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. 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. 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), ]