yes Yes Yes Yes #Obs. 45,198 45,198 45,198 45,198 Pseudo R Note: logistic regressions (unit var.). Standard errors clustered by industry. NAICS

32 Instrumental Variable Results State R&D tax credit as instrument for R&D Used by Bloom, Schankerman, and Van Reenen (2013) Isolate variation in R&D purely driven by tax rules Dep. Variable: Prob(Target) 1st-Stage Vertical Non-Vertical R&D tax credit a (0.483) Ind.(R&D/sales) b (0.084) Ind.(#Patents/assets) a a (0.032) (0.039) (0.039) Other controls Yes Yes Yes #Obs. 45,198 45,198 45,198 R Note: IV probit (unit var.). Standard errors clustered by industry.

33 Patent vs Secrecy Patents are not always the best protection of innovation Cohen, Nelson, and Walsh (2000): secrecy as protection Carnegie-Mellon Survey (CMS) to measure importance of secrecy across manufacturing industries Dep. Variable: Prob(Vertical Target) Specification: Logit IV Probit Low High Low High (1) (2) (3) (4) Ind.(R&D/sales) a b (0.094) (0.094) (0.133) (0.171) Ind.(#Patents/assets) a b (0.050) (0.070) (0.066) (0.062) Other Controls Yes Yes Yes Yes #obs. 9,409 9,333 9,409 9,333 Pseudo R NA NA

34 Plan 1. Introduction - Motivation 2. Measuring Vertical Relatedness 3. Properties of Vertical Network 4. Vertical Acquisitions 5. Vertical Integration

35 Determinants of Vertical Integration Panel data specification: VI i,t = α i + η t + βx i,t + ε i,t Degree of Vertical Integration for each firm-year observation Within-industry and Within-firm source of variation Focus on Industry R&D/Sales and #Patents/Assets as main regressors Use Text-based Network Industry Classification (TNIC) for more precision Mitigate endogeneity concerns (beyond α i ) Incentives for VI depends on product market specificity. Include maturity proxies

36 The Determinants of Vertical Integration: Text Based Dep. Variable: (Text-based) VI Specification: OLS-FE Instrumental Variables Baseline Interaction 1st Stage 2nd Stage (1) (2) (3) (4) (5) (6) Ind.(R&D/sales) a a a c a (0.007) (0.005) (0.008) (-0.006) (0.034) Ind.(#Patents/assets) a a a a a a (0.007) (0.004) (0.009) (0.006) (0.011) (0.011) Ind.(R&D) Ind.(#Patents) a c State R&D Tax Credit (0.004) (0.002) a (0.238) Other Controls Yes Yes Yes Yes Yes Yes Fixed Effects Ind. Firm Ind. Firm Ind. Ind. #obs. 45,198 45,198 45,198 45,198 45,198 45,198 Adj. R Note: OLS regressions using cont. independent variables (unit var.). Standard errors clustered by industry & year. NAICS

37 Patent vs Secrecy Dep. Variable: (Text-based) VI Sub-Sample: Secrecy Specification: OLS-FE IV Low High Low High Low High (1) (2) (3) (4) (5) (6) Ind.(R&D/sales) a a a a a (0.015) (0.016) (0.013) (0.013) (0.076) (0.081) Ind.(#Patents/assets) a a a b (0.014) (0.010) (0.009) (0.008) (0.022) (0.018) Other Controls Yes Yes Yes Yes Yes Yes Fixed Effects Ind. Ind. Firm Firm Ind. Ind. #obs. 9,409 9,333 9,409 9,333 9,409 9,333 Adj. R Note: OLS regressions using cont. independent variables (unit var.). Standard errors clustered by industry & year.

38 Conclusions We develop a new way to measure Vertical Integration Measurement with computational text linguistics More accurately reflects actual integration by firms Findings: We show that vertical organization are impacted by R&D and Patenting - shows the importance of intangible assets. High R&D industries => Less targeted in vertical deals, and less VI High patenting => More targeted in vertical deals, and more VI Support for Incentives / Property Rights Theories of the firm Our vertical network offers many new research possibilities

39 The Determinants of Vertical Targets: NAICS Dep. Variable: Prob(Target) Deal Type Vertical Non-Vertical Vertical Non-Vertical Ind.(R&D/sales) a a a (0.068) (0.028) (0.092) (0.038) Ind.(#Patents/assets) b a a (0.058) (0.029) (0.069) (0.035) Ind.(R&D/sales) Ind.(#Patents/Assets) b a (0.054) (0.022) Other controls Yes Yes Yes Yes #Obs. 45,198 45,198 45,198 45,198 Pseudo R Note: logistic regressions (unit var.). Standard errors clustered by industry. back

40 The Determinants of Vertical Integration: NAICS/Segment Dep. Variable: VI segment Deal Type (1) (2) (3) (4) Ind.(R&D/sales) a (0.031) (0.017) (0.032) (0.018) Ind.(#Patents/assets) a a (0.029) (0.015) (0.038) (0.018) Ind.(R&D/sales) Ind.(#Patents/Assets) b (0.017) (0.008) Other controls Yes Yes Yes Yes Fixed Effects Ind. Firm Ind. Firm #Obs. 45,198 45,198 45,198 45,198 Adj. R Note: OLS regressions using cont. independent variables (unit var.). Standard errors clustered by industry & year. back