patent trolls and startup employmentjfe.rochester.edu/appel_farre-mensa_simintzi_app.pdffl...

Patent Trolls and Startup EmploymentIan Appel, Joan Farre-Mensa, and Elena Simintzi

INTERNET APPENDIX

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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 2011. The letter has been redacted to remove the name and address of the recipient. (Source:https://trollingeffects.org/letters.)

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https://trollingeffects.org/letters

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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 correspondingsigning 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

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Table IA.2: Other State Initiatives

This table lists other state laws and programs aimed at promoting employment at high-techstartups during our sample period. We identify such measures using the Council for Communityand Economic Research State Business Incentive (CCERSBI) database. We specifically focuson programs targeting the information (NAICS 51) and Professional, Scientific, and ProfessionalServices (NAICS 54) sectors, as well as those that involve equity investments (across any indus-try). If a start date for the program is not provided, we attempt to identify this informationthrough internet searches. All programs identified from this database are assumed to start in thefirst quarter of the year and last through the end of the sample. We supplement the programsdetailed above with searches for the passage of state legislation aimed at promoting entrepreneur-ship 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 sampleperiod, 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

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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

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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 ofdifferent ages. The dependent variable is the natural logarithm of employment in the high-techsector for firms with ages of 0–3, 4–10, and 11+ years. Anti-Troll Law is an indicator equalto one if a state has passed anti-troll legislation in or before that quarter. The State Controlsinclude the quarterly real GSP growth rate, the natural logarithm of income per capita, theunemployment rate, and the natural logarithm of granted patents in the state (all lagged) aswell 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 atthe 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 0.0014 0.0048 -0.0002 0.0020 0.0084 0.0090

(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 0.000 0.113 0.000 0.096 0.011 0.066

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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 differentsizes. The dependent variable is the natural logarithm of employment in the high-tech sector forfirms with ages of 0–19, 20–49, and 50+ employees. Anti-Troll Law is an indicator equal to one ifa state has passed anti-troll legislation in or before that quarter. The State Controls include thequarterly real GSP growth rate, the natural logarithm of income per capita, the unemploymentrate, and the natural logarithm of granted patents in the state (all lagged) as well as an indicatorfor other state initiatives aimed at promoting high-tech startup employment. Each observation isa state-quarter. All specifications include state and year-quarter fixed effects. Robust standarderrors are clustered by state. We use ***, **, and * to denote significance at the 1%, 5%, and10% level (two-sided), respectively.

Dep. Var.= Ln(High-Tech Employment)

Firm Size = 0–19 employees 20–49 employees 50+ employees

(1) (2) (3) (4) (5) (6)

Anti-Troll Law 0.0187* 0.0209** 0.0040 0.0070 0.0081 0.0093

(0.0101) (0.0088) (0.0127) (0.0116) (0.0103) (0.0098)




No. Observations 1,036 1,036 1,036 1,036 1,036 1,036

Within R-Squared 0.022 0.127 0.001 0.090 0.004 0.084

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Table IA.5: Controlling for Geographic Effects

The dependent variable in this table is the logarithm of employment at high-tech startups (0–3years of age). Anti-Troll Law is an indicator equal to one if a state has passed anti-troll legislationin or before that quarter. Columns 1 and 2 estimate equation (1) replacing year-quarter fixedeffects 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 thestate has not passed an anti-troll law but at least one neighboring state has. The State Controlsinclude the quarterly real GSP growth rate, the natural logarithm of income per capita, theunemployment rate, and the natural logarithm of granted patents in the state (all lagged) aswell 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 Neighbor

Local Shocks States

(1) (2) (3)

Anti-Troll Law 0.0483* 0.0462* 0.0498*

(0.0242) (0.0246) (0.0286)

Anti-Troll Law Neighbor 0.0012

(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 0.098 0.109 0.092

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Table IA.6: Quantile Regressions

This table reports quantile regressions for the effect of state anti-troll laws on employment athigh tech startups (0–3 years of age). Anti-Troll Law is an indicator equal to one if a state haspassed anti-troll legislation in or before that quarter. The State Controls include the quarterlyreal GSP growth rate, the natural logarithm of income per capita, the unemployment rate, andthe natural logarithm of granted patents in the state (all lagged) as well as an indicator forother state initiatives aimed at promoting high-tech startup employment. Each observation isa state-quarter. All specifications include state and year-quarter fixed effects. Robust standarderrors are clustered by state. We use ***, **, and * to denote significance at the 1%, 5%, and10% level (two-sided), respectively.

Dep. Var. = Ln(High-Tech Startup Employment)

75th Percentile 50th Percentile 25th Percentile

(1) (2) (3)

Anti-Troll Law 0.0466** 0.0359* 0.0356

(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

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Table IA.7: Employee Education Level

The dependent variable in this table is the natural logarithm of employment at IT high-techstartups (0-3 years of age). In columns 1-2, we report the employment change for workers withat least some college education and in columns 3-4 we report the employment change for thosewith no college education. Anti-Troll Law is an indicator equal to one if a state has passedanti-troll legislation in or before that quarter. The State Controls include the quarterly realGSP growth rate, the natural logarithm of income per capita, the unemployment rate, and thenatural logarithm of granted patents in the state (all lagged) as well as an indicator for otherstate 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 errorsare 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 0.0450* 0.0450* 0.0475 0.0477

(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 0.018 0.027 0.015 0.037

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Table IA.8: Patent Applications – Large Firms

This table reports the effect of state anti-troll laws on the number of patent applications atthe state level. The dependent variable is the natural logarithm of one plus the number ofapplications for utility patents for all IT patents and the subset of IT patents that are software-related. Industry classifications are from Chung et al. (2014). The sample consists of largefirms (i.e., 500+ employees) as classified by the USPTO. Anti-Troll Law is an indicator equalto one if a state has passed anti-troll legislation in or before that quarter. The State Controlsinclude the quarterly real GSP growth rate, the natural logarithm of income per capita, andthe unemployment rate in the state (all lagged) as well as an indicator for other state initiativesaimed at promoting high-tech startup employment. Each observation is a state-quarter. Allspecifications include state and year-quarter fixed effects. Robust standard errors are clusteredby 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 0.0276 0.0232 0.0265 0.0223

(0.0510) (0.0486) (0.0548) (0.0531)




No. Observations 1,000 1,000 1000 1000

Within R-Squared 0.001 0.014 0.001 0.008

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Table IA.9: Identification Tests – Patent Applications

The dependent variable is the natural logarithm of one plus the number of applications forsoftware patents. The sample consists of small firms and individual inventors as classified by theUSPTO. Column 1 appends the baseline specification with indicators for the 4 quarters prior tothe adoption of the anti-troll law. Column 2 estimates the baseline specification in a matchedsample (described in text). In column 3, Anti-Troll Lawt-12 is a placebo indicator that equals onestarting 12 quarters prior to a state passing anti-troll legislation. Column 4 excludes Californiaand Massachusetts from the analysis. Column 5 reports weighted OLS regressions with weightsbased on the natural logarithm of the number of software patent applications in each state for2010 and 2011. The State Controls include the quarterly real GSP growth rate, the naturallogarithm of income per capita, and the unemployment rate in the state (all lagged) as well asan indicator for other state initiatives aimed at promoting high-tech startup employment. Eachobservation 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 atthe 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** 0.0798*

(0.0663) (0.0511) (0.0502) (0.0425)

Anti-Troll Lawt-1 -0.0040

(0.0638)

Anti-Troll Lawt-2 0.124

(0.0745)


(0.0696)


(0.0723)


(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 1000 1,280 1,000 960 980

Within R-Squared 0.034 0.016 0.003 0.029 0.019

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Table IA.10: Patent Citations

This table examines the effect of state anti-troll laws on patent quality. In columns 1 and 2, thedependent variable is the natural logarithm of one plus the mean number of citations received bygranted software patent applications filed in each state-quarter. In columns 3 and 4, the dependentvariable is the natural logarithm of one plus the 75th percentile of the number of citations receivedby granted software patent applications filed in each state-quarter. Data on patent citations are fromthe USPTO’s PatentsView database. The sample consists of small firms and individual inventorsas classified by the USPTO (patents with inventors from multiple states are excluded as startupsare unlikely to have operations in multiple states). Anti-Troll Law is an indicator equal to one ifa state has passed anti-troll legislation in or before that quarter. The State Controls include thequarterly real GSP growth rate, the natural logarithm of income per capita, and the unemploymentrate in the state (all lagged) as well as an indicator for other state initiatives aimed at promotinghigh-tech startup employment. Each observation is a state-quarter; state-quarters without at leastone 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 clusteredby 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 0.113 0.105 0.214** 0.220**

(0.0746) (0.0744) (0.0868) (0.0899)




No. Observations 835 835 835 835

Within R-Squared 0.004 0.015 0.009 0.015

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Table IA.11: Dollar Amount of VC Funding

The dependent variable is the natural logarithm of one plus the dollar amount of VC funding ina 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 3–4and 5–6 report results for IT and non-IT sectors, respectively. The sample consists of states with atleast one VC investment at the start of the sample in 2011Q2. Anti-Troll Law is an indicator equalto one if a state has passed anti-troll legislation in or before that quarter. The State Controls includethe quarterly real GSP growth rate, the natural logarithm of income per capita, the unemploymentrate, and the natural logarithm of granted patents in the state (all lagged) as well as an indicatorfor other state initiatives aimed at promoting high-tech startup employment. Each observation is astate-quarter. All specifications include state and year-quarter fixed effects. Robust standard errorsare 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* 0.0651 0.0636

(0.125) (0.112) (0.122) (0.121) (0.161) (0.149)




No. Observations 819 819 819 819 819 819

Within R-Squared 0.007 0.018 0.007 0.018 0.000 0.010

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Table IA.12: Identification Tests – Venture Capital

The dependent variable is the natural logarithm of one plus the number of unique IT firmsraising VC funding in a state. The sample consists of states with at least one VC investment atthe start of the sample in 2011Q2, and includes VC investments at the “startup/seed”, “earlystage”, or “expansion” phase for firms founded in 2005 or later. Column 1 appends the baselinespecification with indicators for the 4 quarters prior to the adoption of the anti-troll law. Column2 estimates the baseline specification in a matched sample (described in text). In column 3, Anti-Troll Lawt-12 is a placebo indicator that equals one starting 12 quarters prior to a state passinganti-troll legislation. Column 4 excludes California and Massachusetts from the analysis. Column5 reports weighted OLS regressions with weights based on the natural logarithm of the numberof unique firms raising VC in each state for 2010 and 2011. The State Controls include thequarterly real GSP growth rate, the natural logarithm of income per capita, the unemploymentrate, and the natural logarithm of granted patents in the state (all lagged) as well as an indicatorfor other state initiatives aimed at promoting high-tech startup employment. Each observation isa state-quarter. All specifications include state and year-quarter fixed effects. Robust standarderrors are clustered by state. We use ***, **, and * to denote significance at the 1%, 5%, and10% level (two-sided), respectively.

Dep. Var. = Ln(1 + # Firms Receiving VC), 2011 VC > 0



(1) (2) (3) (4) (5)

Anti-Troll Law 0.185** 0.101* 0.163*** 0.152***

(0.0750) (0.0545) (0.0537) (0.0528)


(0.0962)


(0.0871)


(0.106)


(0.0970)


(0.101)




No. Observations 819 945 777 798 756

Within R-Squared 0.021 0.019 0.012 0.019 0.026

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Table IA.13: Identification Tests – Patents Pledged as Collateral

The dependent variable is the natural logarithm of one plus the number of software patentspledged as collateral. The sample consists of small firms and individual inventors as classified bythe USPTO. Column 1 appends the baseline specification with indicators for the 4 quarters priorto the adoption of the anti-troll law. Column 2 estimates the baseline specification in a matchedsample (described in text). In column 3, Anti-Troll Lawt-12 is a placebo indicator that equals onestarting 12 quarters prior to a state passing anti-troll legislation. Column 4 excludes Californiaand Massachusetts from the analysis. Column 5 reports weighted OLS regressions with weightsbased on the natural logarithm of the number of software patents pledged in each state for 2010and 2011. The State Controls include the quarterly real GSP growth rate, the natural logarithmof income per capita, and the unemployment rate in the state (all lagged) as well as an indicatorfor other state initiatives aimed at promoting high-tech startup employment. Each observation isa state-quarter. All specifications include state and year-quarter fixed effects. Robust standarderrors are clustered by state. We use ***, **, and * to denote significance at the 1%, 5%, and10% level (two-sided), respectively.

Dep. Var. = Ln(1 + # Firms Pledging Patents)



(1) (2) (3) (4) (5)

Anti-Troll Law 0.183** 0.221*** 0.168** 0.173*

(0.0807) (0.0645) (0.068) (0.101)


(0.109)

Anti-Troll Lawt-2 -0.0033

(0.106)


(0.0960)


(0.103)


(0.0860)




No. Observations 1,000 1,280 1,000 960 700

Within R-Squared 0.012 0.018 0.006 0.012 0.012

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