InstInnovation: Innovation and Institutional Ownership
In sandwich: Robust Covariance Matrix Estimators
InstInnovation R Documentation
Innovation and Institutional Ownership
Description
Firm-level panel data on innovation and institutional ownership from 1991 to
1999 over 803 firms. The observations refer to different firms over different years.
Usage
data("InstInnovation")
Format
A data frame containing 6208 observations on 25 variables.
- company
factor. Company names.
- sales
numeric. Sales (in millions of dollars).
- acompetition
numeric. Constant inverse Lerner index.
- competition
numeric. Varying inverse Lerner index.
- capital
numeric. Net stock of property, plant, and equipment.
- cites
integer. Future cite-weighted patents.
- precites
numeric. Presample average of cite-weighted patents.
- dprecites
factor. Indicates zero precites.
- patents
integer. Granted patents.
- drandd
factor. Indicates a zero R&D stock.
- randd
numeric. R&D stock (in millions of dollars).
- employment
numeric. Employment (in 1000s).
- sp500
factor. Membership of firms in the S&P500 index.
- tobinq
numeric. Tobin's q.
- value
numeric. Stock market value.
- institutions
numeric. Proportion of stock owned by institutions.
- industry
factor. Four-digit industry code.
- year
factor. Estimation period.
- top1
numeric. Share of the largest institution.
- quasiindexed
numeric. Share of "quasi-indexed" institutional owners.
- nonquasiindexed
numeric. Share of "non-quasi-indexed"
institutional owners.
- transient
numeric. Share of "transient" institutional owners.
- dedicated
numeric. Share of "dedicated" institutional owners.
- competition4
numeric. Varying inverse Lerner index in the firm's four-digit industry.
- subsample
factor. Subsample for the replication of columns
1–5 from Table 4 in Aghion et al. (2013).
Details
Aghion et al. (2013) combine several firm level panel datasets (e.g.,
USPTO, SEC and Compustat) to examine the role of institutional investors
in the governance of innovation. Their baseline to model innovation is the Poisson model,
but they also consider negative binomial models.
Berger et al. (2017) argue that nonlinearities in the innovation process emerge in case that the first
innovation is especially hard to obtain in comparison to succeeding innovations.
Then, hurdle models offer a useful way that allows for a distinction between these two processes.
Berger et al. (2017) show that an extended analysis with negative binomial hurdle models
differs materially from the outcomes of the single-equation Poisson
approach of Aghion et al. (2013).
Institutional ownership (institutions) is defined as the proportion of
stock owney by institutions. According to Aghion et al. (2013), an
institutional owner is defined as an institution that files a Form 13-F
with the Securities and Exchange Commission (SEC).
Future cite-weighted patents (cites) are used as a proxy for
innovation. They are calculated using ultimately granted patent, dated
by year of application, and weight these by future citations through
2002 (see Aghion et al. (2013)).
The presample average of cite-weighted patents (precites) is used by Aghion et
al. (2013) as a proxy for unobserved heterogeneity, employing the
"presample mean scaling" method of Blundell et al. (1999).
The inverse Lerner index in the firm's three-digit industry is used
as a time-varying measure for product market competition (competition), where the Lerner is
calculated as the median gross margin from the entire Compustat database
in the firm's three-digit industry (see Aghion et al. (2013)).
A time-invariant measure for competition (acompetition) is constructed by averaging the
Lerner over the sample period.
The classification of institutions into "quasiindexed", "transient" and
"dedicated" follows Bushee (1998) and distinguishes between institutional
investors based on their type of investing. Quasiindexed institutions are
do not trade much and are widely diversified, dedicated institution do
not trade much and have more concentrated holdings, and transient
institutions often trade and have diversified holdings (see Aghion et
al. (2013) and Bushee (1998)).
Source
Data and online appendix of Aghion et al. (2013).
References
Aghion P, Van Reenen J, Zingales L (2013).
“Innovation and Institutional Ownership.”
The American Economic Review, 103(1), 277–304.
\Sexpr[results=rd]{tools:::Rd_expr_doi("10.1257/aer.103.1.277")}
Berger S, Stocker H, Zeileis A (2017).
“Innovation and Institutional Ownership Revisited: An Empirical Investigation with Count Data Models.”
Empirical Economics, 52(4), 1675–1688.
\Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/s00181-016-1118-0")}
Blundell R, Griffith R, Van Reenen J (1999).
“Market Share, Market Value and Innovation in a Panel of
British Manufacturing Firms.”
Review of Economic Studies, 66(3), 529–554.
Bushee B (1998).
“The Influence of Institutional Investors on Myopic R&D Investment Behavior.”
Accounting Review, 73(3), 655–679.
Examples
## Poisson models from Table I in Aghion et al. (2013)
## load data set
data("InstInnovation", package = "sandwich")
## log-scale variable
InstInnovation$lograndd <- log(InstInnovation$randd)
InstInnovation$lograndd[InstInnovation$lograndd == -Inf] <- 0
## regression formulas
f1 <- cites ~ institutions + log(capital/employment) + log(sales) + industry + year
f2 <- cites ~ institutions + log(capital/employment) + log(sales) +
industry + year + lograndd + drandd
f3 <- cites ~ institutions + log(capital/employment) + log(sales) +
industry + year + lograndd + drandd + dprecites + log(precites)
## Poisson models
tab_I_3_pois <- glm(f1, data = InstInnovation, family = poisson)
tab_I_4_pois <- glm(f2, data = InstInnovation, family = poisson)
tab_I_5_pois <- glm(f3, data = InstInnovation, family = poisson)
## one-way clustered covariances
vCL_I_3 <- vcovCL(tab_I_3_pois, cluster = ~ company)
vCL_I_4 <- vcovCL(tab_I_4_pois, cluster = ~ company)
vCL_I_5 <- vcovCL(tab_I_5_pois, cluster = ~ company)
## replication of columns 3 to 5 from Table I in Aghion et al. (2013)
cbind(coef(tab_I_3_pois), sqrt(diag(vCL_I_3)))[2:4, ]
cbind(coef(tab_I_4_pois), sqrt(diag(vCL_I_4)))[c(2:4, 148), ]
cbind(coef(tab_I_5_pois), sqrt(diag(vCL_I_5)))[c(2:4, 148), ]
sandwich documentation built on Sept. 16, 2024, 3:08 a.m.
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| InstInnovation | R Documentation |
Innovation and Institutional Ownership
Description
Firm-level panel data on innovation and institutional ownership from 1991 to 1999 over 803 firms. The observations refer to different firms over different years.
Usage
data("InstInnovation")Format
A data frame containing 6208 observations on 25 variables.
- company
factor. Company names.
- sales
numeric. Sales (in millions of dollars).
- acompetition
numeric. Constant inverse Lerner index.
- competition
numeric. Varying inverse Lerner index.
- capital
numeric. Net stock of property, plant, and equipment.
- cites
integer. Future cite-weighted patents.
- precites
numeric. Presample average of cite-weighted patents.
- dprecites
factor. Indicates zero precites.
- patents
integer. Granted patents.
- drandd
factor. Indicates a zero R&D stock.
- randd
numeric. R&D stock (in millions of dollars).
- employment
numeric. Employment (in 1000s).
- sp500
factor. Membership of firms in the S&P500 index.
- tobinq
numeric. Tobin's q.
- value
numeric. Stock market value.
- institutions
numeric. Proportion of stock owned by institutions.
- industry
factor. Four-digit industry code.
- year
factor. Estimation period.
- top1
numeric. Share of the largest institution.
- quasiindexed
numeric. Share of "quasi-indexed" institutional owners.
- nonquasiindexed
numeric. Share of "non-quasi-indexed" institutional owners.
- transient
numeric. Share of "transient" institutional owners.
- dedicated
numeric. Share of "dedicated" institutional owners.
- competition4
numeric. Varying inverse Lerner index in the firm's four-digit industry.
- subsample
factor. Subsample for the replication of columns 1–5 from Table 4 in Aghion et al. (2013).
Details
Aghion et al. (2013) combine several firm level panel datasets (e.g., USPTO, SEC and Compustat) to examine the role of institutional investors in the governance of innovation. Their baseline to model innovation is the Poisson model, but they also consider negative binomial models. Berger et al. (2017) argue that nonlinearities in the innovation process emerge in case that the first innovation is especially hard to obtain in comparison to succeeding innovations. Then, hurdle models offer a useful way that allows for a distinction between these two processes. Berger et al. (2017) show that an extended analysis with negative binomial hurdle models differs materially from the outcomes of the single-equation Poisson approach of Aghion et al. (2013).
Institutional ownership (institutions) is defined as the proportion of stock owney by institutions. According to Aghion et al. (2013), an institutional owner is defined as an institution that files a Form 13-F with the Securities and Exchange Commission (SEC).
Future cite-weighted patents (cites) are used as a proxy for innovation. They are calculated using ultimately granted patent, dated by year of application, and weight these by future citations through 2002 (see Aghion et al. (2013)).
The presample average of cite-weighted patents (precites) is used by Aghion et al. (2013) as a proxy for unobserved heterogeneity, employing the "presample mean scaling" method of Blundell et al. (1999).
The inverse Lerner index in the firm's three-digit industry is used as a time-varying measure for product market competition (competition), where the Lerner is calculated as the median gross margin from the entire Compustat database in the firm's three-digit industry (see Aghion et al. (2013)). A time-invariant measure for competition (acompetition) is constructed by averaging the Lerner over the sample period.
The classification of institutions into "quasiindexed", "transient" and "dedicated" follows Bushee (1998) and distinguishes between institutional investors based on their type of investing. Quasiindexed institutions are do not trade much and are widely diversified, dedicated institution do not trade much and have more concentrated holdings, and transient institutions often trade and have diversified holdings (see Aghion et al. (2013) and Bushee (1998)).
Source
Data and online appendix of Aghion et al. (2013).
References
Aghion P, Van Reenen J, Zingales L (2013). “Innovation and Institutional Ownership.” The American Economic Review, 103(1), 277–304. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1257/aer.103.1.277")}
Berger S, Stocker H, Zeileis A (2017). “Innovation and Institutional Ownership Revisited: An Empirical Investigation with Count Data Models.” Empirical Economics, 52(4), 1675–1688. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/s00181-016-1118-0")}
Blundell R, Griffith R, Van Reenen J (1999). “Market Share, Market Value and Innovation in a Panel of British Manufacturing Firms.” Review of Economic Studies, 66(3), 529–554.
Bushee B (1998). “The Influence of Institutional Investors on Myopic R&D Investment Behavior.” Accounting Review, 73(3), 655–679.
Examples
## Poisson models from Table I in Aghion et al. (2013)
## load data set
data("InstInnovation", package = "sandwich")
## log-scale variable
InstInnovation$lograndd <- log(InstInnovation$randd)
InstInnovation$lograndd[InstInnovation$lograndd == -Inf] <- 0
## regression formulas
f1 <- cites ~ institutions + log(capital/employment) + log(sales) + industry + year
f2 <- cites ~ institutions + log(capital/employment) + log(sales) +
industry + year + lograndd + drandd
f3 <- cites ~ institutions + log(capital/employment) + log(sales) +
industry + year + lograndd + drandd + dprecites + log(precites)
## Poisson models
tab_I_3_pois <- glm(f1, data = InstInnovation, family = poisson)
tab_I_4_pois <- glm(f2, data = InstInnovation, family = poisson)
tab_I_5_pois <- glm(f3, data = InstInnovation, family = poisson)
## one-way clustered covariances
vCL_I_3 <- vcovCL(tab_I_3_pois, cluster = ~ company)
vCL_I_4 <- vcovCL(tab_I_4_pois, cluster = ~ company)
vCL_I_5 <- vcovCL(tab_I_5_pois, cluster = ~ company)
## replication of columns 3 to 5 from Table I in Aghion et al. (2013)
cbind(coef(tab_I_3_pois), sqrt(diag(vCL_I_3)))[2:4, ]
cbind(coef(tab_I_4_pois), sqrt(diag(vCL_I_4)))[c(2:4, 148), ]
cbind(coef(tab_I_5_pois), sqrt(diag(vCL_I_5)))[c(2:4, 148), ]
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