WORKING PAPER · NO. 2023-32

Where Have All the “Creative Talents” Gone?
Employment Dynamics of US Inventors
Ufuk Akcigit and Nathan Goldschlag
MARCH 2023

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Where Have All the "Creative Talents" Gone?
Employment Dynamics of US Inventors∗
Ufuk Akcigit†

Nathan Goldschlag‡

March 3, 2023

Abstract
How are inventors allocated in the US economy and does that allocation affect innovative capacity? To answer these questions, we first build a model of creative
destruction where an inventor with a new idea has the possibility to work for an
entrant or incumbent firm. If the inventor works for the entrant the innovation is
implemented and the entrant displaces the incumbent firm. Strategic considerations encourage the incumbent to hire the inventor, offering higher wages, and thennot implement the inventor’s idea. To test this prediction, we combine data on theemployment history of over 760 thousand U.S. inventors with information on jobs
from the Longitudinal Employer-Household Dynamics (LEHD) Program at the U.S.
Census Bureau. Our results show that (i) inventors are increasingly concentrated
in large incumbents, less likely to work for young firms, and less likely to become
entrepreneurs, and (ii) when an inventor is hired by an incumbent, compared to ayoung firm, their earnings increases by 12.6 percent and their innovative output declines by 6 to 11 percent. We also show that these patterns are robust and not driven
by life cycle effects or occupational composition effects.

JEL Codes: O3, O4
Keywords: Inventors, innovation, R&D, firms, dynamism, reallocation∗ Any opinions and conclusions expressed herein are those of the authors and do not represent theviews of the U.S. Census Bureau. The Census Bureau has reviewed this data product for unauthorized
disclosure of confidential information and has approved the disclosure avoidance practices applied to thisrelease. We thank Joseph Staudt, Shawn Klimek, participants at the 2022 Research Data Center Annual
Conference and USPTO Chief Economist Seminar for helpful comments and suggestions. Zeki Berkay
Saygin and Craig Chikis provided excellent research assistance. DRB Approval Number(s): CBDRB-FY20CES007-004, CBDRB-FY21-CES007-004, and CBDRB-FY22-CES008-008.
† University of Chicago‡ U.S. Census Bureau

1Introduction

Societies allocate a significant fraction of their resources into R&D to generate economic
growth through technological progress. Therefore many analyses of economic growth
focus on a country’s innovation inputs, either aggregate R&D spending as a share of GDP
or the share of inventors in its workforce (e.g., Jones, 1995). In this paper we argue that it
is not only the level of innovation inputs that matters for growth, but also the allocationof those investments. As Arrow (1962) has pointed out, incumbent monopolists sufferfrom the replacement effect, and may shift their efforts away from producing radical
breakthroughs to defensive activities (Akcigit, Baslandze, and Lotti, 2022). As such, thesocial impact of an inventor may depend on the type of firm they work for.
Standard models of endogenous growth (Romer, 1990; Aghion and Howitt, 1992;
Grossman and Helpman, 1991; Klette and Kortum, 2004; Akcigit, 2017) predict that economic growth should be proportional to the share of the workforce employed in the research sector. Figure 1 presents seemingly contradictory evidence on this point. Panel A
shows utilization-adjusted total factor productivity (TFP) in the U.S. since 2000 (left axis)
and a per capita measure of inventor labor (right axis). While there was a visible acceleration between 2000-2005, after 2005 we see a marked slow-down in TFP growth even as
the share of inventors nearly doubled over the same period. In other words, innovation
inputs are rising as technical progress slows. Equally striking is the shifting allocationof inventors across different-sized firms. Not only did the US economy allocate a bigger
share of its employment into innovation, but its composition has also shifted towards
the largest players in the economy. Panel B shows that the share of inventors employedby large, incumbent firms rose from 48 percent 2000 to about 57 percent in 2016. Akcigit
and Goldschlag (2022) show a complementary fall in the share of inventors employedby young firms. The changing composition might be especially concerning if inventors
at incumbents produce lower quality innovations, which appears to be the case in Panel
C. Panel C shows that inventors at incumbents produce lower quality innovations, withfewer citations, fewer citations per application, fewer independent claims, and more self
citations (e.g. more incremental). These striking figures give rise to an important question: How are inventors allocated in the U.S. economy and does that allocation affectinnovative capacity?

1Figure 1: Inventor Employment Dynamics(a) TFP & Inventors(b) Inventors at Incumbent Firms(c) Inventor Productivity, Young & Incumbent Firms

Source: Fernald and Jones (2014), Inventor Employment History, BDS, author’s calculations
Notes: Panel A shows utilization adjusted TFP (left axis) indexted to 1980 and the count of uniqueinventors per 100 thousand employment (right axis). Panel B shows the percent of inventors employed atincumbent firms, which are defined as those > 20 years old and > 1000 employees. Panel C shows theratio of inventive productivity measures for inventors at incumbent firms relative to young firms, which
are defined as firms aged ≤ 5 years old. Applications is the count of applications that are eventually
granted, cites is the count of citations received, cites per app. is the count of citations divided by thecount of applications, ind. claims is the count of independent claims per granted application (Marco,
Sarnoff, and deGrazia, 2019), and the self cite rate is the share of citations made to patents with the sameassignee.

2

To answer these questions, we first build a tractable creative destruction model thatdevelops intuitions about the strategic incentives incumbent firms face and how theymight use the innovation input market to limit competition. In particular, we allow
for an incumbent to hire an inventor who otherwise would create an innovation inside
an entrant firm and displace the incumbent. Since the incumbent monopolist alreadyhas a product that it sells to the market, without strategic considerations, it does nothave any incentive to produce an innovation that would replace the already successful
product that it sells. However, strategic considerations make the incumbent firm hire
the inventor, offering higher wages, and under certain circumstances, choosing not to
implement the inventor’s idea. The model implies that inventors hired by incumbent
firms will earn more but produce fewer innovations compared to inventors hired byentrants.
Our empirical analysis tests the models implications using data on the employment
history of over 760 thousand U.S. inventors. We combine data on individual inventors ofpatents granted by the U.S. Patent and Trademark Office with information on jobs fromthe Longitudinal Employer-Household Dynamics (LEHD) Program at the U.S. Census
Bureau. LEHD contains quarterly employee-employer relationships drawn from administrative state-level unemployment insurance records.
Our results show that (i) inventors are increasingly concentrated in large incumbents,
less likely to work for young firms, and less likely to become entrepreneurs, (ii) in the
cross section, inventors working for incumbent firms earn more and produce less impactful innovations than inventors at young firms, and (iii) when an inventor is hired byan incumbent, compared to a young firm, their earnings increases by 12.6 percent andtheir innovative output declines by 6 to 11 percent. We also show that these patterns
are robust and not driven by promotion to managerial positions in large incumbents, forinstance.

Related Literature
Our paper is related to a number of literatures on firms, inventors, and innovation. First,
a growing literature has documented that factor reallocation among competing firms is
an important source of productivity growth (e.g., Foster, Haltiwanger, and Krizan, 2001,
2006; Acemoglu, Akcigit, Alp, Bloom, and Kerr, 2018). Firms that experience a positive
shock, such as the arrival of a new innovation, expand, increasing aggregate productivity. Responsiveness to shocks, however, may depend on market conditions. Indeed, inrecent decades the dispersion of firm-level shocks has risen as firm responsiveness to3those shocks as weakened Decker, Haltiwanger, Jarmin, and Miranda (2020). Moreover,models of reallocation typically assume that reallocation, if it occurs, is productivity enhancing. Our work shows that the reallocation of inventor labor may reduce productivity
growth when it is the result of strategic behavior of incumbents. We also show how thosestrategic channels dampen reallocation and limit the replacement of incumbent firms.
Second, a large literature has debated the relative contribution of small, young, andlarge firms to innovation.1 Akcigit and Kerr (2018), for example, show that lower quality
innovations scale more quickly with firm size. Complementary evidence stresses theimportance of the strategic behavior of incumbents. Argente, Baslandze, Hanley, andMoreira (2020) show that not only are the patents of large firms less innovative, butthey are often used to deter competition. In the extreme, incumbents may acquire entrants to avoid competition and stifle innovation (Cunningham, Ederer, and Ma, 2021).
We contribute to this literature by demonstrating how differences in innovation incentives across firm types, and the strategic behavior of incumbents, affects how innovation
inputs are utilized. Our theoretical framework and empirical evidence is highly complimentary to the work of Cunningham, Ederer, and Ma (2021), who demonstrate the“killer acquisiton” phenomenon both theoretically and empirically. Where they focus on
firm-level poaching, our focus is on inventors, employment dynamics, and earnings.
Third, a large literature has documented that capital or labor misallocation can leadto sizable productivity losses (e.g., Hopenhayn and Rogerson, 1993; Hsieh and Klenow,
2009; Hsieh, Hurst, Jones, and Klenow, 2019). In these papers, misallocation is discussed
in terms of static, cross sectional misallocation of factor inputs. From a dynamic perspective focused on the supply of inventors, a related literature finds misallocation oftalent due to financial frictions, socioeconomic background, and R&D and education
policies.2 Our findings speak to both of these literatures by showing how the effects of
misallocation in who becomes an inventor are mediated by how inventors are utilizedby firms.
Finally, various recent papers have argued that the U.S. economy has exhibited declining business dynamism. Decker, Haltiwanger, Jarmin, and Miranda (2014) show thatthe entry rate of firms and establishments has declined over the past several decades.
Karabarbounis and Neiman (2014) document the decline in labor share during the same
period, while Autor, Dorn, Katz, Patterson, and Van Reenen (2020) argue that market
concentration has increased and this increase has been strongly correlated with the de1 See Akcigit and Kerr (2018) for an overview of this literature.
2 See Celik (2022), Aghion, Akcigit, Hyytinen, and Toivanen (2017), Bell, Chetty, Jaravel, Petkova, and

Van Reenen (2019), Akcigit, Grigsby, and Nicholas (2017), Akcigit, Pearce, and Prato (2020).

4

cline in labor share at the sectoral level. Akcigit and Ates (2022) provide a quantitative
investigation and argue that the decline in knowledge diffusion from market leaders tofollowers could be an important driver of declining business dynamism. In this paper,
we show the rising in concentration of inventors within large incumbents, combinedwith the strategic hiring of inventors, could lead to a decline in overall dynamism in the
U.S. economy.

2

Model

To motivate and provide a framework for interpreting our empirical analyses, we present
a simple model in which incumbents face displacement by innovating entrants. Entrants
employ an inventor whose idea can be implemented, at a cost, increasing product quality
and capturing the market. Under certain assumptions, incumbents will have an incentive
to strategically hire, or poach, the entrant’s inventor and not implement the qualityimproving innovation. This occurs if implementing the innovation is sufficiently costly
relative to the size of the innovative step, making it more profitable for the incumbent toshare monopoly rents with the inventor, via higher wages, and avoid displacement by theentrant. In this environment, we expect to see higher wages and less innovative output
associated with inventors hired by incumbents relative to inventors hired by entrants.

Production Environment
Individuals consume a unique final good Yt , which is also used for R&D as discussed
below. A measure L units of labor is supplied inelastically by the household. The final
good at time t is produced by labor and a continuum of intermediate goods j ∈ [0, 1]
with the production technology as follows,
β

Lt
Yt =
1−β

Z 1
0

β 1− βq jt k jt djt.

(1)

Henceforth, we will omit the time subscript where doing so does not cause confusion.
Producing each unit of k j has a marginal cost of η > 0 in terms of the final good. Finalgood is produced in a perfectly competitive market whereas intermediate goods will be
produced by monopolists. Maximization behavior of the final goods producer yields
demand for each intermediate good as follows,β −βpj = Lβ qj k j .
5(2)This implies that the monopoly profit maximization in sector j can be expressed asfollows,
π j = max{ p j k j − ηk j }Subject to Equation (2)k j ,p jHence the equilibrium would be,
k∗j =



1−β
η

1

β

Lq j and p∗j =

η
.
1−β

We normalize η = 1 − β and L = 1. Then the equilibrium profit becomes,
π ∗j = βq j .

Innovation Environment
Each monopolist, operating in a given sector, lives for only one period and passes thefirm to their offspring. We assume incumbent monopolists maximize their one-period
return.3 Each sector has one potential entrant that employs an inventor. Each inventor
has an idea that can be implemented at some cost. In some cases, the incumbent is presented with the opportunity to poach the inventor. Search and matching frictions limit
how often the incumbent is able to hire the entrant’s inventor. Tightness of the inventor
labor market for each sector is fixed, as there is only one inventor, so the incumbent’s
ability to poach resolves to a simple probability of meeting the inventor. If the entrant
implements the inventor’s idea, product quality increases and the entrant displaces theincumbent (i.e. creative destruction occurs). When displacing the incumbent, the entrant
receives post-innovation profits with upgraded quality net of implementation costs andthe incumbent exits. Alternatively, if the incumbent poaches the inventor, the incumbent must then choose whether to implement the idea or not. If the value of monopoly
rents associated with the previous technology dominates the value of implementing the
new idea, net of implementation costs, the incumbent will act strategically, poaching theinventor, and not implement the innovation.
More formally, the sequence of events can be described by the game-tree presented
in Figure 2, which we describe in greater detail below.
3 This relatively simple environment allows us to capture the salient strategic interactions without

significant loss of generality.

6Figure 2: Innovation Environment Game Tree
Draw φ, e

B : MeetA : Not Meet

Incumbent

C : No Poach
Entrant

E : Implement

Entrant Profit:
Inventor Wages:Incumbent

Entrant
G : Imp.

Incumbent Profit:

D : PoachH : No Imp.
I : Imp.

F : Not Implement
 

0
 
π 
 e
 
w̃e



 πi 
 
0
 
 
0

 



0
 
π 
 e
 
we



 πi 
 
0
 
 
0



J : No Imp.
  πi 
 
0
 
 
wi



 πi 
 
0
 
 
wi

Notes: φ is the cost of implementing the inventor’s idea. e is the probability the incumbent meets theentrant’s inventor in the labor market. πi and πe are the incumbent and entrant profits, respectively. w̃e isinventor wages in the case of no meeting with the incumbent. we and wi are inventor wages at theentrant and incumbent respectively.

Entrant Innovation. The cost of implementing the inventor’s idea is φ > 0. When theinventor’s idea is implemented, quality increases from q j to (1 + λ)q j and the entrant
displaces the incumbent. If this occurs, the entrant gets post-innovation profits, with
upgraded quality, net of implementation costs, βq j (1 + λ) − φ and the incumbent exits
and receives zero. We assume that the implementation cost lies between the incremental value of the innovation (βq j λ) and the post innovation revenues βq j (1 + λ), as inInequality 3.
βq j (1 + λ) > φ > βq j λ(3)

The remaining possibilities are that φ > βq j (1 + λ) > βq j λ or βq j (1 + λ) > βq j λ > φ.
The former corresponds to no ideas being implemented because they are too costly forboth the entrant and incumbent. In the latter case, all ideas will be implemented without

7

strategic considerations because it is always profitable to do so. With Inequality 3, we
focus on the most interesting and empirically relevant case.
With probability e ∈ (0, 1) the incumbent meets the entrant’s inventor and has theopportunity to poach. If no meeting occurs, the inventor remains at the entrant andbargains over wages with no outside options. We will solve the no meeting and meetingcases in turn.
Incumbent does not meet inventor (branch A). With probability 1 − e there is no
meeting between the incumbent and inventor. In this case the inventor remains at theentrant. Since the cost of implementation is less than the entrant’s post-entry return(βq j (1 + λ) > φ), the entrant will implement the idea and displace the incumbent, following branch AE of Figure 2. We assume Nash bargaining between the entrant andinventor, with the entrant’s bargaining power represented by γ ∈ (0, 1). Since the entrant and inventor’s outside options are zero (branch AE vs AF), the Nash bargaining
can be expressed as the following maximization problem.
1− γmax(πe − w̃e )γ w̃ew̃e

The inventor’s wages in the case of no meeting with the incumbent is w̃e and theentrant’s profit is πe . Through first-order conditions, we find that the inventor’s equilibrium wages are a fraction of the entrant’s profits equal to their bargaining power (1 − γ),w̃e∗ = (1 − γ)πe
Incumbent meets inventor (branch B). If the incumbent meets the inventor it mustchoose whether or not to poach the inventor. Not poaching the inventor resolves equivalently to not meeting the inventor, described above (A and BC of Figure 2).
The threat of entry creates interesting strategic considerations. Conditional on having poached the inventor, the incumbent’s innovation decision (branch BD) takes thefollowing form:

max





0
,
|{z}

Value o f not implementing

βq j λ − φ| {z }

Value o f implementing







The value of not implementing the idea is profits from the existing technology (βq j >0). The value of implementing the idea is post innovation profits net of implementation
costs and net of profits associated with the existing technology. Given the assumptions
8

on φ in Inequality 3, the value of innovation will be negative, and thus the incumbent
chooses not to implement the inventor’s idea, following BDJ of Figure 2.
In order to poach the inventor the incumbent must offer wages sufficient to induce
the inventor to switch. As with in the no meeting case, we assume that wages are
determined through Nash bargaining with the firm’s bargaining power being γ. Whenbargaining, the inventor’s outside option is to work for the other firm in the sector.
Conditional on meeting the inventor, the bargaining between the incumbent and theinventor takes the following form:
max(πi − wi )γ (wi − we )1−γ
wi

Subscripts i and e designate incumbent and entrant values respectively. The incumbent’s surplus is profit from the existing technology (πi = βq j ) net of inventor wages
paid by the incumbent (wi ). The inventor’s bargaining surplus is the wage at the incumbent (wi ) net of the outside option of wages at the entrant (we ). A similar, symmetricbargaining problem occurs between the entrant and inventor:
max(πe − we )γ (we − wi )1−γ
we

Now we conjecture, and verify, that wi∗ > we∗ . If incumbent wages are greater thanentrant wages, the entrant bargaining problem is maximized when we∗ = πe . Intuitively,
if the incumbent offers higher wages, the inventor can always do better by working forthe incumbent. Since the entrant’s outside option is not entering and receiving zero,
the entrant is willing to set the inventor’s wages equal to its profit. Solving the incumbent bargaining problem, given entrant wages are equal to entrant profit, the inventor’sequilibrium wages at the incumbent can be written as follows:wi∗ = (1 − γ)πi + γπe
Our assumption in Inequality 3 implies that πi > πe since πi = βq j and πe = βq j +( βq j λ − φ) and φ > βq j λ. As πi > πe and wi∗ > we∗ , our conjecture is verified. Note thatsince entrant wages are equal to entrant profits the model also captures the dynamics ofinventor entrepreneurship. If the inventor owns the entrant, rather than being employedby it, the inventor’s return remains equal to post-entry profits and strategic interaction
with the incumbent remains unchanged.
The model yields the following predictions. When an incumbent producing using anolder technology has a cost advantage over an innovative entrant, the model predicts:

9

Prediction 1: The incumbent will poach the inventor and not implement the inventor’s idea, reducing aggregate innovative activity.
Prediction 2: The incumbent will poach inventor entrepreneurs and not implement
the inventor’s idea, reducing aggregate innovative activity.
Prediction 3: The incumbent will offer higher wages than the entrant in order topoach the inventor from the entrant.

3

Empirical EvidenceThis section describes the empirical results on inventor productivity and firm characteristics.

Data
Patent Data. We use data from the U.S. Patent and Trademark Office on patents granted
between 2000 to 2019. We use this data to identify inventors of granted patents andcharacterize their inventive output each period. Name and location information frompatent documents are used in the matching procedures described below. Those matchingalgorithms also rely on the name and location of patent assignees, the firm to which thepatent was associated with at the time of grant. Information on the date of each patent
grant, and the associated application was submitted, are used to create counts of patents
associated with each inventor each period. Additionally, we use data on citations tocitation weight patents counts and count self citations. We also utilize claim counts, aswell as the categorization of claims as either independent or dependent, developed by
Marco, Sarnoff, and deGrazia (2019).
Data Linking. We use linkages between inventor records (patent, inventor sequence
combinations) and the Census Bureau’s disambiguated, anonymized, person-level identifiers, known as Protected Identification Keys (PIKs), developed by Akcigit and Goldschlag (2022). The match proceeds in several steps. First, the Person Validation System (PVS) is used to match inventor name and location information to PIKs (Wagnerand Lane, 2014). The PVS typically relies on detailed personally identifiable informationsuch as date of birth, street address, and/or social security number. Since the patent datacontains only name, city, and state, the PVS system generates a large number of false
positives. To address this, we rely on the the simultaneous consideration of inventor-PIK
and assignee-firm linkages as described in Graham, Grim, Islam, Marco, and Miranda

10(2018) and Dreisigmeyer, Goldschlag, Krylova, Ouyang, and Perlman (2018). We augment those matches with unique PVS matches and additional inventor records capturedby the disambiguated inventor identifiers in the PatentsView patent database.
Ultimately, we observe the employment histories of approximately 760 thousand inventors associated with 3.6 million patents granted between 2000 and 2016.
Administrative and Survey Data. Our frame of inventor jobs is developed using datafrom the LEHD Program at the U.S. Census Bureau. LEHD contains quarterly job-level
observations drawn from administrative state-level unemployment insurance records.
Not all states are covered by these data and those that are covered are not observedin every year. We restrict to 45 states with employment data starting in 2004 or earlier.
Our job-level panel includes only dominant (highest earnings), beginning-of-quarter jobs
from 2000Q1 to 2016Q3.4 Our final sample contains nearly 35 million quarterly job
observations.
We augment LEHD employer information with firm characteristics including firm
size and firm age from the Longitudinal Business Database (LBD) (Haltiwanger, Hyatt,
McEntarfer, Sousa, and Tibbets, 2014). In addition to firm age and firm size, we also
use linkages between the LEHD and LBD to identify entrepreneurs. Finally, we collectinformation on occupation from the American Community Survey (ACS). Despite being
relatively rare—the ACS sample ranges from 800 thousand to 3.5 million housing units
during our sample period—we are able to attach occupation information to over 100
thousand of our inventor-job observations.

MeasuresInventive Productivity. Once inventor records are disambiguated and linked to PIKs, weare able to assign time-varying inventive productivity measures drawn from patent data.
We count the number of granted patent applications (Apps) and applications weighted
by the number of citations received in the first five years after the patent was granted(Cites). Windowing citation counts in this way limits truncation effects due to older
patents having more time to accrue citations. We also compute the number of citations
received per application in the period (Cites Per App). We differentiate between citations
made and citations received, identifying self-citations as those where the citing and cited
patent have the same assignee. We use the count of citations made and self citations
4 A beginning-of-quarter job is defined as a job for which the individual has positive earnings in botht and t − 1. A dominant beginning-of-quarter job is the highest earning beginning-of-quarter job for theindividual within a given quarter. Some states enter the data after 2000Q1.

11

made to compute the share of self citations (Sel f Cite Rate). Patents that primarily citeother patents owned by the same firm are more incremental in nature (Akcigit and Kerr,
2018). Finally, we measure the scope of patents by computing the share of claims that areclassified as independent (Independent Claims) using data developed by Marco, Sarnoff,and deGrazia (2019).
Employment Outcomes. For each quarter in our Inventor Employment History database
we measure the natural log of real full-quarter earnings (ln( Earnings)).5 Hire and separation events are identified using the sequence of employers observed in an individual’s
employment history.6 Following Choi, Goldschlag, Haltiwanger, and Kim (2021), we
identify entrepreneurs as individuals that receives earnings from a startup “on day one”
and are among the top earning workers at the firm in the first year.7
To better approximate the intuitions of the model, we classify employers based on
size and age as either a young or incumbent firm. Young firms are those less than age
six. Incumbents are older larger firms that are at least 21 years old and have at least
1,000 employees. This dichotomy sharpens the contrast in the types of firms that employ
inventors.

Results
In our Inventor Employment History database roughly 12 percent of inventors are female, 27 percent were born outside of the US, and approximately 9 percent are observed
starting at least one business during our sample period. Over 60 percent of the inventorquarter observations are of inventors aged 36 to 55, making our inventor sample older,on average, than the US workforce. In 2010, approximately 46 percent of the US labor
force was between the ages of 35 and 54.8 Approximately 55 percent of inventor jobs areat incumbent firms while about 10 percent are at young firms. Comparing this to datafrom the Census Bureau’s Business Dynamics Statistics, approximately 38 percent of all
employment during this period was associated with incumbent firms and 12 percent
in young firms.9 The hire and separation rates we observe for inventors are about 4.6
5 Full quarter earnings is only observed when a job has positive earnings in both the previous andsubsequent quarters. This reduces the likelihood that an earnings measure represents only partial quarter
employment. Earnings is normalized to 2012q1 dollars and demeaned within 6-digit NAICS and quarter.
6 Stable hire and separations are defined analogously to those measured in the Quarterly Workforce
Indicators (QWI).
7 Entrepreneurship outcomes are only available in our data through 2015.
8 See the Bureau of Labor Statistics Current Population Survey Employment status of the civilian noninstitutional population by age, sex, and race tables for details https://www.bls.gov/cps/aa2010/aat3.txt.
9 See the Business Dynamics Statistics tables for details https://www.census.gov/data/datasets/timeseries/econ/bds/bds-datasets.html.

12percent and 4.4 percent, respectively.10
We find interesting time series patterns for several of our employment dynamics
measures. As shown in Figure 1, the share of inventors employed by incumbents roseby 17%, or 8 percentage points, from 48.9 percent in 2000 to 57.3 percent in 2016. Also,
as shown by Akcigit and Goldschlag (2022), hire and separation rates for inventors are
declining over this period. The hire and separation rates of inventors fell by roughly
40% between 2000 and 2016, from about 7 percent to less than 4 percent. Meanwhile,
workers in similar industries saw hire and separation rates that were fairly stable at 6 to
6.5 percent, suggestive of declining employment dynamism among inventors.
The entrepreneurship rate among inventors also fell over this period. Panel A of Figure 3 shows the change in the likelihood that an inventor starts a new business each year,
relative to 2000, after controlling for individual fixed effects. Entrepreneurship among
inventors is relatively rare. Though about 9 percent of inventors are ever observed as
entrepreneurs, only 0.639 percent founded firms in 2000. Since 2000, the inventor entrepreneurship rate fell by roughly 44%, or nearly 0.3 percentage points. This is especially concerning since firms founded by inventors have a much steeper life cycle curve.
Panel B of Figure 3 shows the change in firm size over the firm’s life cycle for inventor
founded and non-inventor founded firms, relative to its size at birth, after controlling forindustry-year effects. Conditional on survival, by age five inventor founded firms growby nearly 70 percent while non-inventor founded firms grow by 41 percent.11 Not only
do inventor founded firms experience higher growth rates, they are also larger at birth.
On average, inventor founded firms have 8.1 employees and non-inventor founded firms
have 5.7 employees.12 The results in Figure 3 suggest that the high growth young firms
generated by inventors are becoming more rare over time.
Though many measures of employment dynamics and entrepreneurship in the U.S.
economy fell over this period, one might have expected those trends to be different
among the high human capital inventive workforce. Instead, we find that inventors are
increasingly less likely to change jobs, less likely to start a business, and increasingly
concentrated among older, larger incumbent firms. In the cross section, we find thatinventors employed by incumbents produce patents that have fewer citations, fewer independent claims, and more self citations (Panel C of Figure 1). Moreover, inventors
10 See Akcigit and Goldschlag (2022) for additional descriptive analyses of the Inventor Employment

History database.
11 Elasticities for ln ( FirmSize ) are interpreted using the e β a , where β is defined as in the equation inathe Figure notes.
12 Standard errors for these means are (32.5) and (24.1), respectively.

13

Figure 3: Inventor Entrepreneurship(b) Inventor Firm Performance(a) Declining Inventor Entrepreneurship

Source: Inventor Employment History, author’s calculations
Notes: Panel A reports estimates of β k s from the following equation,kEntrepi,t = α + ∑2015
k =2001 β k Dt + ψi + ei,t . For inventor i in year t, Entrepi,t equals 1 if the inventor started abusiness in that year and zero otherwise, Dt are year effects, and ψi are person fixed effects. The mean of
Entrepi,t in 2000 is 0.0064. Panel B shows estimates of β a s from the following equation,ln( FirmSize f ,t ) = α + ∑10
a=1 β a FirmAge f ,t + δ f ,t + e f ,t . For firm f in year t, ln ( FirmSize f ,t ) is the log offirm employment in year t, FirmAge f ,t is the firm’s age in year t, and δ f ,t are industry-year fixed effects.

employed by incumbents have 13% higher earnings than inventors at young firms.13
The shift of inventors away from young firms towards incumbents, taken together
with the fact that inventors employed by incumbents have slightly more but lower quality patents, may lead to an aggregate decline in quality-adjusted innovative output. To
put the shifting composition into context, and setting aside causality and general equilibrium forces, a back-of-the-envelope calculation suggests that the shifting composition
could yield, ceteris paribus, a 0.6% increase in applications and a 5.3% decrease in citations.14 This finding is consistent with the strategic use of patents described by Argente,
13 Additional summary statistics on the Inventor Employment History database can be found in theOnline Appendix
14 The share of inventors at young firms was 14 and 7.5 percent in 2000 and 2016, respectively. The shareat incumbents was 48 and 57 percent. Normalizing these shares to exclude the omitted group (middle
age and small old firms), the incumbent share rose from 77.4 to 88.4 percent. The average applications atincumbents and young firms is 0.919 and 0.0872, respectively. Average citation weighted application countis 0.537 and 0.828 for incumbent and young, respectively (see Online Appendix Table A1). This implies((0.884∗0.919)+(0.116∗0.0872))−((0.774∗0.919)+(0.226∗0.0872))) and a 5.3
a shift in applications of 0.568 percent (100 ∗(0.774∗0.919)+(0.226∗0.0872)
percent decrease in citations (100 ∗((0.884∗0.537)+(0.116∗0.828))−((0.774∗0.537)+(0.226∗0.828))).
(0.774∗0.537)+(0.226∗0.828)

14

Baslandze, Hanley, and Moreira (2020).
Rather than different types of firms doing fundamentally different types of innovation, these patterns may reflect a broader shift in the age composition of the inventor
workforce. Indeed, Akcigit and Goldschlag (2022) document an aging of the inventor
population. If older inventors have lower output (e.g. Jones (2010)), higher earnings,
and are more likely to work for incumbents, then these differences may simply reflect
inventor life cycle and age composition effects.
In order to more precisely estimate the difference in inventor output and earnings
between inventors at young and incumbent firms, we propose a matching exercise andevent study framework. Specifically, we identify similar inventors hired by incumbents
and young firms and measure how their outcomes differ after the hire. We identify
inventors hired by incumbents and young firms each period and create an annual panelof earnings and inventive output measures before and after the hire (t − 4 to t + 4 witht = 0 being the year of the hire event).15 To find “pairs” of very similar hire events, weclassify inventors into deciles of the distribution of inventive output or earnings in eachyear prior to the hire (t − 1 to t − 4 with t = 0 being the year of the hire event). For
each outcome measure, we match on these decile bins, sector, and hire quarter. When
more than one potential match exists, we select a single match that is closest in age
and pre-hire inventive productivity or earnings.16 By incorporating age in our matchingalgorithms we ensure that differences in earnings and inventive output do not simply
reflect differences in the inventor’s life cycle. Our matching strategy generates very
similar pre-hire outcomes (see bottom panel of Table 1).
With the sample of matched incumbent and young firm hires, we measure the difference in post-hire outcomes between the two groups by estimating the following equation.
For inventor i, in year relative to the hire t, and hire event e we estimate,
15 For the event study we aggregate from the quarterly observations in our inventor jobs panel to theannual level by summing applications and citation weighted applications and taking the mean of fullquarter earnings, self citation rate, independent claim share, and citations per application. Individuals
with no applications in a given year will appear with zero applications while those with no full quarter
earnings will be have a missing earnings value for the year (and are therefore dropped).
16 Specifically, we minimize the normalized euclidean distance between age and inventor output or
earnings in each year t − 1 to t − 4. The match that is closest in age and pre-hire outcomes will be selected.
A given young hire event cannot be matched to more than one incumbent hire event and vice versa.
However, if an inventor experiences more than one hire event over time, they may enter the regression
analysis with either multiple hire events for a given firm type (e.g. incumbent) or different firm types. For
additional details and match balance statistics, see Online Appendix.

15

4

4

j=−4

j=−4

Yite = α + ∑ λ j d[ j]ite + β 1 Incumbentie + ∑ η j d[ j]ite × Incumbentie(4)+ β 2 Ageite + δj + γk + ψi + eite
Incumbentie is equal to one if the hire event occurred at a incumbent firm, zero if it
occurred at a young firm. δj , γk , and ψi are sector, hire year, and person fixed effects
respectively. Person-level fixed effects will control for time invariant characteristics ofindividual inventors. Ageite is the inventor age. λ j s capture year relative to hire effects.
Our focus will be estimates of the η j s, which capture the difference in outcome Y between
inventors hired by incumbents to similar inventors hired by young firms in each period
before and after the hire event.
Figure 4 shows the results of the hire event study for earnings and applications. Our
matching, by construction, eliminated pre-event differences between the incumbent andyoung firm hire samples. Estimates of η j for t − 4 to t − 2 are insignificant for bothearnings and applications. In the year of the hire (t = 0) we see incumbents have 15
percent higher earnings than young firm hires.17 This stabilizes to roughly 11 percent
four years after the hire event. At the same time, incumbent hires see a 0.023 lower
application count which falls to a 0.052 gap four years after the hire event. A decline inapplications of 0.052 represents approximately 8 percent of the pre-hire mean application
count (see bottom panel of Table 1).
As noted previously, we consider several different measures of inventive output. To
explore these alternative measures, we collapse the time dimension of our event study
into a difference-in-differences framework with a Postite binary that is equal to one if
t ≥ 0 and zero otherwise. The estimation equation becomes the following.

Yite = α + +υ1 Postite + υ2 Incumbentie + υ3 Postite × Incumbentie+ β 4 Ageite + δj + γk + ψi + eite

(5)

The difference-in-differences estimates for these additional outcomes are presented
in Table 1. The table shows the estimates for υ1 and υ3 and the associated pre-hiremeans in the bottom panel. The first two columns show the estimates for applications
and earnings. We find 0.036 lower applications for incumbent hires in the post period,
roughly 6 percent of the pre-hire mean for those hired by incumbents. For earnings, we
17 Elasticities for ln ( Earnings ) are interpreted using the eη j .

16

Figure 4: Young and Incumbent Inventor Hires

Source: Inventor Employment History, Founding Team Database
Notes: Shown are event study estimates for years relative to the hire event of the difference in an
inventor’s outcome, patent quality (citations per application) and log earnings, between inventors hiredby an incumbent firm and those hired by a young firm. We match inventor hire events at young firms(those age ≤ 5) and incumbent firms (firms at least 21 years old and with at least 1,000 employees).
Matching is performed on hire period and pre-hire period characteristics. Shown are estimates of η j from
Equation 4.

find 12.6 percent higher post-hire earnings for inventors hired by incumbents relative toyoung firm hires. Across the other measures, incumbent hires have 11.9 percent lower
citations, 11.3 percent lower citations per application, 5.4 percent lower independentclaim share, and 37.7 percent higher self citation rates, all relative to pre-hire incumbent
means.
The estimates shown in Table 1, though not casual, are in agreement with the crosssectional differences in earnings and inventive output referenced earlier and are consistent with the predictions of the model. Taken together these estimates suggests inventors employed by and those hired by incumbents, relative to those at young firms,
have higher earnings and lower inventive output, fewer applications, fewer citations andcitations per application, patents with more limited scope, and a higher self citation rate.

17

Table 1: Young and Incumbent Hires: Alternative Outcomes

Post
Post x Incumbent

R2
N
Pre-Hire Means
Incumbent Hires
Young HiresAppsln( Earnings)

Citations-0.1289***(0.006064)-0.03585***(0.007011)-0.03429***(0.007612)
0.1184***(0.009772)

0.311
781,000
0.6017(1.34)
0.6068(1.429)-0.8955***(0.2094)-0.6861***(0.2069)

Citationsper App-0.2611***(0.02871)-0.1145***(0.02439)

Independent
Claims-0.07455***(0.002297)-0.01581***(0.002392)

Self Cite
Rate-0.01234***(0.001006)
0.02278***(0.001358)

0.8106
143,000

0.2531
371,000

0.1845370,000

0.1818660,000

0.4716
213,000

10.29(1.045)10.29(1.051)5.758(33.01)
5.933(33.27)1.009(4.246)
1.039(4.338)0.2925(0.5645)0.2947(0.5795)

0.06035(0.132)0.0601(0.1309)

Source: Inventor Employment History, author’s calculation
Notes: Difference-in-Differences estimates for the hire event for each outcome Yi,t,e , between inventors
hired by an incumbent firm and those hired by a young firm. Estimates are of υ1 and υ3 from Equation 5.
Standard errors are shown in parentheses. Observation counts vary by measure because outcome relies aseparate match, minimizing the difference in pre-match values of the given measure. Incumbent binary
included but not reported. Observation counts have been rounded to avoid the disclosure of confidential
information.

Robustness
In this section we demonstrate the robustness of our estimates to several concerns aboutmeasurement and mechanisms.
Mechanical Self Citation Rate Effects. One potential concern with the self citation
rate estimates is that since incumbents have larger patent portfolios inventors hired byincumbents mechanically exhibit higher self citation rates because incumbent firms account for a larger share of patents at risk of receiving a citation. To determine the extent
to which this mechanical effect accounts for our estimates, we create a simulated self
citation rate by randomly generating self citations in proportion to the assignee’s shareof the technology groups (Cooperative Patent Classification (CPC) codes) cited by thepatent. When an assignee accounts for a larger share of the corpus of patents withina given technology code, the patent is more likely to generate self citations. We thenestimate Equation 5 with this simulated self citation rate and find estimates that are anorder of magnitude lower. The estimate for Post × Incumbent is 0.002107 compared to18

0.02278 found in Table 1.18 This suggests that the result that incumbent hires are more
likely to generate self citations is not entirely mechanical.
Occupational Differences. One potential explanation for the event study patterns aredifferences in the types of jobs inventors are doing at incumbents versus young firms.
If inventors are more likely to be managers at incumbent firms, or become managers
when they are hired by incumbents, we might expect divergent patterns in earnings andinnovative output. We explore this possibility by using occupation information matchedto our full inventor jobs panel (not limited to the hire match samples). In the ACSmatched sample, 49.8% of inventors are in technical occupations occupations while 26%
are in managerial occupations.19 Looking at the occupational composition of inventors
at incumbents versus young firms, we find inventors at young firms are more likely to
be managers and less likely to be in technical occupations than inventors at incumbent
firms. Among inventors employed by incumbents, 57% are in technical occupations.
That number is 44% for young firms. In contrast, the percent of inventors employed
by incumbents that are managers is 23%, but this figure is 31% at young firms. These
patterns are intuitive if we think inventors at young firms tend to be key employees orfounders that bring both innovative and managerial skills to the firm. In either case,these shares suggest that the diverging earnings and output measures we find are notdriven by differences in occupation.
Age and Life Cycle Effects. Despite the fact that our matching algorithms generate
a balanced age composition among both incumbent and young firm hires, it may stillbe the case that the estimated effects are primarily driven by differences among older
inventors. To explore this possibility, we fully interact our difference-in-differences specification (Equation 5) with a binary, OlderInventorie , equal to one if the inventor is greaterthan the median age among our hire-event inventors and zero otherwise. The triple interaction of Postite × Incumbentie × OlderInventorie captures the difference in the post-hireoutcomes between younger and older inventors. We find the triple interaction insignificant for all of our inventive output measures and negative and significant for earnings.
The earnings estimates are intuitive since earnings life cycle effects will tend to result inolder inventors having higher earnings, lessening the incumbent earnings premium.20
18 The standard error for the simulated self citation rate Post × Incumbent estimate is (0.0003031). See

the Online Appendix for additional estimation details.
19 Within technical occupations we include Computer and Mathematical, Architecture and Engineering,or Life, Physical, and Social Science occupations, which correspond to ACS occupation codes 1000 to 1965.
In management occupation we include Management, Business Operations, or Financial Specialists, whichcorrespond to ACS occupation codes ≤ 950.
20 See Online Appendix for estimation output.

19

Adjustment Costs at Incumbents. Another potential explanation consistent with out
findings is that the post-hire costs of integrating into incumbent firms is more significant
than for young firms. Incumbents may be less flexible to do additional layers of bureaucracy (Williamson, 1984). Incumbent firms may also have established R&D projects thatnew inventor hires must integrate with. To test this possibility, we define a new measurethat captures whether an inventor has a patent application in a technology class that is
new to the inventor in a given year. The measure, NewCPCit , is equal to 1 if an inventor
has a patent application at time t in a major CPC class that the inventor has no priorapplications in and zero otherwise. Estimation results suggest that incumbent hires are
actually less likely to submit patent applications in a new-to-the-inventor CPC class than
young firm hires.21
Hire Events in Great Recession. The structure of our hire event study requires information on earnings and patenting activity observed before and after the hire event. In
addition, citation-based outcomes are observed only through 2014 because all patents
are allowed five years after grant to accrue citations. All of these restrictions taken together mean that our hire events occur only in the years 2005 to 2012, which straddles
the great recession. To ensure our results are not primarily driven by the great recession
years we estimate our difference-in-differences specification with hire events occurringin 2005 and 2006. We again find negative effects for citations per application and positiveeffects for earnings. The earnings estimates for Post × Incumbent is a bit larger (0.1803),
and the citation estimate is more negative as well (-1.864).22

4Conclusion

Talent is the most important ingredient for innovation and the allocation of talent acrossfirms has a first-order impact on the innovative capacity of an economy. In this paper,
we develop a theory of creative destruction that focuses on the allocation of inventors
across firms. Our model describes the strategic incentives faced by incumbents. To
avoid displacement by the entrant, incumbent firms will poach inventors and shelve
their innovations.
On the empirical side, our paper uses novel data on the employment history of over
760 thousand inventors, to investigate the model’s predictions. We show empirically that(i) inventors are increasingly concentrated in large incumbents, less likely to work for
21 See Online Appendix for estimation output.
22 The standard errors for these estimates are 0.02457 for earnings and 0.5934 for citations.

20

young firms, and less likely to become entrepreneurs, (ii) when an inventor is hired byan incumbent, compared to a young firm, their earnings increases by 12.6 percent andtheir innovative output declines by 6 to 11 percent.
These findings have very important policy implications. First, looking to countrylevel aggregates of innovation inputs (e.g. R&D spending or inventors per capita) may
be misleading and fail to provide a complete picture of a country’s innovative capacity.
While having a higher share of inventors is desirable, the composition of those inventors
across firm types is also important. Second, not all factor reallocation is necessarily
good. Our analysis has documented that inventor reallocation toward large incumbents,
at least the way it happened in recent decades, might be lowering the growth capacityof the country. Finally, policies that encourage more incumbent innovation might occur
at the expense of entrant innovations, which are higher quality on average.
These important findings point to several interesting, policy-relevant questions. For
instance, what role do non-compete agreements play in explaining when inventors work
for incumbents or young firms? Those policies may play an important role in generating
spin-offs and inventor entrepreneurship. In addition, what role do financial frictions
play in explaining inventor’s choice to work for incumbents? If an inventor faces credit
constraints, that may weaken incentives to work for an entrant or start a new firm. We
hope that the data developed by Akcigit and Goldschlag (2022) will spur further researchon these topics and shed light on the “black box” of inventor employment dynamics inthe US.

21

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24

Online Appendix

Online Appendix
A.1

Inventor History Summary Statistics

Table A1 provides summary statistics for our Inventor Employment History database.
The Panel A provides characteristics and employment dynamics measures and the bottom two panels (B and C) summarize our earnings and inventive outcome measures
for inventor jobs at incumbent and young firms respectively. Observation counts arereported in thousands. The observation counts for the first three rows are the count of
unique inventors. In the remaining rows, observation counts represent the number ofquarterly job observations. For example, for Female in Table A1, there are 760 thousand
inventors, 11.7 percent of which are female. In contrast, for Age ≤25, there are nearly
34.7 million job observations, 3.4 percent of which cover inventors inventors age lessthan or equal to 25 years old.

25

Online Appendix

Table A1: Summary Statistics
Mean Std. Dev
Panel A: Characteristics and Dynamics
Female0.11680.3212Foreign Born
0.26860.4432
Ever Entrepreneur
0.08960.2856Age ≤ 25
0.03352
0.18
Age 26-35
0.20510.4038
Age 36-45
0.33590.4723
Age 46-55
0.29940.458
Age ≥ 560.126
0.3319
Incumbent Employer
0.551
0.4974Young Employer
0.10270.3036
Hires
0.04558
0.2086
Separations
0.04442
0.206
Panel B: Incumbent Employer
ln(Earnings)
10.33
0.659
Apps
0.091880.5301
Cites
0.5371
22.29
Ind Claim per App
0.15750.7996
Cite per App
4.852
15.44
Self Cite Rate
0.14290.193
Panel C: Young Employer
ln(Earnings)
10.2
0.8675Apps
0.087170.4543Cites
0.8275
10.09
Ind Claim per App
0.16340.9009
Cite per App
8.511
19.91
Self Cite Rate
0.062120.1289N (000s)
760
760
759
34,69034,69034,69034,69034,69034,69034,69034,57034,57018,32019,11015,92017,110
1,103
1,176
3,262
3,563
3,145
3,300
202
213

Source: Inventor Employment History, Founding Team Database
Notes: Observation counts have been rounded to avoid the disclosure of confidential information.

Hire Event MatchingThis analysis measures the impact on an inventor’s earnings and inventive output ofbeing hired by an incumbent firm relative to being hired by a young firm. To do this, we
identify all hire events for inventors in which the hiring firm is either an incumbent or ayoung firm. With those hire events we construct a balanced panel of annual observations
before and after the hire. The panel contains 9 years, 4 years before the event, t = 0 is hire
event quarter and the following 3 quarters, and then 4 years following the hire year. We
track measures of inventive productivity (apps, citations, etc.) and earnings aggregated26Online Appendix
to the annual level for each year (lead and lag) relative to the hire event. We aggregate
to annual measures of earnings and inventive productivity by summing applications
and citation weighted applications and taking the mean of full quarter earnings, self
citation rate, independent claim share, and citations per application. Individuals with
no applications in a given year will appear with zero applications while those with nofull quarter earnings will be have a missing earnings value for the year (and are therefore
dropped). Note that if an inventor has multiple back-to-back hire events, the relative year
measures will overlap for the different inventor-hire event combinations.
With this inventor-hire event-relative year panel we perform our matching, aiming
to find incumbent and young hire event twins—young and incumbent hire events inwhich the inventors have very similar pre-hire inventive productivity or earnings. We
perform a separate matching exercise for each of our outcome variables (applications,earnings, etc.). We create percentile bins (10 for patent measures and 50 for earnings) of
the outcome variable by relative year prior to hire (e.g. separate bins for t − 4, t − 3, etc.).
Call these percentile bins pt−4 , pt−3 , pt−2 , and pt−1 . Similarly, call the outcome measurein each relative year yt−4 , yt−3 , etc.
Records with a zero measure in the outcome variable are grouped together within arelative year, which yields 11 bins for cite measures and 51 for earnings. Inventor-hire
event pairs are dropped if (1) all outcome measures are either missing or zero for all preevent relative years. These “all zero” cases are events we know relatively little about–it isdifficult to assess the quality of an inventor or their trajectory if we observe no inventive
activity within our window around the hire event.
The matching iterates over each young firm hire event, block matching to all incumbent hire events by [year and quarter of hire, sector,pt−4 ,pt−3 ,pt−2 ,pt−1 ]. In many cases,
this yields more than one potential match for a given young hire event. To resolve this,we calculate the euclidean distance, disti,j , between the outcomes in each prior year andage. For young hire event i and incumbent hire event j, the distance is defined as follows.
q2(6)
disti,j = yDi f f t2−4,i,j + ... + yDi f f t2−1,i,j + ageDi f f i,j
The difference in each prior year, yDi f f t−k,i,j , is noramalized to fall between 0 and 1,
and is defined as follows.
abs(yt−k,i − yt−k,j ) − min( abs(yt−k,i − yt−k,j ))yDi f f t−k,i,j =∀jmax ( abs(yt−k,i − yt−k,j )) − min( abs(yt−k,i − yt−k,j ))
∀j

(7)

∀j

The normalized difference in age, ageDi f f i,j , is defined analogously to yDi f f t−k,i,j .
The matching algorithm minimizes the disti,j , breaking ties randomly. We match without
replacement—once an incumbent hire event has been matched to a young hire event it
cannot match to any other young hire event.
Table A2 shows the mean and standard deviation of pre-hire outcomes and age forthe matched young and incumbent hires along with a ttest for the difference in means.
Some differences in age are statistically significant, but all are less than one year. Noneof the differences in pre-hire outcomes are statistically significantly different.

27

Online Appendix

Table A2: Hire Event Match Balance
Variable
Applications
Age
Apps
Citations
Age
Cites
Independent Claim ShareAge
Ind Claim Share
Cites per Application
Age
Cites per App
Self Cite Rate
Age
Self Cite Rateln( Earnings)
Ageln(Earnings)

Mean
Mean
Young Incumbent

Diff

40.3
(9.613)0.6068(1.429)40.26(9.602)0.6017(1.34)-0.04(0.03262)-0.0051(0.004703)

39.93(9.543)
5.933(33.27)39.61(9.309)5.758(33.01)-0.32(0.04644)-0.175(0.1633)

40.26(9.636)
0.2947(.5795)40.22(9.556)
0.2925(.5645)-0.04(0.03499)-0.0022(0.002086)

39.91(9.551)
1.039(4.338)39.57(9.285)1.009(4.246)-0.34(0.04648)-0.03(0.02119)

38.61(10.54)0.0601(.1309)

37.98
-0.63
(10.73)(0.02219)
0.060350.00025(.132) (0.0008218)

40.18(9.48)
10.29(1.051)

40.05(9.505)10.29(1.045)-0.13(0.07345)
0(0.008108)

Source: Inventor Employment History, Founding Team Database
Notes: Match balance statistics for pre-hire characteristics from hire event study. Standard errors areshown in parentheses.

Simulated of Self Citation Rates
Table A3 shows estimation results of Equation 5 where Yite is a simulated self citation
rate in which, given the number of citations a patent makes to each CPC technology
28Online Appendix
group, a patent cite’s the assignee’s existing patent corpus in proportion to it’s share of
all patents in that CPC group.

Table A3: Simulated Self Citation Rates
Self Cite Rate
Simulated
Post-0.001016***(0.0002248)
Post x Incumbent
0.002107***(0.0003031)
R2
0.3374
N
213,000
Source: Inventor Employment History, author’s calculation
Notes: Estimates are of υ1 and υ3 from Equation 5. Standard errors are shown in parentheses. Incumbent
binary included but not reported. Observation counts have been rounded to avoid the disclosure ofconfidential information.

New to Inventor Major CPC

Table A4: New Major CPC
Post
Post x Incumbent
R2
N

New CPC-0.2013***(0.001729)-0.002968**(0.001037)
0.1499548,000

Source: Inventor Employment History, author’s calculation
Notes: Estimates are of υ1 and υ3 from Equation 5. Standard errors are shown in parentheses. Incumbent
binary included but not reported. Observation counts have been rounded to avoid the disclosure ofconfidential information. New CPC is a binary equal to one if the inventor has an application in a major
CPC technology group that the inventor is not previous observed with patent applications in that major
CPC. This measure is left censored in 2000, the beginning of our data.

Age and Life Cycle Effects

29

Table A5: Age of Inventor Heterogeneous Effects
Post
Post × Incmbnt
Post × Incmbnt × GT Median Age
R2
N

Apps
-0.0875***(0.006729)-0.03515***(0.007388)-0.002157(0.01031)
0.3794
781,000ln( Earnings) Citations Citations Independent Self Cite Rate
0.1425*** -0.7108** -0.285***-0.06288***-0.01155***(0.005931)(0.2374) (0.03404)(0.002914)(0.001388)
0.1311*** -0.7268**-0.112**-0.01578***0.02411***(0.006376)(0.2307)(0.0343)(0.003255)(0.001869)-0.02288**
0.07252 -0.003854-0.0001692-0.002262(0.008913)(0.3262) (0.04415)(0.004564)(0.00257)
0.8408143,000

0.3308371,000

0.2694370,000

30
Yite = α + +ν1 Postite + ν2 Incumbentie + ν3 GTMedit+ν4 Postite × Incumbentie + ν5 GTMedit × Postite+ν6 GTMedit × Postite × Incumbentie ++ β 4 Ageite + δj + γk + ψi + eite

0.6357
213,000
Online Appendix

Source: Inventor Employment History, author’s calculation
Notes: Estimates for ν1 , ν4 , and ν6 from the following equation.

0.2657660,000

Online Appendix

Hire Events Before Great Recession

Table A6: Hire Events Before Great Recession
CitationsPost
0.4137(0.4502)
Post × Incmbnt-1.864**(0.5934)
R2
N

0.6801
82,500ln( Earnings)-0.06049***(0.01711)
0.1803***(0.02457)
0.957623,500Source: Inventor Employment History, author’s calculation
Notes: Estimates are of υ1 and υ3 from Equation 5 using only hires that occurred in 2005 and 2006.
Standard errors are shown in parentheses. Incumbent binary included but not reported. Observation
counts have been rounded to avoid the disclosure of confidential information.

31