R&D Activity and Acquisitions in High Technology Industries: Evidence from the

U.S. Electronic and Electrical Equipment Industries

Bruce A. Blonigen

 Department of Economics 

 1285 University of Oregon 

 Eugene, OR 97403-1285 

bruceb@oregon.uoregon.edu

(541) 346-4680 

Christopher T. Taylor

Antitrust Division, Bureau of Economics

 Federal Trade Commission

Washington, DC 20580

ctaylor@ftc.gov

 (202) 326-2997

Abstract: Theory argues that R&D intensity and acquisition activity may be either directly or inversely

related. However, empirically we know relatively little about which firms are responsible for acquisition

activity in high-technology industries. Using a panel of 217 U.S. electronic and electrical equipment

firms from 1985-93 and limited dependent variable estimation techniques, we find relatively low R&Dintensity firms are more likely to acquire. This result is true both when looking at between and within

estimators, indicating that acquisitions may be used as a short term or long term strategy. These results

are robust to a number of sensitivity test.

JEL Classification: L21, O32, L63

* We thank Lee Branstetter, Shane Greenstein, Bronwyn Hall, Carol Horton Tremblay, Wes Wilson,

David Schimmelpfennig, Scott Stern, anonymous referees and participants at the NBER conference,

Competition and Organization in Technology-Intensive Industries, and Western Economic Association

meetings for useful comments and suggestions. We would also like to thank James Mehring for valuable

research assistance. The views expressed herein are those of the authors and do not represent the views

of the Federal Trade Commission or any individual Commissioner. Any errors or omissions are the

responsibility of the authors. 
 

1

 In addition to the papers mentioned above, chapter 10 of Tirole (1988) has a thorough overview of this

literature.

 

2

 We look at acquisition activity here because that is the focus of this paper. Licensing arrangements seem

to most important for the pharmaceutical industry. 

 

3

 Our choice and definition of high technology sectors was limited by the categories reported by Mergers

and Acquisitions.

1

I. Introduction

R&D activity and innovation have taken center stage in economic analysis of high-technology

industries. A number of papers including Dasgupta and Stiglitz (1981), Reinganum (1985) and 

Jovanovic and MacDonald (1994a; 1994b) model and simulate industry evolution through patterns of

innovation and imitation by firms.1 Firm survival in these models depends on their ability to innovate

or imitate new products. This line of research suggest that firms must generate marketable products

on their own or exit. 

However, this ignores the fact that firms may obtain technology (or other assets) through

acquisitions or licensing. In other words, acquisition or licensing activity may be important in

determining firm survival and growth as R&D. Papers such as Salant (1984), Gallini and Winter

(1985), Katz and Shapiro (1986), and Gans and Stern (1997) show that licensing or acquisitions can

alter firms incentives to innovate. By allowing innovations to be obtained by the firm with the highestvalued use, the acquisition market plays an important role in these high-technology sectors. 

Empirically, licensing and acquisition activities are important for high-technology industries.2 

The first two columns of table 1 show annual average acquisitions and average annual share of all

manufacturing acquisitions for some select high technology sectors in the United States from 1989-94.3

For comparison columns 3 and 4 show each sector’s share of total manufacturing firms and total

manufacturing shipments, respectively. Table 1 demonstrates that acquisition activity in these high

technology sectors is much larger than their share of total manufacturing firms or shipments. For
2

example, computer and office equipment firms represent 0.6 percent of all manufacturing firms and

account for 2.2 percent of all manufacturing shipments, but represents almost 5 percent of

manufacturing acquisition activity. All four high technology sectors in table 1 display this same

pattern. 

This paper examines the empirical evidence on the relationship between R&D and acquisition

activity in industrial sectors where innovations and technology are important. The question is which

firms are acquiring assets in these industries – in particular, is it firms that are investing in R&D or

not. Theoretically, the relationship between R&D and acquisitions is an open question. The

traditional acquisition literature can support the view that either relatively high R&D or low R&D

firms will acquire more. There could be synergy gains by acquiring like assets, R&D, or

complementary assets, such as sales or distribution.

In this paper we empirically examine the relationship between R&D intensity and acquisition

activity in over 200 firms in the U.S. electronic and electrical equipment industry from 1985 to 1993.

Controlling for traditional merger motives, we test whether high R&D intensity firms are more or less

likely to make acquisitions. Estimation is complicated by 1) availability of only discrete counts of

acquisitions, our dependent variable, 2) issues of simultaneity, and 3) dynamic considerations of the

relationship between R&D intensity and acquisition activity over time. Using a recent GMM estimator

for count/panel data sets suggested by Wooldridge (1997), we overcome these difficulties.

Our results show a strong negative correlation between R&D intensity and acquisition activity;

in other words, relatively low R&D firms in these industries are more likely to participate in the

acquisition market. These results are robust to a wide variety of specifications and sensitivity tests. 

This includes allowing for unobserved firm-specific effects and controlling for simultaneity in our

nonlinear panel data set. 

The rest of the paper is organized as follows. The next section discusses in greater detail the
3

potential for competing hypotheses on the relationship between R&D intensity and acquisition activity. 

The following section presents the econometric models used to test our hypotheses and other merger

motives in high-technology electronics. We then present our empirical results and a final section

concludes. 

II. R&D and Acquisition Activity in High Technology Industries

A traditional motive for acquisition activity is the potential for synergy gains. As formulated in

Hall (1987), the acquisition market is a matching process. In this matching process a firm calculates

the potential synergy gains and costs from an acquisition with all the possible target firms. A firm with

more assets will have a greater potential for synergy with another firm’s assets, ceteris paribus, and

thus, more likely to acquire. If firms with higher R&D intensity are generating more technological and

innovative assets, one would expect R&D intensity to be positively correlated with acquisition activity. 

This assumes there is a strong correlation between R&D intensity and valuable innovations, which may

not be true (Trajtenberg (1990)). However, Geroski et al. (1993) find that the process of innovation

may be just as important to firm profitability as the product of innovation; thus, the assets connected

with the R&D process may be as important for synergy motives as the innovations they may generate. 

Thus, there is a credible case for a positive correlation between R&D intensity and acquisition activity.

Hall (1987) specifically explores the role of R&D activity in creating synergy gains that lead to

acquisitions. She estimates a matching model of the acquisition decision by a firm. Conditional that a

firm is in the acquisition market, the firm considers all other firms as potential targets and acquisitions

occur when assets of the acquiring and target create synergy gains to yield a large enough return. The

paper uses a large cross-industry sample constructed from all firms in the Compustat data files and

focuses on synergy gains with respect to R&D assets and activity. The main finding with this matching

model is that firms of like sizes and R&D intensity are more likely to merge. In addition, Hall (1987)

finds that the shadow price of R&D intensity of the target firm increases in the acquiring firm’s R&D
4

intensity. These results suggest that R&D intensity may create important synergies that make a firm’s

valuation of a potential target greater. However, it should be noted that this result does not necessarily

mean that R&D intensity is positively correlated with acquisition activity since it is conditional on the

firm already having decided to acquire. In fact, when Hall (1987) explores determinants of the

probability that a firm engage in acquisitions, R&D intensity is not a significant explanatory variable

across the study’s sample of years, 1976-86. However, for a subsample, 1982-86, R&D intensity is

negatively related to the probability of acquisition.

A negative correlation between R&D intensity and acquisition activity may occur because firms

are choosing between an internal growth strategy with relatively high R&D intensity versus an external

growth strategy with acquisitions. This is what is traditionally known as “make or buy” strategy. 

Anecdotal evidence of managers using acquisitions for growth are common in high-technology

industries. For example, a 1991 Electronic Business (January 7, 1991, pp. 28-32) article reports that the

CEO of Seagate Technology, a manufacturer of disk drives, blamed financial losses in early fiscal 1989

for a slow down in R&D which then made Seagate tardy in bringing new innovations to the marketplace. 

As a result, Seagate acquired Imprimus Technology Inc., formerly a disk drive subsidiary of Control

Data Corporation which claimed the fastest disk drive in the world at that time, in October of 1989. In

another example, Vishay Intertechnology, a manufacturer and distributor of electronic resistors,

apparently decided on external over internal acquisition of technology in the late 1980s as well. Again,

an Electronic Business article reports that the CEO of Vishay, Felix Zandman, felt that “Vishay could

have grown either by developing new products or by acquiring companies in a related business. ‘We

decided to acquire,’ he says” (Electronic Business, Jan. 7, 1991, p. 39). From November 1987 to

October 1988, Vishay bought three resistor companies. A final example comes from the software

industry. Mark Bailey, Vice President at Symantec Corporation, writes in an article for the

March/April, 1995 issue of Mergers & Acquisitions, 
5

“de novo innovations are becoming riskier, more expensive, and more time consuming in

markets where survival depends on speed. Hence, high tech firms, as exemplified by software

developer Symantec Corp., are going outside to get companies with talented people and proven

products that can meet market demands and generate technological throw-offs for the future.” 

(p. 31)

The article notes that Symantec Corporation acquired 18 firms in its 12-year history. 

Interestingly, these examples point out that acquisitions may be either a long-run strategy for

growth or, in the case of Seagate, potentially a response to difficulties generating innovations and

growing internally. If the latter case is the norm, it is not clear that there would be negative relationship

between R&D intensity and acquisition activity in general, as would be true with the former case. As

Trajtenberg (1990) points out, while there is a strong relationship between R&D and patents, the

relationship between R&D and valuable innovations is much weaker. Perhaps firms do not vary their

R&D efforts, but use acquisitions in those periods when they have a below average realization of

valuable innovations. In this case, one would expect to find a correlation between R&D intensity and

acquisition activity using a within estimator.

A recent paper by Gans and Stern (1997) in the patent race and innovation literature suggests the

relationship between R&D intensity and licensing/acquisition activity may be theoretically ambiguous. 

They begin with the standard model in this literature where an incumbent firm and entrant firm compete

in a patent race. However, if the entrant wins the race, they do not assume the entrant will start

production. Instead, the entrant may license the new technology to the incumbent (or equivalently the

incumbent may acquire the potential entrant). They find that licensing/acquisition, rather than product

market competition, is a unique equilibrium in their model when the entrant innovates before the

incumbent. Intuitively, they obtain their results because the firms can do better by sharing monopoly

profits which are greater than the sum of the duopoly profits. Importantly, this environment can have

quite different impacts on the incumbent firm’s research activity. Even if the entrant wins, the

incumbent’s research activity is important for bargaining over the rents from the new innovation. The
6

threat of matching the entrant’s innovation with its own can increase the rents accruing to the incumbent. 

Gans and Stern find that when the expected licensing fee (or acquisition cost) is small, the incumbent

considers the entrant’s research as an imperfect substitute for its own research; i.e. the incumbent’s and

entrant’s research activities are strategic substitutes. In contrast, when the expected licensing fee is

large, they are strategic complements, which is consistent with the traditional literature on patent races.

Besides the papers mentioned above, a few other notable papers have empirically examined

acquisition activity in high technology industries and its relationship to the R&D process. Granstrand

and Sjölander (1990) suggest acquisitions in high-technology industries are large firms acquiring the

technology generated by small firms. They also present preliminary empirical evidence this occurs with

Swedish firms. Hall (1990) is the most comprehensive study to explore the general relationship between

R&D intensity in an industry (as proxied by R&D expenditures as a percent of sales) and acquisition

activity, however the study mainly focuses on the ex post intensity of R&D activity after a merger or

acquisition takes place, rather than its potential role as a factor in acquisition decisions by firms. An

empirical trend found by Hall suggests a possible ex ante relationship between R&D and acquisition

activity -- Hall’s analysis of over a thousand manufacturing firms from 1977-1987 shows acquiring

firms tend to have lower R&D expenditures relative to the rest of their industry. One explanation is

some firms have chosen an external method of acquiring innovation or technology. Finally, Friedman et

al. (1979) examine the relationship between R&D and joint venture activity (as opposed to acquisition

activity) at the firm level across a cross-section of industries. They find the greater the involvement of

firms in joint venture activity, the lower the R&D expenditures, suggesting joint venture activity may

be an external substitute for internal R&D activity. They also compare the degree of substitutability

between R&D and joint ventures across industries and find higher degrees of substitution in industries

with higher average R&D levels (i.e., in high-technology industries).

In summary, theory argues that the relationship between R&D intensity and acquisition activity
 

4 We note that there is a decently wide variance in R&D intensity across sectors in these industries as

well. However, as we note below, our results are robust to eliminating observations of very low R&D or

very high R&D intensity, or controlling for industry effects.

 

5 About one-third of our acquisition observations were partial acquisitions and most of the complete

acquisitions were of quite small firms. For those acquisitions where a price was reported, approximately

40 percent, the average price was slightly less than 150 million. When less than half a doxen transactions

over a billion are removed the average transaction drops to less than 75 million, The descriptions of the

acquisitions in Mergers and Acquisitions for our sample firms’ acquisitions often listed “technological”

assets as a motivation for the acquisition. Over sixty percent of the transactions listed items like

engineering services, computer programing sercies, radio frequency ID cards, or tantalum capacitors.

7

may be either one of substitutes or complements. The sparse empirical work on this issue finds mixed

results. Previous empirical work has typically examined firms across a wide cross-section of industries,

yet the R&D process and technological innovation is much more important in certain sectors of the

economy. The hypotheses discussed above may be almost solely applicable to high technology

industries, so that estimates from a sample of firms across a wide variety of industries may obscure a

strong interaction between R&D intensity and acquisition activity in these particular sectors. In

response, this study narrows the focus to an industry with a preponderance of firms with relatively high

R&D intensity: the electronics and electrical equipment industry.4 

III. Methodology and Data

A. Methodology

To test the relationship between R&D intensity and acquisition activity, we estimate the

determinants of acquisition activity by a firm, which include its R&D intensity. Measuring a firm’s

acquisition activity level in dollars is impossible since the terms of acquisition deals are often kept

private. Therefore, we measure acquisition activity by observing the annual discrete counts of

acquisitions by a firm (ACQit) reported in the publication, Mergers & Acquisitions, and use this as our

dependent variable. Acquisitions were defined broadly to include not only acquisitions of whole firms

but also partial acquisitions and equity increases of more than $1 million dollars in another firm.5 These
Another 6 percent of the transaction involved software companies. The remaining less than thirty percent

were in services, like financing or customer service or in low technology equipment and parts, such as

fuses or wholesale electrical parts. Our method has both advantages and disadvantages from Hall’s (1987)

study. Unlike that study we do not have target firm characteristics and are not testing a “matching”

model. However, our definition of acquisitions is not limited to only firms for which we can obtain

financial data, as with Hall’s study. If we followed Hall’s study, we would have ended up with only a

handful of acquisition observations versus the 531 acquisitions we record for this sample.

 

6 We note that our results are qualitatively identical for a probit specification where the dependent

variable is defined as whether there any acquisitions in a period or not.

8

Prob[ACQ'ACQit] '

'(2%ACQit)

'(2)'(ACQit%1)

u

2

it (1&uit)

ACQit (1)

modes of acquisitions often involve transfer of technological assets just like complete acquisitions.

Previous studies have often specified a probit analysis to model such a dependent variable. However, a

probit model may suffer from specification bias, since it treats a firm with one acquisition in a period as

observationally equivalent to a firm that has two or more acquisitions during the period. There are a fair

number of multiple acquisition observations, so we initially model our dependent variable as following a

negative binomial specification which specifically handles the integer property of the dependent variable

directly and includes “0" observations as natural outcomes.6 In particular, we specify our dependent

variable (ACQit) as following a Poisson process which has a Poisson parameter, 8it. Then we make the

common assumption that this Poisson parameter is a function of regressors, Xit . We choose the

particular relationship, ln 8 , where exp(,) has a gamma distribution with mean one it ' exp($

)Xit) % ,

and variance ", and $ is a vector of parameters to be estimated. This leads to the following negative

binomial specification which we use for our initial analysis:

where uit = 2 / (2 + 8it) and 2 = 1/". 

Our choice of regressors incorporates our R&D-related hypotheses concerning the relationship

between R&D intensity and acquisition activity, while controlling for other firm-level variables that may
 

7 We follow Hall (1987) in defining R&D intensity this way and in using an assets measure as a proxy

for the firm’s size. As we note in the text below, our results are essentially identical if we use a firm’s

sales in the construction of these variables, rather than total assets.

 

8

 Comprehensive surveys of the merger motives literature include Hughes et al. (1980), Jensen and

Ruback (1983) and Scherer and Ross (1990).

 

9 Schwartz (1982), and Harris et al. (1982) are examples of studies that have used random samples of

Fortune 500 companies to test M/A motives. Tremblay and Tremblay (1988) and Hannan and Rhoades

(1987) focus on individual industries. A large number of determinants have been examined across these

studies. We use a fairly parsimonious specification, but note that our results of interest, the correlation

between R&D intensity and acquisition activity, is quite robust to alternative regressor sets. 

9

affect a firm’s acquisition activity. We measure R&D intensity (RDPERit) as the ratio of the firm’s

R&D expenditures to total assets.7 Of course, previous empirical studies of M/A motives have tested for

a wide variety of other determinants of acquisition activity.8 Some of the more common variables used

include the size of the firm, indebtedness, and profitability.9 The majority of studies in the merger and

acquisitions literature (including Hall (1987), and Tremblay and Tremblay (1988)) control for the size of

the firm, invariably finding a significant positive correlation between size and the probability of

acquiring. We use the firm’s total assets (ASSETSit) to proxy for size. To take into account capital

constraints, we include a firm’s debt position (ratio of total debt to total assets - DATit) , expecting a

negative correlation between debt position and acquisition activity. Jensen (1988) suggests that better

performing firms will acquire and Tremblay and Tremblay (1988) find that “more successful” firms in

the beer industry (defined as output share of market previous two years) are more likely to acquire. 

Constructing a variable as in Tremblay and Tremblay (1988) is problematic for our sample, since they

do not produce for similar output markets. However, profitability of a firm is likely an important signal

that a firm is well managed and performing well. Therefore, we include a measure of the firm’s income

(after expenses, before extraordinary items, and before provisions for common and preferred stock)

divided by sales (RETSALEit) and expect a positive correlation. Finally, in a related vein, we include a

measurement of a firm’s cash flow (CFLit) given Jensen’s (1988) free cash flow hypothesis that suggests
 

10 A balanced panel is necessary for some of the statistical specifications we use below. In addition,

we eliminated firms (less than five) that were completely acquired during this period and thus, no longer

reported financial characteristics and firms reporting negative total assets.

10

that better performing firms will also have higher cash flow and be more likely to acquire. We use a

cash flow measurement reported by Compustat which is defined as a firm’s income (after expenses,

before extraordinary items, and before provisions for common and preferred stock) plus depreciation

and amortization.

The above empirical model assumes that the regressors, including RDPERit, are exogenous. 

This is highly unlikely. These endogeneity issues are not unique to our R&D intensity variable, but

potentially affect all the financial variables we include in our regressor set. However, endogeneity is

difficult to control for in the limited dependent variable models we employ. Previous studies that have

encountered similar endogeneity considerations have often ignored the issue or have created

predetermined regressors by lagging them one period to avoid potential simultaneity. Both of these

approaches have obvious drawbacks. After the preliminary results below we address this issue, by first

reporting results when the regressors are lagged, and then using a relatively new GMM estimator

suggested by Wooldridge (1997) which allows us to exploit the panel nature of the data to control for

endogeneity. Estimates from both the lagged-regressors and GMM specification suggest that the

simultaneity bias works toward understatement of our coefficients in the preliminary results.

B. Data

Our sample is a panel of data on electronic and electrical equipment firms, covering the period

from 1985 to 1993. All firms listed in the Compustat database with primary Standard Industrial

Classification (SIC) of 36 and 357 were sampled. However, any firm without complete coverage of all 

the independent variables for the regressions are eliminated.10 This leaves 217 firms in our sample. 
 

11 It is possible to construct (and Compustat reports) data for firms by calendar year, however, these

are based on appropriately combining unaudited quarterly reports. We felt the audited fiscal year reports

would reduce measurement error.

11

These financial data correspond to a firm’s fiscal year, which is not necessarily the calendar year.11

Because Mergers and Acquisitions, our source for the acquisition data, reports on a quarterly basis, we

were able to match acquisitions closely to the period corresponding to the firm’s fiscal year. Table 2

reports all variables used in the empirical analysis along with the sources, mean, standard deviation, the

minimum value, the mode and the maximum value. The average yearly number of acquisitions by firms

in our sample is considerably less than one, with zero acquisitions for well over half our observations

and a maximum number of fourteen. Table 2 also shows that R&D intensity is quite high across these

firms and time periods, averaging 9.7 percent of total assets. One concern is that there are a number of

observations with very high R&D intensity. Below we examine sensitivity of our results to these

potential outliers in the R&D intensity dimension, as well as across other regressors we use, since there

is substantial variability in these control regressors too. However, as we discuss below, our results are

not driven by outliers in these variables.

Table 3 shows descriptive statistics stratified over time for the main variables in this analysis. 

The total number of acquisitions in the data set fluctuates between 40 and 80 over the ten years. The

average number of acquisitions by a firm ranges from between 0.184 and 0.369. However, the

percentage of firms making at least one acquisition ranges between 13.8 and 19.2 percent. The

difference between the two indicates a decent amount of multiple acquisitions by firms in a year. 

Average R&D intensity is increasing over the length of the data set. These “yearly” observations should

be treated with some caution, however. Since firms’ fiscal years vary, these yearly observations only

cover roughly the same time period. 

As a first look at the relationship between R&D intensity and acquisition activity across our

sample, table 4 matches observations in different R&D intensity ranges and the associated average
12

annual acquisition rate. It also lists relatively large representative firms in each R&D intensity range. A

fairly substantial negative relationship between R&D intensity and average annual acquisitions emerges,

ranging from 0.39 acquisitions for very low-R&D observations (less than 5% of assets) to 0.02

acquisitions for very high-R&D observations (greater than 20% of assets). Even eliminating the

extremes of the R&D intensity range, average annual acquisitions are almost two times higher for firms

with R&D intensity between 5% and 10% of assets and firms with R&D intensity between 15% and

20% of assets. Of course, this does not control for other factors that may be correlated with R&D

intensity and determine acquisition activity. Thus, we turn next to a more formal empirical analysis.

IV. Results

The first column of table 5 shows the results of a negative binomial maximum likelihood

estimation on the full data set. A log-likelihood ratio test rejects the hypothesis that the coefficients are

jointly zero for the specification. RDPER shows a negative sign and is statistically significant at

standard significance levels. The negative correlation provides evidence that firms’ R&D intensity and

acquisition activity are substitutes, suggesting that there is specialization in R&D and product market

activities across firms. The marginal effect of R&D intensity on a firm’s acquisition activity is quite

substantial. At the sample mean, our estimates suggest a firm with a 5 percentage point higher R&D

intensity ratio (e.g., from 7 percent of assets to 12 percent of assets ) has an approximately 28 percent

lower yearly acquisition rate.

As expected, ASSETS is strongly significant with expected positive sign. Other explanatory

variables have expected sign as well, with point estimates for RETSALE and CFL statistically

significant. A variety of other specifications were estimated as sensitivity checks, including estimation
 

12 Alternative regressors included other measures of firm profitability, such as return-on-equity and

return-on-investment measures. These generally yielded similar, but noisier, estimates relative to

RETSALE, We also tried alternative measures for a firm’s liquidity to test the free cash flow effect on

the probability of acquisition, including a firm’s current ratio and quick ratio. These generally yielded

noisy point estimates and quantitatively similar coefficients on other regressors, including RDPER. We

also estimated various functional forms of the dependent variables which similar effects.

 

13

 We also ran each year of our panel as a separate cross section. While the estimates were less

precise, each cross section estimated a negative correlation between R&D intensity and acquisition

activity, with four of the nine RDPER coefficients estimated as statistically significant at standard

confidence levels. Marginal effects of R&D intensity were generally quite similar as well to that estimated

over the entire sample, especially in the years in which RDPER was estimated with precision.

 

14 This variable was constructed from Mergers and Acquisitions as well, which lists acquisitions by

quarters. Thus, for example, if a firm’s fiscal year end is March 31, this variable is U.S. domestic

13

of OLS, probit, and Poisson models, as well as a variety of alternative explanatory variable matrices.12

While the point estimates on some of the explanatory variables are sensitive to choice of specification,

the coefficient on RDPER is quite insensitive to these alternative specifications, both in terms of sign

and magnitude. In addition, using a firm’s sales as a proxy for size rather than total assets and/or

defining RDPER as the ratio of R&D expenditures to sales yields qualitatively identical results. Further

sensitivity checks, particularly with respect to potential outliers and choice of sample, are reported and

discussed below.

One concern with our estimates is that we are not controlling for time effects. As table 3 shows,

there is some variability in total acquisitions occurring across our sample of firms. Controlling for these

effects is complicated by the fact that our sample firms vary in the time period covered by their fiscal

years and hence in the period covered by their annual observation in our data, as discussed above. In

order to judge if the time series nature of our data is a concern, we tried a number sensitivity tests. 

First, we added year dummies as explanatory variables and found these to be jointly insignificant.13

This approach suffers from the problem of varying fiscal years across firms. To address this, we next

constructed a variable of total U.S. domestic acquisition activity (excluding the electronic and electrical

equipment industries to avoid endogeneity) that more closely corresponded to each firm’s fiscal year.14
acquisitions for quarters 2, 3, and 4 of the previous year and quarter 1 of the current year.

14

Including this variable does not significantly alter any of our coefficient signs and was typically

insignificant in most specifications we tried. 

Another substantial concern with our estimates is simultaneity bias, not only with the RDPER,

but all right-hand side regressors. If an acquisition or merger is large enough relative to the firm’s

initial size, it is likely that it will substantially alter the firm’s financial variables. With respect to R&D

intensity, Hall’s (1990) analysis suggests endogeneity bias works toward finding a negative coefficient

on RDPER in our estimates, since her time series analysis shows that firm’s typically reduce R&D

intensity after an acquisition or merger. On the other hand, there may be reasons to expect the bias to

work the other way. For example, a third factor, capital constraints, may be positively correlated with

both acquisitions and R&D intensity. Himmelberg and Petersen (1994) find evidence that capital market

imperfections may substantially affect R&D activity because it means the firms must rely on internal

financing. These same considerations may similarly affect acquisition activity and lead to both activities

moving together depending on the firm’s finances and biasing RDPER toward a positive coefficient. In

the end, these considerations are substantial and could lead to an estimate on RDPER that has substantial

bias in either direction.

Other papers in this literature (e.g., Hall (1987)) often address endogeneity concerns by lagging

the regressors so they are predetermined. In like manner, we next report the results from a negative

binomial model where all right-hand side regressors are lagged one period in column 2 of table 5. 

Interestingly, this specification yields results that are qualitatively similar to a specification with

contemporaneous regressors. The point estimate on RDPER declines modestly, but the difference is not

statistically significant. The coefficient on ASSETS falls by about a third, while the coefficients on

RETSALE and CFL increase. 

 However, it is clear that lagging regressors is not an ideal method of addressing endogeneity
15

r

it(ß) ' ACQit & ACQi,t%1

[μit(ß)/μi,t%1(ß)], t'1,...,T&1, (2)

E[w

)

itr

it(ß)] ' 0, t'1,...,T&1. (3)

concerns. First, the lagged regressor specification with annual data means that the firm makes current

acquisition decisions based on last year’s R&D intensity, debt, profitability, etc., which may be a

difficult assumption to defend. In addition, as Wooldridge (1997) points out, lagging regressors in a

panel data set does not control for all sources of endogeneity if current values of the dependent variable

affect future values of the regressors. In our case, this means that our estimates may be inconsistent if

current acquisition activity affects future R&D intensity, profitability and other financial characteristics

we include as controls. 

Until recently it was difficult, if not impossible, to address these issues in the nonlinear

count/panel data framework we employ in this paper. However, Wooldridge (1997) develops a

generalized method of moments (GMM) estimation approach that corrects for these endogeneity

concerns in a panel and count data model. Wooldridge’s paper suggests a forward-difference

transformation that leads to appropriate orthogonal moment conditions when simultaneity or feedback

over time from the dependent variable are possible in a multiplicative panel data set. Following

Wooldridge, define a transformation function

where T is the number of periods in our panel and $ is the vector of coefficients. We define μit($) =

exp(xit$), where xit is the matrix of regressors, which is the common functional form used to represent

the mean in a count data model, such as Poisson or negative binomial. Given an appropriate instrument

matrix, wit, Wooldridge shows that 

The orthogonality conditions represented in (3) allow us to obtain consistent GMM estimates. We use

contemporaneous and one-period lagged values of regressors for instruments, as well as an additional
 

15 With the forward difference transformation used for this estimator, this means that contemporaneous

regressors are predetermined and thus appropriate as instruments. Firm-level patent data were retrieved

from the U.S. Patent and Trademark Office CD-ROM, CASSIS (Classification and Search Support

Information System).

 

16 In a related vein, Morck et al. (1990) find that personal managerial objectives can often explain

acquisitions that perform badly in increasing shareholders’ profits. 

 

17 Including 4-digit industry effects in the negative binomial specification, both with and without lagged

regressors, yields very similar results to the GMM estimates that control for fixed-effects below. 

16

variable and its one-period lag: patents per level of sales.15 The latter is used as an additional instrument

for our variable of interest, R&D intensity. Previous studies have demonstrated a strong correlation

between R&D expenditures and patents (e.g., Trajtenberg [1990]), but it is unlikely that acquisition

activity affects current-period patents, since patents are generated through an often lengthy R&D

process. An appendix provides more details of the GMM estimation procedure we use. To our

knowledge, Montalvo (1997) is the only other application of this estimation approach to date. 

Besides addressing endogeneity concerns, the GMM estimation procedure also controls for fixed

effects across the panel. The results to this point examine pooled data across all firms in our sample. 

While we find a number of firm-level variables with substantial explanatory power, there may be

sources of unobserved heterogeneity in firms’ acquisition patterns. Unobserved firm-specific effects

may be likely in our sample for a number of reasons. Some managers may simply have a predilection

for acquiring other firms. Roll (1986) suggests that hubris on the part of managers of bidding firms may

mean that some firms pay more than is warranted for a target firm.16 However, if this is true then a

potential implication is these managers are also acquiring more often than they should based on

observables. Finally, by accounting for firm-specific effects we are assure that more broadly classified

fixed effects, such as industry-specific effects, are not driving the inverse relationship between R&D

intensity and acquisition activity.17

The third column of coefficients in Table 5 give results from our GMM estimation. We use a
17

fixed effects Poisson model (as suggested by Wooldridge (1997)) as our starting values for the

coefficients. The GMM over identification statistic (P

2

(99) = 103.9 with p-value 0.35) fails to reject the

null hypothesis, which suggests that our instruments are appropriately orthogonal to μit($). Controlling

for endogeneity and fixed effects significantly increases the size of the coefficient on RDPER. Most of

this difference is due to controlling for fixed effects, as the fixed-effect Poisson starting value for

RDPER in the GMM estimation (before controlling for endogeneity) is 9.333. Thus, the endogeneity

bias works toward reducing the coefficient some, which is consistent with the difference between the

negative binomial and lagged regressor negative binomial specification (in columns 1 and 2 of table 5). 

The other control variables have identical signs to the negative binomial specification with the exception

of CFL, though RETSALE is statistically insignificant while DAT is now significant with the GMM

specification.

Since R&D intensity and acquisition activity are substitutes using a fixed-effects estimator, this

relationship occurs over time within individual firms’ operations as well. In other words, with firm

fixed effects, the substitute relationship between R&D intensity and acquisition activity is being

estimated solely from within-firm variation. This suggests that firms’ strategies concerning external

versus internal growth are not necessarily predetermined, but evolve and change over time.

 A. Further Sensitivity Checks

Although we have discussed numerous sensitivity analyses as we presented results above, in this

section we examine sensitivity to potential outliers and issues surrounding the sample of firms in our

data. Examining the descriptive statistics in table 2, there is a high degree of variance among most of

the variables. In our data, one firm is an order of magnitude larger than virtually all the firms in our

sample and responsible for a proportionally large percent of each year’s acquisitions, General Electric. 

Additionally, there are a three firms, Computer Automation, Power Designs and Dian Controls, that
 

18 Firms often have interests in a number of industries and must choose one “primary” SIC to report.

Thus, for example, a firm could have 33 percent of its operations in wholesale distribution, 33 percent in

retail, and 34 percent in electronics manufacturing and would report the manufacturing as its primary SIC.

18

have annual observations where R&D intensity is 100 percent of total assets or larger. These firms are

quite small and have only one acquisition between them in our data. Whether these “outliers” are

driving the negative correlation between R&D intensity and acquisition activity is an important question.

Table 6 displays the descriptive statistics when these four firms are eliminated from the sample. 

Elimination of these firms has a significant impact on the descriptive statistics for a number of the

variables. Means are affected to some degree in all cases. In addition, standard deviations and

maximum values are reduced in all variables, except for CFL. This is particularly true with the

dependent variable, ACQ, and explanatory variables, RDPER and TA, which see their maximum values

and standard deviations decrease substantially from their former value.

We next reestimate our empirical model with this reduced sample. Columns 4 and 5 of table 5

report results from a negative binomial with lagged regressors and the GMM specification on the new

data sample. Interestingly, most of the estimated coefficients and their associated marginal effects at the

means are quite similar, suggesting that the outlying firms were not driving the estimated relationships. 

This is particularly true of the relationship between R&D intensity and acquisition activity. The only

exception to this is ASSETS for which the coefficient increases by an order of magnitude. 

Other sensitivity tests included eliminating firms with no acquisition activity in the data set. One

might be concerned these firms’ acquisition decisions follow a completely specification than firms that

do acquire. However, there was virtually no impact on our estimated coefficients. Another concern

may be that the SIC listed by Compustat may be misleading and we are including firms that may be

distribution firms rather than high-technology electronics manufacturers.18 Distribution firms would

have negligible R&D expenditures and our results on R&D intensity may just be suggesting that

distribution firms acquire more than manufacturing firms. We ran a sample of firms where RDPER
19

averages over 2.5 percent. This led to qualitatively identical results and the coefficient of interest

(RDPER) is insignificantly affected. 

V. Conclusion

This paper has provided evidence for a significant relationship between R&D activity and

patterns of acquisitions in a high technology industry. Robust to a variety of alternative specifications

and sensitivity tests, we find R&D intensity and acquisition activity substitute for each other across a

panel of electronics firms. This supports the notion that firms in high-technology industries may have

different strategies by at least partially specializing in one of these two modes, internal R&D or

acquisitions, for survival and growth. Our results are potentially relevant for the Gans and Stern (1997)

paper as well. The inverse relationship is consistent with a market where expected acquisition costs for

incumbents are low enough that acquiring incumbent firms R&D activity may be a strategic substitutes

for R&D. In fact, the internal/external growth story and the results of Gans and Stern complement each

other to the extent that a substitute relationship between R&D and acquisition activity is possible only if

there is an efficient acquisition market. In that sense, our paper suggests that a well-functioning

acquisition market plays an important role in determining the structure of an industry.

We foresee future work in this area along a number of lines. First, while our results show that

firms may take substantially different paths toward growth and survival, our results do not address

whether firms pursuing one strategy or the other tend to be more successful. Second, we have

controlled for firm-specific effects, but these firm-specific effects may not be constant if there is

turnover in management. In other words, we controlled for corporate hubris, not necessarily manager

hubris. Examining whether new management affects the firm’s acquisition propensity is an interesting

avenue to pursue.
20

References

Dasgupta, Partha and Joseph Stiglitz. “Entry, Innovation, Exit: Towards a Dynamic Theory of

Oligopolistic Structure.” European Economic Review. Vol. 15 (1981), pp. 137-158. 

Francis, Jennifer, and Abbie Smith. “Agency Costs and Innovation: Some Empirical Evidence.” Journal

of Accounting and Economics. Vol. 19 (1995), pp. 383-409.

Friedman, Philip, Sanford V. Berg, and Jerome Duncan. “External vs. Internal Knowledge Acquisition:

Joint Venture Activity and R&D Intensity.” Journal of Economics and Business. Vol. 31 (1979), pp.

103-110. 

Gallini, Nancy T. and Ralph A. Winter. “Licensing in the Theory of Innovation.” RAND Journal of

Economics, Vol 16(Summer 1985), pp. 237-252. 

Gans, Joshua S. and Scott Stern. “Incumbency and R&D Incentives: Licensing the Gale of Creative

Destruction.” Mimeo, 1997.

Geroski, Paul, Steve Machin, and John Van Reenen. “The Profitability of Innovating Firms.” RAND

Journal of Economics, Vol 24 (1993), pp. 198-211.

Granstrand, Ove, and Sören Sjölander. “The Acquisition of Technology and Small Firms by Large

Firms.” Journal of Economic Behavior and Organization, Vol. 13 (1990), pp. 367-386.

Hall, Bronwyn H. “The Effect of Takeover Activity on Corporate Research and Development.” In

Corporate Takeovers: Causes and Consequences, edited by Alan J. Auerbach. Chicago: University of

Chicago (NBER Conference Volume), 1987. 

Hall, Bronwyn H. “The Impact of Corporate Restructuring on Industrial Research and Development.”

Brookings Papers of Economic Activity: Microeconomics 1990, pp. 85-135, 1990.

Hannan, Timothy H. and Stephen A. Rhoades. “Acquisition Targets and Motives: the Case of the

Banking Industry," Review of Economics and Statistics. Vol. 69 (1987), pp. 67-74.

Harris, Robert S., John F. Stewart, David K. Guilkey, and Willard T. Carlton. “Characteristics of

Acquired Firms: Fixed and Random Coefficients Probit Analyses,” Southern Economic Review. Vol 49

(July 1982), pp.164-184.

Himmelberg, Charles P. and Bruce C. Petersen. “R&D and Internal Finance: A Panel Study of Small

Firms in High-Tech Industries.” Review of Economics and Statistics. Vol 76(February 1994), pp. 38-

51.

Hughes, Alan, Dennis C. Mueller, and Ajit Singh. “Hypotheses About Mergers.” In The Determinants

and Effects of Mergers, edited by Dennis C. Mueller. Cambridge, MA: Oelgeschlager, Gunn & Hain,

Publishers, Inc., 1980.

Jensen, Michael C. and Richard S. Ruback. “The Market for Corporate Control: The Scientific

Evidence.” Journal of Financial Economics. Vol. 11 (1983), pp. 5-50.
21

Jensen, Michael C. “Takeovers: Their Causes and Consequences.” Journal of Economic Perspectives.

Vol. 2 (1988), pp. 21-48.

Jovanovic, Boyan and Glenn M. MacDonald. “Competitive Diffusion.” Journal of Political Economy.

Vol. 102 (February 1994a), pp. 24-52.

Jovanovic, Boyan and Glenn M. MacDonald. “The Life Cycle of a Competitive Industry.” Journal of

Political Economy. Vol. 102 (April 1994b), pp. 322-47.

Katz, Michael L. and Carl Shapiro. “How to License Intangible Property.” Quarterly Journal of

Economics. Vol. 101 (1986), pp. 567-590.

McGuckin, Robert H. and Sang V. Nguyen. “On Productivity and Plant Ownership Change: New

Evidence from the Longitudinal Research Database.” RAND Journal of Economics. Vol. 26 (Summer

1995), pp. 257-276.

Montalvo, Jose G. “GMM Estimation of Count and Data Models with Fixed and Predetermined

Instruments,” Journal of Business and Economic Statistics. Vol. 15(January 1997), pp. 82-89.

Morck, Randall, Andrei Shleifer, and Robert W. Vishny. “Do Managerial Objectives Drive Bad

Acquisitions.” Journal of Finance. Vol. 45 (1990), pp. 31-48. 

Reinganum, Jennifer. “Innovation and Industry Evolution.” Quarterly Journal of Economics. Vol. 100

(1985), pp. 81-100.

Roll, Richard. “The Hubris Hypothesis of Corporate Takeovers.” Journal of Business, Vol. 59 (1986),

pp. 197-216.

Salant, Stephen W. “Preemptive Patenting and the Persistence of Monopoly: Comment,” American

Economic Review. Vol. 74(March 1984), pp. 247-253.

Scherer F. M., and David Ross. Industrial Market Structure and Economic Performance. Boston, MA:

Houghlin Mifflin Company, 1990.

Schwartz, Steven.“Factors Affecting the Probability of Being Acquired: Evidence for the United

States,” Economic Journal, Vol. 92 (June 1982), pp. 391-398.

Tirole, Jean. The Theory of Industrial Organization. Cambridge, MA: The MIT Press, 1988.

Trajtenberg, Manuel. “A Penny for Your Quotes: Patent Citations and the Value of Innovations,” RAND

Journal of Economics, Vol. 21 (1990), pp. 172-187.

Tremblay, Victor J. and Carol Horton Tremblay. “The Determinants of Horizontal Acquisitions:

Evidence from the U.S. Brewing Industry,” Journal of Industrial Economics, Vol. 37 (September 1988),

pp. 21-45.

Wooldridge, Jeffrey M. “Multiplicative Panel Data Models Without the Strict Exogeneity Assumption,”
min

ß j

N

i'1

Wˆ

)

i

r

i

(ß)

)

Oˆ

&1

j

N

i'1

Wˆ

)

i

r

i

(ß) ,

wˆ

i1 0 0 . . . 0 0

0 wˆ

i2 0 . . . 0 0

.

.

0 0 0 0 wˆ

i,T&1

Oˆ / N

&1j

N

i'1

Wi(ßˆ)

)

r

i

(ßˆ)r

i

(ßˆ)

)Wi

(ßˆ)

ßGMM ' ßˆ % (Rˆ

)

Oˆ

&1

Rˆ )

&1N&1j

N

i'1

sˆ

i

Econometric Theory, Vol. 13(October 1997), pp. 667-78.

 APPENDIX 

GMM Estimation Procedure

This appendix follows Wooldridge (1997) and gives further details of the GMM procedure used for

estimation. The GMM estimator is obtained by solving

where ß is a vector of parameters, ri (ß) is a (T-1)x1 vector, (ri1(ß) , . . . , ri, T-1 (ß) )’ , and is a matrix of Wˆ

i

instruments defined as

wherewˆ is a 1xL vector of instruments for each t=1, . . . T-1. In addition, given an - consistent it N

estimator, ßˆ , one can obtain

 From this set up a one-step estimator, which is first-order equivalent to the GMM estimator, takes the form

where , is a matrix of derivatives of with respect to ß, and Rˆ / N

&1j

N

i'1

wi(ßˆ)

)Lß

r

i

(ßˆ) Lßr

i

(ßˆ) r

i

(ß)

sˆ . The asymptotic covariance matrix of is estimated by , where i' Rˆ

)

Oˆ

&1

wˆ

)

i

ˆr

i

ßGMM N

&1

(R ̃

)

O ̃

&1

R ̃ )

&1

 are defined as above, except with parameter vector in place of . R ̃ and O ̃ ßGMM ßˆ
TABLE 1: Acquisition activity in selected high technology U.S. manufacturing sectors.

Sector

Sector’s

average annual

domestic

acquisitions,

1989-94

Sector’s average

share of domestic

manufacturing

acquisitions,

1989-94

Sector’s share of

total U.S. firms

in

manufacturing

(1992)

Sector’s share

of total U.S.

manufacturing 

shipments

(1992)

Chemicals and Drugs 73.7 7.8% 4.6% 5.7%

Computer and Office

Equipment

46.2 4.9% 0.6% 2.2%

Electronic and Electrical

Equipment

84.3 8.9% 3.1% 4.5%

Measuring, Medical and

Photographic Equipment

96.0 10.2% 2.2% 6.6%

Sources: Acquisition data for columns 1 and 2 come from the publication Mergers and Acquisitions,

various issues. Data for columns 3 and 4 are from the U.S. 1992 Census of Manufactures.

Notes: Chemicals and Drugs includes SIC 281, 283, 286, 287, and 289, Computer and Office Equipment is

SIC 357, Electronic and Electrical Equipment is SIC 36, and Measuring Medical and Photographic

Equipment is SIC 38. Acquisition classifications were by target firm and only those transactions of

$1 million or greater are recorded by Mergers and Acquisitions.
TABLE 2: Descriptive statistics of variables.

Variable Description Mean 

Standard 

Deviation Min Median Max 

ACQit Number of acquisitions by firm I in year t. 0.272 0.862 0 0 14

RDPERit R&D expenditures divided by total assets. 0.097 0.165 0 0.073 3.755

TAit Total assets in billions. 1.398 11.033 0 0.051 251.51

RETSALEit Income before extraordinary items

divided by sales.

- 0.018 0.219 - 3.321 0.028 1.036

DATit Debt to total assets ratio. 0.024 0.043 0 0.017 1.020

CFLit Cash flow in billions. 0.109 0.681 -2.209 0.003 10.237

Notes: Data on acquisitions come from the publication Mergers and Acquisitions, various issues. All other

data are from the Compustat database.

TABLE 3: Time series descriptive statistics for sample firms.

Year Total

Acquisitions

Average

Acquisitions

Firms

Acquiring

(%)

Average

RDPER

(%)

1985 40 0.184 14.8 8.6

1986 66 0.304 19.3 8.8

1987 60 0.277 19.3 8.4

1988 55 0.254 18.4 9.1

1989 60 0.277 19.4 10.5

1990 62 0.286 15.7 9.1

1991 46 0.212 13.8 9.5

1992 62 0.286 18.9 11.5

1993 80 0.369 19.8 12.0

Notes: All data pertain to the 217 electronic and electrical equipment firms sampled from the Compustat

database. Total acquisitions are across all sample firms for the year. Average acquisitions is total

acquisitions divided by number of firms (217), whereas firms acquiring gives the percentage of firms that

made at least one acquisition during the year. The difference in these measures is due to the multiple

acquisitions by firms in a year. Average RDPER are yearly cross-section averages for the variable as

defined in table 2. 
TABLE 4: Acquisition activity by R&D intensity ranges across the sample.

R&D intensity range

Average

annual

acquisitions

Number of sample

observations in R&D

intensity range

Large representative firms in R&D

intensity range

0.0 - 5.0% 0.39 653 Cobra Electronics Corp.

Kuhlman Corp.

Sheldahl Inc.

Ametek Inc. 

Bell & Howell Co.

International Rectifier Corp.

5.0 - 10.0% 0.25 639 Andrew Corp.

General Instrument Corp.

IBM Corp.

ADC Telecommunications Inc.

Storage Technology Corp.

Texas Instruments Inc.

10.0 - 15.0% 0.24 369 General Datacomm Industries

Hewlett-Packard Co.

National Semiconductor Corp.

Intel Corp.

Tektronix Inc.

Siliconix Inc.

15.0 - 20.0% 0.13 166 Advanced Micro Devices Inc.

Analog Devices Inc.

Cray Research Inc.

More than 20% 0.02 126 Evans & Sutherland Computer

Corp.

Xicor Inc.

Entire sample 0.27 1953
TABLE 5: Determinants of firm-level acquisition activity in U.S. electronic and electrical equipment

manufacturers, 1985-93.

Full sample Sample without outliers

Regressor

Negative

Binomial

Neg. Bin./ 

Lagged

Regressors

GMM

Estimation

Neg. Bin./ 

Lagged

Regressors

GMM 

Estimation

Constant -1.027*** 

(0.176) 

- 1.091***

(0.166) 

-1.170***

(0.167) 

RDPER - 5.675*** 

(1.338) 

- 5.208*** 

(1.281) 

- 9.266***

(1.670) 

- 4.825***

(1.304) 

- 8.520** 

(3.538) 

ASSETS 0.024*** 

(0.005) 

0.016* 

(0.010) 

0.006***

(0.001) 

0.080***

(0.021) 

0.069***

(0.004) 

RETSALE 2.110*** 

(0.768) 

2.872*** 

(0.669) 

1.055 

(1.787) 

3.274***

(0.675) 

1.157 

(0.906) 

DAT - 3.870 

(4.452) 

- 4.929 

(4.504) 

- 6.603***

(0.682) 

- 3.416 

(4.582) 

- 8.665 

(12.341) 

CFL 0.146* 

(0.085) 

0.321*** 

(0.124) 

- 0.077***

(0.014) 

- 0.238 

(0.190) 

- 0.041***

(0.013) 

Alpha 2.193*** 

(0.329) 

2.179*** 

(0.322) 

2.238***

(0.346) 

Log-Likelihood - 1135.66 - 1131.75 -1090.87 

Likelihood-ratio

Test (p-value)

175.51 

(0.000) 

181.79

(0.000)

143.52

(0.000)

Sample Size 1953 1953 1953 1917 1917

NOTES: The dependent variable is the number of acquisitions for a firm during a fiscal year (ACQit).

Likelihood-ratio test is of the null hypothesis that slopes (excluding constant) are jointly zero and is

distributed ?

2

(5). Standard errors are in parentheses, except for the likelihood-ratio test which reports pvalue of test in parentheses. 
TABLE 6: Descriptive statistics of variables for sample without outlier observations.

Variable Description Mean Standard 

Deviation

Min Median Max 

ACQit Number of acquisitions by firm I in year t. 0.241 0.654 0 0 7

RDPERit R&D expenditures divided by total assets. 0.088 0.072 0 0.073 0.856

TAit Total assets in billions. 0.848 5.379 0 0.053 92.473

RETSALEit Income before extraordinary items

divided by sales.

- 0.011 0.190 - 3.321 0.029 0.355

DATit Debt to total assets ratio. 0.022 0.032 0 0.017 0.629

CFLit Cash flow in billions. 0.084 0.557 -2.209 0.003 10.237

Notes: Data on acquisitions come from the publication Mergers and Acquisitions, various issues. All

other data are from the Compustat database.