CEOs’ uncommon names and corporate innovation

Figures & data

Table 1. Summary statistics. This table reports the summary statistics of variables used in this study. The sample consists of firm-year observations between 1992 and 2018. All continuous variables are winsorized at the 1st and 99th percentile. Panel A provides summary statistics of the full sample. Panel B compares the name uncommonness of CEO names with the first names in the general population. Panel C compares the average name uncommonness of all CEO names with that of the CEOs of 100 companies with the most patents granted. Significance level of the test of the differences between the means of the two samples is denoted next to the column “Difference”

Panel A: Full sample
Variable	N	Mean	Median	Std	P25	P75
Pat	38233	10.67	0.00	25.75	0.00	4.08
Cit	14332	19.59	9.80	33.39	3.75	21.25
Uncommonness	38233	-0.36	-0.21	0.35	-0.70	-0.06
LN_AT	38233	7.49	7.40	1.73	6.25	8.66
ROA	38233	0.12	0.12	0.16	0.07	0.18
R&D/AT	38233	0.03	0.00	0.07	0.00	0.03
Capex/AT	38233	0.05	0.04	0.05	0.02	0.07
Leverage	38233	0.23	0.21	0.22	0.06	0.35
Tobin’s Q	38233	2.00	1.48	2.10	1.11	2.21
InstHold	38233	0.55	0.65	0.35	0.26	0.84
LnAge	38233	3.04	3.09	0.75	2.56	3.69
Tangibility	38233	0.27	0.19	0.24	0.07	0.40
HHI	38233	0.76	1.00	0.28	0.50	1.00
HHI^2	38233	0.66	1.00	0.38	0.25	1.00
Panel B: Uncommonness: CEO vs. general population
Variable	CEO	General population		Difference
Mean of Uncommonness	-0.36	-0.17		-0.19	***
Panel C: Uncommonness: All CEO vs. CEO of innovative companies
Variable	All CEO	CEO of 100 companies with the most patent granted		Difference
Mean of Uncommonness	-0.36	-0.25		-0.11	***

*, ** and *** indicate statistical significance at the 10%, 5% and 1% level, respectively.

Figure 1. CEO name uncommonness by year.

This figure presents the average CEO name uncommonness (Y-axis variable) in the sample by year. Lower value (more negative) indicates more common names.

Table 2. CEO name uncommonness and corporate innovation: OLS regression. This table presents the results of multivariate regressions that estimate the effect of CEO name uncommonness on corporate innovation in a pooled OLS framework. The sample consists of firm-year observations between 1992 and 2018. I estimate the following equation: $\mathit{L}n{\left(1+Pat\right)}_{i,t+n}\left(orln{\left(1+Cite\right)}_{i,t+n}\right)=\alpha +\beta Uncommonnes{s}_{i,t}+\gamma { }^{\mathrm{\prime }}Control{s}_{i,t}+Fir{m}_i+Yea{r}_t+{\epsilon }_{i,t,}$ The dependent variable Ln(1+ Pat)_i,t+n, is the natural logarithm of one plus the number of patents filed (and eventually granted) in one (t + 1), two (t + 2), and three (t + 3) years, and results are reported in columns (1)—(3), respectively. The dependent variable LnCite_i,t+n, is the natural logarithm of one plus the average number of citations received per patent in one (t + 1), two (t + 2), and three (t + 3) years, and results are reported in columns (4)—(6), respectively. Uncommonness_i,t is an uncommonness measure of the CEO’s name constructed based on the frequency of first names in the population. Year fixed effects Year_t and firm fixed effects Firm_j are included in all regressions. All other variables are as defined in Appendix A. Standard errors clustered by CEO-firm pairs are reported in parentheses. ***, **, and * indicate significance at the 1%, 5%, and 10% level, respectively

	(1)	(2)	(3)	(4)	(5)	(6)
	Ln(1+ Pat)i,t+1	Ln(1+ Pat)i,t+2	Ln(1+ Pat)i,t+3	Ln(1+ Cite)i,t+1	Ln(1+ Cite)i,t+2	Ln(1+ Cite)i,t+3
Uncommonness	0.075***	0.074***	0.054**	0.025	0.011	0.006
	(0.022)	(0.023)	(0.024)	(0.028)	(0.028)	(0.030)
LN_AT	0.176***	0.158***	0.135***	−0.012	−0.031	−0.045**
	(0.013)	(0.014)	(0.014)	(0.020)	(0.021)	(0.021)
ROA	0.030	0.040	0.043	0.029	−0.049	0.161
	(0.039)	(0.048)	(0.054)	(0.093)	(0.089)	(0.108)
R&D/AT	0.406**	0.347*	0.168	0.152	−0.093	−0.149
	(0.199)	(0.200)	(0.214)	(0.198)	(0.206)	(0.217)
Capex/AT	0.157	0.085	−0.022	0.192	0.231	−0.097
	(0.114)	(0.117)	(0.126)	(0.315)	(0.326)	(0.343)
Leverage	−0.195***	−0.225***	−0.224***	0.022	0.004	−0.048
	(0.037)	(0.041)	(0.042)	(0.066)	(0.079)	(0.091)
Tobin’s Q	0.007**	0.007**	0.007**	0.007**	0.008***	0.003
	(0.003)	(0.003)	(0.003)	(0.003)	(0.003)	(0.003)
InstHold	−0.034	−0.002	0.048	0.018	0.022	−0.022
	(0.030)	(0.033)	(0.035)	(0.050)	(0.051)	(0.054)
LnAge	0.069*	0.053	0.048	−0.248***	−0.231***	−0.189***
	(0.036)	(0.038)	(0.041)	(0.048)	(0.051)	(0.055)
Tangibility	0.308***	0.257***	0.296***	−0.105	−0.041	−0.097
	(0.086)	(0.088)	(0.091)	(0.179)	(0.162)	(0.174)
HHI	−0.151	−0.022	0.072	−0.097	0.190	0.046
	(0.202)	(0.204)	(0.206)	(0.256)	(0.256)	(0.274)
HHI^2	0.072	0.006	−0.039	0.102	−0.128	−0.027
	(0.146)	(0.149)	(0.151)	(0.189)	(0.190)	(0.203)
Constant	−0.544***	−0.390***	−0.258*	3.170***	3.144***	3.143***
	(0.147)	(0.149)	(0.154)	(0.207)	(0.217)	(0.231)
Year fixed effect	Yes	Yes	Yes	Yes	Yes	Yes
Firm fixed effect	Yes	Yes	Yes	Yes	Yes	Yes
Observations	38,233	35,095	32,084	14,332	13,240	12,163
Adjusted R^2	0.864	0.868	0.873	0.681	0.680	0.672

Table 3. Difference-in-differences test. This table reports the results of a differences-in-differences test on how patents produced in a firm are affected following the departure of a CEO with an uncommon name. Using a sample of all firms that experienced a sudden death of its CEO over the entire sample period of 1992–2018, I retain firm-year observations for a five-year window centered in the CEO death year and estimate the following model: $\mathit{L}n{\left(1+Pat\right)}_{i,t}\left(orln{\left(1+Cite\right)}_{i,t}\right)=\alpha +{\beta }_{1}Trea{t}_i\times Pos{t}_t+{\beta }_{2}Trea{t}_i+{\beta }_{3}Pos{t}_i+\gamma { }^{\mathrm{\prime }}Control{s}_{i,t}+Industr{y}_i+Yea{r}_t+{\epsilon }_{i,t,}$ where Treat_i is a dummy that equals one for treatment firms (departing CEO’s name uncommonness is above sample median) and zero for control firms (departing CEO’s name uncommonness is below sample median). Post_t is a dummy variable that equals one for years on or after the CEO’s departure. Year fixed effects Year_t and industry fixed effects Industry_i are included in all regressions. All other variables are as defined in Appendix A. Standard errors clustered by CEO-firm pair are reported in parentheses. ***, **, and * indicate significance at the 1%, 5%, and 10% level, respectively

	(1)	(2)
	Ln(1+ Pat)	Ln(1+ Cite)
Treat×Post	−0.333**	0.206
	(0.159)	(0.221)
Treat	0.097	0.194
	(0.215)	(0.207)
Post	−0.050	−0.118
	(0.107)	(0.171)
LN_AT	0.624***	0.122
	(0.081)	(0.090)
ROA	0.439	3.991**
	(0.951)	(1.676)
R&D/AT	9.785*	3.467
	(5.204)	(2.147)
Capex/AT	−1.528	−0.025
	(1.271)	(2.302)
Leverage	−0.304	−0.525
	(0.571)	(1.140)
Tobin’s Q	0.196***	−0.050
	(0.060)	(0.056)
InstHold	−0.365	0.612
	(0.315)	(0.431)
LnAge	0.603***	−0.041
	(0.158)	(0.188)
Tangibility	1.772***	0.490
	(0.600)	(0.775)
HHI	−4.651**	0.736
	(2.246)	(1.867)
HHI^2	3.350**	−0.884
	(1.579)	(1.431)
Constant	−5.049***	0.469
	(1.148)	(1.288)
Year fixed effect	Yes	Yes
Industry fixed effect	Yes	Yes
Observations	319	114
Adjusted R^2	0.797	0.723

Figure 2. Dynamics of innovation output around the death of uncommonly named CEO.

This figure plots the dynamic effects of the death of an uncommonly named CEO on the patent count in Panel A and citations received per patent in Panel B. The x-axis shows the time relative to CEO’s death (time 0), where the year before the CEO’s death is used as the base year (time –1). On the y-axis, the graph plots the coefficient estimates of the interaction between timedummy variables and treatment dummy in the regression as specified in Equation (3)(3) $\begin{array}{rl}& Ln{\left(1+Pat\right)}_{i,t}\left(orln{\left(1+Cite\right)}_{i,t}\right)=\alpha +{\beta }_{1}Trea{t}_i\times Befor{e}_t^{-2}+{\beta }_{2}Trea{t}_i\times Curren{t}_t+{\beta }_{3}Trea{t}_i\times Afte{r}_t^1\\ & +{\beta }_{4}Trea{t}_i\times Afte{r}_t^2+\gamma { }^{\mathrm{\prime }}Control{s}_{i,t}+Industr{y}_i+Yea{r}_t+{\epsilon }_{i,t,}\end{array}$(3) . The solid vertical bars are the 90% confidence intervals of the coefficient estimates. Confidence intervals are calculated from standard errors clustered by CEO-firm pair.

Table 4. Investment in innovation. This table shows the effect of CEO name uncommonness on corporate innovation input measures. Innovation inputs are measured by two variables. The first measure R&D/AT is R&D expenditures divided by total assets and the results are reported in columns (1)-(3). The second measure is Asset Growth, the annual asset growth rate, and the results are reported in columns (4)-(6). All other variables are as defined in Appendix A. Standard errors clustered by CEO-firm pair are reported in parentheses. ***, **, and * indicate significance at the 1%, 5%, and 10% level, respectively

	(1)	(2)	(3)	(4)	(5)	(6)
	R&D/AT i,t+1	R&D/AT i,t+2	R&D/AT i,t+3	Asset Growthi,t+1	Asset Growthi,t+2	Asset Growthi,t+3
Uncommonness	0.002**	0.001*	0.002**	−0.002	0.004	−0.000
	(0.001)	(0.001)	(0.001)	(0.008)	(0.008)	(0.008)
LN_AT	−0.008***	−0.004***	−0.004***	−0.162***	−0.169***	−0.153***
	(0.001)	(0.001)	(0.001)	(0.009)	(0.007)	(0.008)
ROA	−0.008	0.011*	0.007	0.253***	0.245***	0.096***
	(0.011)	(0.006)	(0.007)	(0.084)	(0.036)	(0.036)
R&D/AT	0.019	0.042	−0.001	0.249	0.342***	0.168
	(0.045)	(0.051)	(0.022)	(0.166)	(0.079)	(0.108)
Capex/AT	0.005	−0.005	−0.012	0.323***	0.138**	0.147*
	(0.007)	(0.007)	(0.008)	(0.073)	(0.066)	(0.086)
Leverage	−0.003	−0.009	−0.004	−0.232***	−0.142***	−0.079***
	(0.007)	(0.006)	(0.005)	(0.038)	(0.025)	(0.026)
Tobin’s Q	−0.000	0.000	0.000	0.069***	−0.001	−0.003
	(0.000)	(0.000)	(0.000)	(0.008)	(0.003)	(0.002)
InstHold	0.000	0.001	0.003***	−0.039***	−0.034***	−0.015
	(0.001)	(0.001)	(0.001)	(0.015)	(0.013)	(0.013)
LnAge	0.004**	0.002	0.001	−0.041**	0.002	0.015
	(0.001)	(0.002)	(0.002)	(0.019)	(0.014)	(0.014)
Tangibility	0.025***	0.012**	0.008**	−0.122**	−0.057	−0.009
	(0.005)	(0.005)	(0.004)	(0.051)	(0.042)	(0.042)
HHI	0.004	0.006	0.002	0.004	0.033	0.160**
	(0.005)	(0.006)	(0.007)	(0.089)	(0.086)	(0.079)
HHI^2	−0.003	−0.003	−0.001	−0.025	−0.018	−0.095*
	(0.004)	(0.004)	(0.005)	(0.063)	(0.062)	(0.058)
Constant	0.073***	0.052***	0.052***	2.388***	2.373***	2.150***
	(0.007)	(0.007)	(0.006)	(0.101)	(0.071)	(0.068)
Year fixed effect	Yes	Yes	Yes	Yes	Yes	Yes
Firm fixed effect	Yes	Yes	Yes	Yes	Yes	Yes
Observations	38,233	35,095	32,082	38,185	35,017	31,991
Adjusted R^2	0.794	0.801	0.802	0.236	0.131	0.111

Table 5. Patents in known vs unknown classes. This table shows the effect of CEO name uncommonness on the number of patents in previously known vs unknown areas. The dependent variable Ln(1+ Known) is the natural logarithm of the number of patents in technology classes that the firm has applied for and eventually granted before a given year. The dependent variable Ln(1+ Unknown) is the natural logarithm of the number of patents in technology classes that the firm has never applied for and granted before a given year. All other variables are as defined in Appendix A. Standard errors clustered by CEO-firm pair are reported in parentheses. ***, **, and * indicate significance at the 1%, 5%, and 10% level, respectively

	(1)	(2)	(3)	(4)	(5)	(6)
	Ln(1+ Known)i,t+1	Ln(1+ Known)i,t+2	Ln(1+ Known)i,t+3	Ln(1+ Unknown)i,t+1	Ln(1+ Unknown)i,t+2	Ln(1+ Unknown)i,t+3
Uncommonness	0.093***	0.096***	0.082***	−0.009	−0.013	−0.018
	(0.023)	(0.024)	(0.025)	(0.011)	(0.012)	(0.012)
LN_AT	0.180***	0.174***	0.160***	0.033***	0.020***	0.006
	(0.015)	(0.016)	(0.017)	(0.006)	(0.007)	(0.007)
ROA	0.018	0.027	0.020	0.000	0.013	0.023
	(0.040)	(0.048)	(0.052)	(0.018)	(0.023)	(0.024)
R&D/AT	0.483**	0.337*	0.271	−0.018	0.069	−0.080
	(0.198)	(0.191)	(0.216)	(0.092)	(0.095)	(0.081)
Capex/AT	0.153	0.109	−0.027	0.065	0.044	0.063
	(0.116)	(0.121)	(0.133)	(0.072)	(0.073)	(0.073)
Leverage	−0.153***	−0.204***	−0.229***	−0.082***	−0.071***	−0.052**
	(0.039)	(0.045)	(0.049)	(0.018)	(0.020)	(0.022)
Tobin’s Q	−0.003	−0.003	−0.002	0.009***	0.009***	0.007***
	(0.003)	(0.003)	(0.003)	(0.002)	(0.002)	(0.002)
InstHold	−0.100***	−0.069**	−0.008	0.040**	0.050***	0.048***
	(0.031)	(0.033)	(0.035)	(0.016)	(0.017)	(0.018)
LnAge	0.049	0.023	0.006	0.021	0.032	0.048**
	(0.037)	(0.039)	(0.041)	(0.019)	(0.019)	(0.020)
Tangibility	0.039	0.017	0.056	0.320***	0.288***	0.284***
	(0.097)	(0.098)	(0.104)	(0.042)	(0.044)	(0.046)
HHI	−0.119	−0.085	−0.060	−0.030	0.099	0.178
	(0.206)	(0.210)	(0.216)	(0.109)	(0.114)	(0.122)
HHI^2	0.016	0.009	−0.003	0.045	−0.032	−0.069
	(0.149)	(0.153)	(0.158)	(0.078)	(0.082)	(0.088)
Constant	−0.512***	−0.375**	−0.246	−0.227***	−0.215***	−0.204**
	(0.172)	(0.176)	(0.180)	(0.077)	(0.082)	(0.086)
Year fixed effect	Yes	Yes	Yes	Yes	Yes	Yes
Firm fixed effect	Yes	Yes	Yes	Yes	Yes	Yes
Observations	38,233	35,095	32,084	38,233	35,095	32,084
Adjusted R^2	0.842	0.848	0.855	0.471	0.468	0.466

Table 6. Patents proximity and economic value. This table shows the effect of CEO name uncommonness on the patent technological proximity between newly filed patents and those filed before as well as the patent’s economic value. The dependent variable Proximity is a continuous measure of patent technological proximity between newly filed patents and those filed before (Balsmeier et al., 2017; Jaffe, 1989). The dependent variable ${\stackrel{\mathbf{‾}}{\mathit{D}\mathit{o}\mathit{l}\mathit{l}\mathit{a}\mathit{r}\mathit{V}\mathit{a}\mathit{l}\mathit{u}\mathit{e}}}_{\mathit{t}\mathbf{+}\mathbf{1}\mathbf{\to }\mathit{t}\mathbf{+}\mathbf{3}}$ is the moving average of the economic value for the patents filed in a three-year window (t + 1 to t + 3) based on the patent value data compiled by (Kogan et al., 2017). All other variables are as defined in Appendix A. Standard errors clustered by CEO-firm pair are reported in parentheses. ***, **, and * indicate significance at the 1%, 5%, and 10% level, respectively

	(1)	(2)	(3)	(4)
	Proximityi,t+1	Proximityi,t+2	Proximityi,t+3	${\stackrel{\mathbf{‾}}{\mathit{D}\mathit{o}\mathit{l}\mathit{l}\mathit{a}\mathit{r}\mathit{V}\mathit{a}\mathit{l}\mathit{u}\mathit{e}}}_{\mathit{t}\mathbf{+}\mathbf{1}\mathbf{\to }\mathit{t}\mathbf{+}\mathbf{3}}$ 1
Uncommonness	0.010**	0.012**	0.010*	−4.321*
	(0.004)	(0.005)	(0.005)	(2.309)
LN_AT	−0.001	−0.005**	−0.006**	0.320
	(0.002)	(0.002)	(0.002)	(1.513)
ROA	−0.010	−0.005	−0.004	26.653***
	(0.008)	(0.010)	(0.009)	(5.726)
R&D/AT	0.008	−0.023	0.035	39.496***
	(0.039)	(0.042)	(0.039)	(9.467)
Capex/AT	0.073**	0.086**	0.061	−0.710
	(0.033)	(0.034)	(0.037)	(20.295)
Leverage	−0.000	−0.009	−0.018*	16.459***
	(0.007)	(0.009)	(0.010)	(3.595)
Tobin’s Q	−0.001	0.000	0.001	0.711***
	(0.001)	(0.001)	(0.001)	(0.177)
InstHold	0.002	0.006	0.007	−0.578
	(0.006)	(0.006)	(0.007)	(4.482)
LnAge	−0.027***	−0.029***	−0.027***	−24.101***
	(0.006)	(0.006)	(0.007)	(4.155)
Tangibility	−0.046***	−0.046**	−0.051**	1.235
	(0.018)	(0.019)	(0.020)	(11.218)
HHI	0.035	−0.013	−0.051	3.238
	(0.044)	(0.047)	(0.048)	(18.103)
HHI^2	−0.010	0.022	0.042	1.046
	(0.032)	(0.034)	(0.035)	(13.872)
Constant	0.976***	1.028***	1.049***	108.678***
	(0.027)	(0.027)	(0.029)	(15.618)
Year fixed effect	Yes	Yes	Yes	Yes
Firm fixed effect	Yes	Yes	Yes	Yes
Observations	38,233	35,095	32,084	15,307
Adjusted R^2	0.358	0.359	0.356	0.631

Table 7. Citation distributions. This table shows the effect of CEO name uncommonness on the number of patents with different scientific impacts based on the citation distributions. Specifically, I sort patents into three groups based on the citation distribution of the patents filed in the same technological class in a given year. The dependent variable Ln(1+ High Impact) is the natural logarithm of one plus the number of patents that belong to the top quartile of citation distributions, and the results are presented in Panel A. Ln(1+ Mid Impact) is the natural logarithm of one plus the number of patents that belong to the middle 50% of citation distributions, and the results are presented in Panel B. Ln(1+ Low Impact) is the natural logarithm of one plus the number of patents that belong to the bottom quartile of citation distributions, and the results are presented in Panel C. All control variables in Equation (1)(1) $Ln{\left(1+Pat\right)}_{i,t+n}\left(orln{\left(1+Cite\right)}_{i,t+n}\right)=\alpha +\beta Uncommonnes{s}_{i,t}+\gamma { }^{\mathrm{\prime }}Control{s}_{i,t}+Fir{m}_i+Yea{r}_t+{\epsilon }_{i,t,}$(1) and year fixed effects and firm fixed effects are included. All other variables are as defined in Appendix A. Standard errors clustered by CEO-firm pair are reported in parentheses. ***, **, and * indicate significance at the 1%, 5%, and 10% level, respectively

Panel A: High-impact patents
	(1)	(2)			(3)
	Ln(1+ High Impact)i,t+1	Ln(1+ High Impact)i,t+2			Ln(1+ High Impact)i,t+3
Uncommonness	0.040	0.041			0.035
	(0.025)	(0.026)			(0.027)
Controls	Yes	Yes			Yes
Year fixed effect	Yes	Yes			Yes
Firm fixed effect	Yes	Yes			Yes
Observations	38,233	35,095			32,084
Adjusted R^2	0.851	0.856			0.861
Panel B: Mid-impact patents
	(1)	(2)			(3)
	Ln(1+ Mid Impact)i,t+1	Ln(1+ Mid Impact)i,t+2			Ln(1+ Mid Impact)i,t+3
Uncommonness	0.001	−0.001			−0.002
	(0.034)	(0.035)			(0.036)
Controls	Yes	Yes			Yes
Year fixed effect	Yes	Yes			Yes
Firm fixed effect	Yes	Yes			Yes
Observations	38,233	35,095			32,084
Adjusted R^2	0.748	0.750			0.750
Panel C: Low-impact patents
	(1)	(2)		(3)
	Ln(1+ Low Impact)i,t+1	Ln(1+ Low Impact)i,t+2		Ln(1+ Low Impact)i,t+3
Uncommonness	0.096***	0.105***		0.101***
	(0.033)	(0.036)		(0.037)
Controls	Yes	Yes		Yes
Year fixed effect	Yes	Yes		Yes
Firm fixed effect	Yes	Yes		Yes
Observations	38,233	35,095		32,084
Adjusted R^2	0.773	0.785		0.797

Table

Variables	Definition
Measures of Innovation
Pat	The number of patents applied by a given company (and eventually granted by USPTO) in a given year, adjusted for truncation
Cite	The number of forward citations received per patent in a given year, adjusted for truncation
Firm Characteristics
Uncommonness	The negative value of the frequency of the CEO’s first name scaled by the frequency of the most popular name by gender in the “National Data on the relative frequency of given names in the population of U.S. births” between 1880 and 2020
LN_AT	Natural logarithm of book value of assets
ROA	The income before extraordinary items divided by the book value of total assets
R&D/AT	The ratio of R&D expenditures scaled by total assets, set to zero if missing
Capex/AT	Capital expenditure scaled by book value of total assets
Leverage	Book value of debt divided by total assets
Tobin’s Q	The market value of assets divided by book value of total assets
InstHold	The percent of institutional holding of a firm reported in form 13 F, computed as the average of the values across four quarters.
LnAge	Natural logarithm of the number of years since a firm’s first appearance on SEC filings
Tangibility	Property, plant and equipment divided by the book value of assets
HHI	Herfindahl index of the industry of a given firm, based on sales. Industry is defined by 4-digit SIC code.
Asset Growth	The annual growth rate of asset, calculated as the total assets in the current year scaled by the total assets in the year before minus one.
Known	Number of patents in the technological classes where the firm has applied for and eventually granted before a given year.
Unknown	The number of patents in technology classes where the firm has never applied for and granted before a given year
Proximity	The technology proximity measure computed using the following equation, following Balsmeier et al. (2017) and Jaffe (1989):$\mathrm{P}\mathrm{r}\mathrm{o}\mathrm{x}\mathrm{i}\mathrm{m}\mathrm{i}\mathrm{t}{\mathrm{y}}_{it}=\frac{{\sum }_{n=1}^N{f}_{int}{f_{int}}_{-1}}{{\left({\sum }_{n=1}^N{f_{int}}^2{\sum }_{n=1}^N{f}_{int}{{ }_{-1}}^2\right)}^{\frac{1}{2}}}$, where $f_{int}$ is the percentage of patents in technological class n filed by firm i in year t, and ${f_{int}}_{-1}$ is the percentage of patents in technological class n filed by firm i before year t.
${\overline{DollarValue}}_{t+1\to t+3}$	The moving average of the economic value for the patents filed in a three-year window (t + 1 to t + 3). The dollar value of patents is obtained from Kogan et al. (2017)
High Impact	The number of patents that belong to the top quartile of citation distributions of all patents filed in the same technological class in a given year
Med Impact	The number of patents that belong to the middle 50% of citation distributions of all patents filed in the same technological class in a given year
Low Impact	The number of patents that belong to the bottom quartile of citation distributions of all patents filed in the same technological class in a given year

Table

Panel A: Uncommonness since 1992
	(1)	(2)	(3)	(4)	(5)	(6)
	Ln(1+ Pat)i,t+1	Ln(1+ Pat)i,t+2	Ln(1+ Pat)i,t+3	Ln(1+ Cite)i,t+1	Ln(1+ Cite)i,t+2	Ln(1+ Cite)i,t+3
Uncommonness_Sicne1992	0.089***	0.087***	0.071**	−0.003	−0.021	−0.033
	(0.029)	(0.030)	(0.031)	(0.036)	(0.035)	(0.038)
Controls and fixed effects	Yes	Yes	Yes	Yes	Yes	Yes
Observations	38,233	35,095	32,084	14,332	13,240	12,163
Adjusted R^2	0.864	0.868	0.873	0.682	0.681	0.673
Panel B: Uncommonness since the birth year of the oldest CEO
	(1)	(2)	(3)	(4)	(5)	(6)
	Ln(1+ Pat)i,t+1	Ln(1+ Pat)i,t+2	Ln(1+ Pat)i,t+3	Ln(1+ Cite)i,t+1	Ln(1+ Cite)i,t+2	Ln(1+ Cite)i,t+3
Uncommonness_Sicne1992	0.076***	0.076***	0.055**	0.026	0.010	0.007
	(0.023)	(0.023)	(0.024)	(0.028)	(0.028)	(0.030)
Controls and fixed effects	Yes	Yes	Yes	Yes	Yes	Yes
Observations	38,233	35,095	32,084	14,332	13,240	12,163
Adjusted R^2	0.864	0.868	0.873	0.682	0.681	0.673

Balsmeier, B., Fleming, L., & Manso, G. (2017). Independent boards and innovation. Journal of Financial Economics, 123(3), 536–557. https://doi.org/10.1016/j.jfineco.2016.12.005

 Web of Science ®Google Scholar

Jaffe, A. B. (1989). Characterizing the “technological position” of firms, with application to quantifying technological opportunity and research spillovers. Research Policy, 18(2), 87–97. https://doi.org/10.1016/0048-7333(89)90007-3

 Web of Science ®Google Scholar

Kogan, L., Papanikolaou, D., Seru, A., & Stoffman, N. (2017). Technological innovation, resource allocation, and growth. The Quarterly Journal of Economics, 132(2), 665–712. https://doi.org/10.1093/qje/qjw040

 Web of Science ®Google Scholar