Patent Trolls and Startup Employment

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1 Patent Trolls and Startup Employment Ian Appel, Joan Farre-Mensa, and Elena Simintzi INTERNET APPENDIX 1

2 Figure IA.1: Sample Demand Letter The following is an example of a patent demand letter sent by Lodsys LLC, a non-practicing entity, in The letter has been redacted to remove the name and address of the recipient. (Source: 2

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5 Table IA.1: Signing Dates of State Anti-Troll Laws This table lists the 32 states with anti-troll laws in our sample period along with corresponding signing dates. Connecticut and Michigan also adopted laws in 2017 after the end of our sample. State Law Signed AL 4/2/2014 AZ 3/24/2016 CO 6/5/2015 FL 6/2/2015 GA 4/15/2014 ID 3/26/2014 IL 8/26/2014 IN 5/5/2015 KS 5/20/2015 LA 5/28/2014 ME 4/14/2014 MD 5/5/2014 MN 4/29/2016 MS 3/28/2015 MO 7/8/2014 MT 4/2/2015 NH 7/11/2014 NC 8/6/2014 ND 3/26/2015 OK 5/16/2014 OR 3/3/2014 RI 6/4/2016 SC 6/9/2016 SD 3/26/2014 TN 5/1/2014 TX 6/17/2015 UT 4/1/2014 VT 5/22/2013 VA 5/23/2014 WA 4/25/2015 WI 4/24/2014 WY 3/11/2016 5

6 Table IA.2: Other State Initiatives This table lists other state laws and programs aimed at promoting employment at high-tech startups during our sample period. We identify such measures using the Council for Community and Economic Research State Business Incentive (CCERSBI) database. We specifically focus on programs targeting the information (NAICS 51) and Professional, Scientific, and Professional Services (NAICS 54) sectors, as well as those that involve equity investments (across any industry). If a start date for the program is not provided, we attempt to identify this information through internet searches. All programs identified from this database are assumed to start in the first quarter of the year and last through the end of the sample. We supplement the programs detailed above with searches for the passage of state legislation aimed at promoting entrepreneurship using our own searches of the Legiscan database. We find that intrastate crowdfunding laws, intended to promote startup creation, were adopted by a number of states during our sample period, and append those to the list of collected state business incentive programs. State Law/Program Date AK 49th State Angel Fund (49SAF) 1/1/2012 AL Alabama Innovation Fund 1/1/2012 AL Crowdfunding Law 4/9/2014 AZ Computer Data Center Program 1/1/2013 AZ Crowdfunding Law 4/1/2015 CO Advanced Industries Accelerator Programs 1/1/2013 CO Crowdfunding Law 4/13/2015 CT Service and Manufacturing Facilities Tax Credit 1/1/2012 CT Connecticut Bioscience Innovation Fund (CBIF) 1/1/2013 CT Regenerative Medicine Research Fund 1/1/2014 FL Crowdfunding Law 6/16/2015 IA Crowdfunding Law 7/2/2015 ID Crowdfunding Law 1/20/2012 ID Idaho Opportunity Fund 1/1/2013 IL Crowdfunding Law 7/29/2015 IN Crowdfunding Law 3/25/2014 KY Crowdfunding Law 3/19/2015 MA AmplifyMass 1/1/2014 MA DeployMass 1/1/2014 MA Crowdfunding Law 1/15/2015 MD Maryland Venture Fund (MVF) 1/1/2012 MD Propel Baltimore Fund 1/1/2012 MD Veterans Opportunity Fund (VOF) 1/1/2012 MD Cybersecurity Investment Incentive Tax Credit (CIITC) 1/1/2013 MD Cyber Security Investment Fund (CIF) 1/1/2014 MD Maryland E-Nnovation Initiative Fund (MEIF) 1/1/2015 6

7 Table IA.2 Cont. State Law/Program Date MD Crowdfunding Law 5/16/2016 ME Maine Economic Development VC Investment Program 1/1/2012 ME Crowdfunding Law 3/2/2014 MI Crowdfunding Law 12/30/2013 MN Crowdfunding Law 6/15/2015 MS Crowdfunding Law 2/9/2015 MT Crowdfunding Law 4/1/2015 ND Research ND 1/1/2013 NE Crowdfunding Law 5/27/2015 NJ Angel Investor Tax Credit Program 1/1/2013 NJ Crowdfunding Law 11/9/2015 NJ Opportunity Partnership Grants 1/1/2016 NJ Skills Partnership/Customized Training Grant 1/1/2016 NY Innovate NY Fund 1/1/2012 NY Start-up New York 1/1/2014 OR Crowdfunding Law 10/15/2015 RI Industry Cluster Grant 1/1/2015 SC Technology Intensive Facility Sales Tax Exemption 1/1/2013 SC Crowdfunding Law 6/26/2015 TN Crowdfunding Law 5/19/2014 TX Jobs for Texas 1/1/2013 TX Franchise Tax Credit for Qualified R&D Activities 1/1/2014 TX Crowdfunding Law 10/22/2014 VA Virginia Biosciences Health Research Grants 1/1/2013 VA Crowdfunding Law 3/23/2015 VT Crowdfunding Law 6/16/2014 WA Crowdfunding Law 3/28/2014 WA Data Center Tax Exemption 1/1/2015 WI Crowdfunding Law 11/7/2013 WV Crowdfunding Law 3/15/2016 WY Seed Capital Network Program 1/1/2012 WY Crowdfunding Law 3/3/2016 7

8 Table IA.3: Non-High-Tech Employment by Age This table reports the effect of state anti-troll laws on non-high-tech employment at firms of different ages. The dependent variable is the natural logarithm of employment in the high-tech sector for firms with ages of 0 3, 4 10, and 11+ years. Anti-Troll Law is an indicator equal to one if a state has passed anti-troll legislation in or before that quarter. The State Controls include the quarterly real GSP growth rate, the natural logarithm of income per capita, the unemployment rate, and the natural logarithm of granted patents in the state (all lagged) as well as an indicator for other state initiatives aimed at promoting high-tech startup employment. Each observation is a state-quarter. All specifications include state and year-quarter fixed effects. Robust standard errors are clustered by state. We use ***, **, and * to denote significance at the 1%, 5%, and 10% level (two-sided), respectively. Dep. Var.= Ln(Non-High-Tech Employment) Firm Age = 0 3 years 4 10 years 11+ years (1) (2) (3) (4) (5) (6) Anti-Troll Law (0.0128) (0.0111) (0.0106) (0.0097) (0.0059) (0.0054) State Controls no yes no yes no yes State FE yes yes yes yes yes yes Year-Quarter FE yes yes yes yes yes yes No. Observations 1,036 1,036 1,036 1,036 1,036 1,036 Within R-Squared

9 Table IA.4: High-Tech Employment by Firm Size This table reports the effect of state anti-troll laws on high-tech employment at firms of different sizes. The dependent variable is the natural logarithm of employment in the high-tech sector for firms with ages of 0 19, 20 49, and 50+ employees. Anti-Troll Law is an indicator equal to one if a state has passed anti-troll legislation in or before that quarter. The State Controls include the quarterly real GSP growth rate, the natural logarithm of income per capita, the unemployment rate, and the natural logarithm of granted patents in the state (all lagged) as well as an indicator for other state initiatives aimed at promoting high-tech startup employment. Each observation is a state-quarter. All specifications include state and year-quarter fixed effects. Robust standard errors are clustered by state. We use ***, **, and * to denote significance at the 1%, 5%, and 10% level (two-sided), respectively. Dep. Var.= Ln(High-Tech Employment) Firm Size = 0 19 employees employees 50+ employees (1) (2) (3) (4) (5) (6) Anti-Troll Law * ** (0.0101) (0.0088) (0.0127) (0.0116) (0.0103) (0.0098) State Controls no yes no yes no yes State FE yes yes yes yes yes yes Year-Quarter FE yes yes yes yes yes yes No. Observations 1,036 1,036 1,036 1,036 1,036 1,036 Within R-Squared

10 Table IA.5: Controlling for Geographic Effects The dependent variable in this table is the logarithm of employment at high-tech startups (0 3 years of age). Anti-Troll Law is an indicator equal to one if a state has passed anti-troll legislation in or before that quarter. Columns 1 and 2 estimate equation (1) replacing year-quarter fixed effects with year-quarter 4-Census-region and year-quarter 9-Census-division fixed effects, respectively. In column 3, Anti-Troll Law Neighbor is a placebo indicator set equal to one if the state has not passed an anti-troll law but at least one neighboring state has. The State Controls include the quarterly real GSP growth rate, the natural logarithm of income per capita, the unemployment rate, and the natural logarithm of granted patents in the state (all lagged) as well as an indicator for other state initiatives aimed at promoting high-tech startup employment. Each observation is a state-quarter. Robust standard errors are clustered by state. We use ***, **, and * to denote significance at the 1%, 5%, and 10% level (two-sided), respectively. Dep. Var. = Ln(High-Tech Startup Employment) Controlling for Local Shocks Neighbor States (1) (2) (3) Anti-Troll Law * * * (0.0242) (0.0246) (0.0286) Anti-Troll Law Neighbor (0.0248) State Controls yes yes yes State FE yes yes yes Year-Quarter FE no no yes Year-Quarter Division FE no yes no Year-Quarter Region FE yes no no No. Observations 1,036 1,036 1,036 Within R-Squared

11 Table IA.6: Quantile Regressions This table reports quantile regressions for the effect of state anti-troll laws on employment at high tech startups (0 3 years of age). Anti-Troll Law is an indicator equal to one if a state has passed anti-troll legislation in or before that quarter. The State Controls include the quarterly real GSP growth rate, the natural logarithm of income per capita, the unemployment rate, and the natural logarithm of granted patents in the state (all lagged) as well as an indicator for other state initiatives aimed at promoting high-tech startup employment. Each observation is a state-quarter. All specifications include state and year-quarter fixed effects. Robust standard errors are clustered by state. We use ***, **, and * to denote significance at the 1%, 5%, and 10% level (two-sided), respectively. Dep. Var. = Ln(High-Tech Startup Employment) 75th Percentile 50th Percentile 25th Percentile (1) (2) (3) Anti-Troll Law ** * (0.0219) (0.0200) (0.0222) State Controls yes yes yes State FE yes yes yes Year-Quarter FE yes yes yes No. Observations 1,036 1,036 1,036 11

12 Table IA.7: Employee Education Level The dependent variable in this table is the natural logarithm of employment at IT high-tech startups (0-3 years of age). In columns 1-2, we report the employment change for workers with at least some college education and in columns 3-4 we report the employment change for those with no college education. Anti-Troll Law is an indicator equal to one if a state has passed anti-troll legislation in or before that quarter. The State Controls include the quarterly real GSP growth rate, the natural logarithm of income per capita, the unemployment rate, and the natural logarithm of granted patents in the state (all lagged) as well as an indicator for other state initiatives aimed at promoting high-tech startup employment. Each observation is a statequarter. All specifications include state and year-quarter fixed effects. Robust standard errors are clustered by state. We use ***, **, and * to denote significance at the 1%, 5%, and 10% level (two-sided), respectively. Dep. Var.= Ln(IT Startup Employment) Some College Education No College Education (1) (2) (3) (4) Anti-Troll Law * * (0.0251) (0.0248) (0.0321) (0.0313) State Controls no yes no yes State FE yes yes yes yes Year-Quarter FE yes yes yes yes No. Observations 1,036 1,036 1,036 1,036 Within R-Squared

13 Table IA.8: Patent Applications Large Firms This table reports the effect of state anti-troll laws on the number of patent applications at the state level. The dependent variable is the natural logarithm of one plus the number of applications for utility patents for all IT patents and the subset of IT patents that are softwarerelated. Industry classifications are from Chung et al. (2014). The sample consists of large firms (i.e., 500+ employees) as classified by the USPTO. Anti-Troll Law is an indicator equal to one if a state has passed anti-troll legislation in or before that quarter. The State Controls include the quarterly real GSP growth rate, the natural logarithm of income per capita, and the unemployment rate in the state (all lagged) as well as an indicator for other state initiatives aimed at promoting high-tech startup employment. Each observation is a state-quarter. All specifications include state and year-quarter fixed effects. Robust standard errors are clustered by state. We use ***, **, and * to denote significance at the 1%, 5%, and 10% level (two-sided), respectively. Dep. Var. = Ln(1 + # Firms Pledging Patents), Large Firms IT Software (1) (2) (3) (4) Anti-Troll Law (0.0510) (0.0486) (0.0548) (0.0531) State Controls no yes no yes State FE yes yes yes yes Year-Quarter FE yes yes yes yes No. Observations 1,000 1, Within R-Squared

14 Table IA.9: Identification Tests Patent Applications The dependent variable is the natural logarithm of one plus the number of applications for software patents. The sample consists of small firms and individual inventors as classified by the USPTO. Column 1 appends the baseline specification with indicators for the 4 quarters prior to the adoption of the anti-troll law. Column 2 estimates the baseline specification in a matched sample (described in text). In column 3, Anti-Troll Law t-12 is a placebo indicator that equals one starting 12 quarters prior to a state passing anti-troll legislation. Column 4 excludes California and Massachusetts from the analysis. Column 5 reports weighted OLS regressions with weights based on the natural logarithm of the number of software patent applications in each state for 2010 and The State Controls include the quarterly real GSP growth rate, the natural logarithm of income per capita, and the unemployment rate in the state (all lagged) as well as an indicator for other state initiatives aimed at promoting high-tech startup employment. Each observation is a state-quarter. All specifications include state and year-quarter fixed effects. Robust standard errors are clustered by state. We use ***, **, and * to denote significance at the 1%, 5%, and 10% level (two-sided), respectively. Dep. Var. = Ln(1 + # Software Patent Applications) Coefficient Matched Placebo Excluding Weighted Trend Sample Timing CA+MA Regression (1) (2) (3) (4) (5) Anti-Troll Law 0.141** 0.137** 0.110** * (0.0663) (0.0511) (0.0502) (0.0425) Anti-Troll Law t (0.0638) Anti-Troll Law t (0.0745) Anti-Troll Law t (0.0696) Anti-Troll Law t (0.0723) Anti-Troll Law t (0.0503) State Controls yes yes yes yes yes State FE yes yes yes yes yes Year-Quarter FE yes yes yes yes yes No. Observations ,280 1, Within R-Squared

15 Table IA.10: Patent Citations This table examines the effect of state anti-troll laws on patent quality. In columns 1 and 2, the dependent variable is the natural logarithm of one plus the mean number of citations received by granted software patent applications filed in each state-quarter. In columns 3 and 4, the dependent variable is the natural logarithm of one plus the 75th percentile of the number of citations received by granted software patent applications filed in each state-quarter. Data on patent citations are from the USPTO s PatentsView database. The sample consists of small firms and individual inventors as classified by the USPTO (patents with inventors from multiple states are excluded as startups are unlikely to have operations in multiple states). Anti-Troll Law is an indicator equal to one if a state has passed anti-troll legislation in or before that quarter. The State Controls include the quarterly real GSP growth rate, the natural logarithm of income per capita, and the unemployment rate in the state (all lagged) as well as an indicator for other state initiatives aimed at promoting high-tech startup employment. Each observation is a state-quarter; state-quarters without at least one granted software patent application with citation information are excluded from the analysis. All specifications include state and year-quarter fixed effects. Robust standard errors are clustered by state. We use ***, **, and * to denote significance at the 1%, 5%, and 10% level (two-sided), respectively. Dep. Var. = Ln(1 + # Patent Citations) Mean 75th Percentile (1) (2) (3) (4) Anti-Troll Law ** 0.220** (0.0746) (0.0744) (0.0868) (0.0899) State Controls no yes no yes State FE yes yes yes yes Year-Quarter FE yes yes yes yes No. Observations Within R-Squared

16 Table IA.11: Dollar Amount of VC Funding The dependent variable is the natural logarithm of one plus the dollar amount of VC funding in a state. The sample includes VC investments at the startup/seed, early stage, or expansion phase for firms founded in 2005 or later. Columns 1-2 report results for all sectors, while columns 3 4 and 5 6 report results for IT and non-it sectors, respectively. The sample consists of states with at least one VC investment at the start of the sample in 2011Q2. Anti-Troll Law is an indicator equal to one if a state has passed anti-troll legislation in or before that quarter. The State Controls include the quarterly real GSP growth rate, the natural logarithm of income per capita, the unemployment rate, and the natural logarithm of granted patents in the state (all lagged) as well as an indicator for other state initiatives aimed at promoting high-tech startup employment. Each observation is a state-quarter. All specifications include state and year-quarter fixed effects. Robust standard errors are clustered by state. We use ***, **, and * to denote significance at the 1%, 5%, and 10% level (two-sided), respectively. Dep. Var. = Ln(1 + $ VC Funding), 2011 VC>0 Sector = All Sectors IT Non-IT (1) (2) (3) (4) (5) (6) Anti-Troll Law 0.267** 0.241** 0.273** 0.232* (0.125) (0.112) (0.122) (0.121) (0.161) (0.149) State Controls no yes no yes no yes State FE yes yes yes yes yes yes Year-Quarter FE yes yes yes yes yes yes No. Observations Within R-Squared

17 Table IA.12: Identification Tests Venture Capital The dependent variable is the natural logarithm of one plus the number of unique IT firms raising VC funding in a state. The sample consists of states with at least one VC investment at the start of the sample in 2011Q2, and includes VC investments at the startup/seed, early stage, or expansion phase for firms founded in 2005 or later. Column 1 appends the baseline specification with indicators for the 4 quarters prior to the adoption of the anti-troll law. Column 2 estimates the baseline specification in a matched sample (described in text). In column 3, Anti- Troll Law t-12 is a placebo indicator that equals one starting 12 quarters prior to a state passing anti-troll legislation. Column 4 excludes California and Massachusetts from the analysis. Column 5 reports weighted OLS regressions with weights based on the natural logarithm of the number of unique firms raising VC in each state for 2010 and The State Controls include the quarterly real GSP growth rate, the natural logarithm of income per capita, the unemployment rate, and the natural logarithm of granted patents in the state (all lagged) as well as an indicator for other state initiatives aimed at promoting high-tech startup employment. Each observation is a state-quarter. All specifications include state and year-quarter fixed effects. Robust standard errors are clustered by state. We use ***, **, and * to denote significance at the 1%, 5%, and 10% level (two-sided), respectively. Dep. Var. = Ln(1 + # Firms Receiving VC), 2011 VC > 0 Coefficient Matched Placebo Excluding Weighted Trend Sample Timing CA+MA Regression (1) (2) (3) (4) (5) Anti-Troll Law 0.185** 0.101* 0.163*** 0.152*** (0.0750) (0.0545) (0.0537) (0.0528) Anti-Troll Law t (0.0962) Anti-Troll Law t (0.0871) Anti-Troll Law t (0.106) Anti-Troll Law t (0.0970) Anti-Troll Law t (0.101) State Controls yes yes yes yes yes State FE yes yes yes yes yes Year-Quarter FE yes yes yes yes yes No. Observations Within R-Squared

18 Table IA.13: Identification Tests Patents Pledged as Collateral The dependent variable is the natural logarithm of one plus the number of software patents pledged as collateral. The sample consists of small firms and individual inventors as classified by the USPTO. Column 1 appends the baseline specification with indicators for the 4 quarters prior to the adoption of the anti-troll law. Column 2 estimates the baseline specification in a matched sample (described in text). In column 3, Anti-Troll Law t-12 is a placebo indicator that equals one starting 12 quarters prior to a state passing anti-troll legislation. Column 4 excludes California and Massachusetts from the analysis. Column 5 reports weighted OLS regressions with weights based on the natural logarithm of the number of software patents pledged in each state for 2010 and The State Controls include the quarterly real GSP growth rate, the natural logarithm of income per capita, and the unemployment rate in the state (all lagged) as well as an indicator for other state initiatives aimed at promoting high-tech startup employment. Each observation is a state-quarter. All specifications include state and year-quarter fixed effects. Robust standard errors are clustered by state. We use ***, **, and * to denote significance at the 1%, 5%, and 10% level (two-sided), respectively. Dep. Var. = Ln(1 + # Firms Pledging Patents) Coefficient Matched Placebo Excluding Weighted Trend Sample Timing CA+MA Regression (1) (2) (3) (4) (5) Anti-Troll Law 0.183** 0.221*** 0.168** 0.173* (0.0807) (0.0645) (0.068) (0.101) Anti-Troll Law t (0.109) Anti-Troll Law t (0.106) Anti-Troll Law t (0.0960) Anti-Troll Law t (0.103) Anti-Troll Law t (0.0860) State Controls yes yes yes yes yes State FE yes yes yes yes yes Year-Quarter FE yes yes yes yes yes No. Observations 1,000 1,280 1, Within R-Squared

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