goretti cabaleiro

8/13/2019 Goretti Cabaleiro.

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Licensing Out as a Real Activity to engage in Myopic Management

Goretti Cabaleiro CervioUniversidad Carlos III de Madrid

[email protected]

(Early draft: Please Do Not Distribute Without Permission)

This paper empirically tests that managers, under the pressure to attain analystsforecast, can license out their intellectual property as a mean to increase short-termearnings. However, as licensing contracts always imply a trade-off, I expect that thesecompanies will increase their benefits in the short term (Revenue Effect) but they willalso harm their market share in the long run (Dissipation Effect). Results confirm that 1)companies are more likely to license out their intellectual property when they were notable to achieve analysts forecasts and 2) companies that have increased the number oflicensing out contracts with respect to the previous year present a decreasing market

shares trend in the following two years, 3) this decreasing market shares trend isstronger for companies that license out their technology and were not able to achieveanalysts forecast than for companies that overcome the earnings threshold.

Key words: Licensing, Intellectual Property, Real Activities, Myopic Management.


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1. INTRODUCTION.

Over the last two decades licensing agreements have experienced an unprecedented

growth and its management had become a core competence of high-tech companies

(Zuniga & Guellec, 2008, Lichtenthaler and Einst, 2009; Kamiyama et al. 2006). In fact,

in order to facilitate knowledge transfer, companies are establishing their own licensing

department and/or publishing in their web their technology available to license1.

The main reason that explains this increasing trend is the revenue that licensing

generates. In a survey conducted by Zuniga & Guellec (2009) 51% of the European

companies and 53.6% of Japanese companies recognized that their main motivation to

license-out their technology in the previous three years was the licensing revenue.

However, licensing also has a negative counterpart. That is, even though companies

increase their benefits by the licensing revenues (net of transaction costs), they also

could reduce their market share or their price-cost margin because the additional

competition in the product market (Arora & Fosfuri, 2003; Fosfuri, 2006). Therefore,

licensing decision has to be taken with caution: balancing the short-term earnings

against the possible negative long-term consequences. Nevertheless, evidence shows

that companies sometimes put at stake their competitive position underestimating this

long-term effect2.

Similarly, over the last decades financial analysts are increasingly influencing

companies strategies. The consequences of missing their forecast, even by a small

quantity, have been disproportionate3. It has put extra pressure on managers and has

created incentives to manipulate current earnings. In a survey conducted by Graham et

al. (2005) CFOs admit that they put much attention on meeting the earnings thresholds.

#See, for example:a. Dow Chemical: http://www.dow.com/licensing/b. Kimberly Clark: http://www.merck.com/licensing/home.htmlc. Merck & Co: http://www.merck.com/licensing/home.htmla. Dow Chemical: http://www.dow.com/licensing/b. Kimberly Clark: http://www.merck.com/licensing/home.htmlc. Merck & Co: http://www.merck.com/licensing/home.html"For instance, Hitachi before 2003 was one of the companies that license-out more technology. In fact, in 2002 the companypresented licensing revenues of JPY43 billion. However, as a result of its aggressive licensing strategy, licensees in China andKorea rapidly improved their technology and threatened its competitive advantage. In 2003 Hitachi had to restrict its licensingpolicy if it did not want to be overcome by licensees (Kamiyama et al. 2006).

$For instance, Oracle on December 1997 had seen how its stock price declined by 29% as a consequence of not achieving the

analysts forecast by 0.04$ (even this result was 4% above EPS for the same quarter in last year). (Skiner & Sloan, 2002). AlsoProcter & Gamble has seen how its stock price was reduced by 30% when they warned that the company would not beat theanalysts forecast in the first quarter of 2000. The same warning before the second quarter of that year generated an additionalreduction on the stock price of 10% and the CFOs dismissal (Duncan, 2001).


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They recognized that they are willing to inflate current earnings in order to achieve

them and that they prefer to manipulate earnings using real activities rather than using

accruals.

Motivated by these two apparently unrelated trends this paper wants to shed light on the

relationship between the companys financial situation and its licensing strategy. In

particular, my objective is to examine 1) whether managers license-out their technology

to inflate current earnings and 2) what are the long-term consequences for the

companies that took the licensing decision under pressure to meet analysts forecast. I

argue that managers that feel the pressure to attain the analysts forecast will have

incentives to inflate current earnings and, thus, engage in myopic management. Since

myopic managers put more emphasis on the short term than in the long run, I expect

that, at the time to make the licensing decision, managers will overestimate the revenue

effect (short-term effect) while underestimate the dissipation effect (long-term effect).

An example of the overestimation of the revenue effect is the declaration of Daniel M.

McGavock, managing director of intellectual property consulting firm Intercap: On

one hand, you dont want to abandon your patents ability to exclude competitors from

your market. But, on the other hand, you could be talking about hundreds of millions of

dollars in new revenue from strategic licensing, not to mention a host of strategic

benefits(Kline, 2003).

This distortion will generate that, under pressure for inflating short-term earnings,

managers license out more technology than the optimal and/or license it under not

appropriate situations and/or license out inappropriate technology and this, in turn, will

imply negative long-term consequences.

I test my hypotheses using a panel of 107 U.S. high-tech companies during 1998-2009

(1,281 observations). Licensing data is one of the strength points of this paper. It has

been collected from four different sources: Prompt Database, Google, Highbeam

Research and SDC Platinum. This extensive search has delivered 1,729 licensing

agreements4of which 840 correspond to license in, 716 to license out and 173 to

cross licensing.

To test the first hypothesis I followed the same procedure as in Bushee (1998). I

estimated a logit model explaining the probability of increasing the number of licensing

out contracts from period t-1 to period t using as main independent variable a dummy

that captures whether the company has or has not achieved analysts forecasts in period

%&'() #*% (+,-'.+'/ 0/1--2-'3. 4-1- ,5((-,3-6 7152 89: ;(03+'


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t-1. Results confirm that companies are more likely to license out their intellectual

property when they are not able to achieve analysts forecasts.

To provide evidence about the second hypothesis I compared the market shares

evolution of a) companies that were not able to achieve analysts forecast in the

previous year with b) companies that were not able to achieve analysts forecast and

have increased the number of licensing out contracts with respect to the previous year

and with c) companies that have increased the number of licensing out contracts and

that were able to achieve analysts forecast in the previous year. Results show that 1)

companies that have increased the number of licensing out contracts with respect to the

previous year present a decreasing market shares trend in the following two years, 2)

this decreasing market shares trend is stronger for companies that license out their

technology and were not able to achieve analysts forecast than for companies that

overcome the earnings threshold.

This paper contributes to innovation literature that examines the strategic drivers of

technology licensing. Prior research has identified economic as well strategic

motivations for licensing (Shepard, 1987; Katz & Shapiro,1985; Gallini, 1984;

Rockett,1990; Lichtenthaler, 2007). I extend this research showing that the pressure to

achieve analysts forecasts is also a potential determinant to license out technology.

I also contribute to the Myopic Management Theory. I proposed a new real activity to

inflate current earnings. Prior research had identified several activities that managers

use to engage in myopic management (Aaker ,1991; Roychowdhury, 2006; Moorman &

Spencer, 2008; Chapman & Steenburgh, 2009). I extended this literature showing that

licensing-out technology can be used as a real activity that increases current earnings at

the expense of reducing market share in the incoming years.

The remainder of this paper is organized as follows. Section 2 presents the theoretical

background regarding Myopic Management and Licensing. Section 3 develops the

hypotheses to be tested. Section 4 describes the methodological analysis. Section 5

reports and discusses the results. Section 6 concludes.


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2. THEORICAL BACKGROUND

2.1. Licensing Theory

Over the last decades companies have moved from protecting aggressively their

knowledge to license it (Vishwasrao, 2004). Nowadays, licensing is the most important

way of technology transfer and companies are increasingly working on developing an

efficient corporate structure to facilitate knowledge transfer (Arora et al. 2011)

The most important motivation for licensing out technology is the revenue it generates.

That is, the present value of the fixed fee and/or the royalties that the licensee has to pay

to the licensor. Surveys conducted by Gambardella (2005), Robbins (2008) and Zuniga

& Guellec (2009) corroborate that earnings revenue is, by far, the main motivation for

companies to license-out technology.

However, in order to really generate profits from licensing, three important points

should be considered before taking the decision. First, managers should know what are

the downstream assets needed to exploit the innovation. For companies that do not own

the marketing and distributions capabilities would be more difficult to benefit from

their innovations. Hence, license out their technology would be a good idea (Teece,

1986). Second, managers should be conscious of what are the transaction costs related

with the licensing contract. If the transaction costs generated with the buying of

downstream assets are greater than the transaction costs related with the licensing of

technology the best strategy would be to license out technology (Arora & Fosfuri,

2003).5 Finally, managers should pay attention to the extent of the profit-dissipation

effect. This effect refers to the reduction in the licensors profit as a consequence of the

additional competition in the product market or of an existing firm becomes more

aggressive. Therefore, a company should license its technology only if the revenueeffect (net of transaction costs) is greater than the profit-dissipation effect.

In order to limit the extent of the latter effect researchers found that it is better to license

out technology when the strength of the patent protection is high (Cohen et al., 2000;

Arora and Ceccagnoli, 2006), when intellectual property refers to general technologies

5 In general, these contracts are distinguish by: 1) higher search costs resulting from looking for suitable licensees and/or licensors,

2) the existence of information asymmetries between the parties and the consequent incomplete contracts, 3) the bargaining

difficulties due to the risk of giving information before signing the contract and 4) the lack of an established mechanism for pricing

technologies.


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2.2. Myopic Management.

Effective management requires to be focused on the long-term and to take projects that

generate the highest expected net present value (Mizik, 2009). However, the importance

that the market gives to current earnings forces managers to put more emphasis on

strategies that result in immediate pay-offs (Dechow, 1994; Degeorge et al. 1999).

Usually managers compensation as well their evaluation are based on the companys

stock price (Mizik, 2009) and this, in turn, depends on the achievement of three

earnings benchmarks: zero earnings, the prior comparable periods earnings and the

analysts forecasts (Degeorge et al. 1999). The pressure to meet these thresholds gives

managers incentives to manipulate results and, thus, inflate current earnings. In fact,

evidence has shown that this pressure has already changed the distribution of earnings

reported: only few firms report losses while so many report small profits (Dechow,

Richardson & Tuna, 2003). In a survey conducted by Graham et al. (2005) financial

executives declared that in order to avoid negative surprises they are willing to inflate

current earnings and that they prefer to do so by using real activities manipulation rather

than using accruals. Even though the objective under both strategies is to inflate current

earnings their implications and costs differ greatly. Namely, manipulating discretionary

accruals supposes to change the time at which earnings are recognized, not to modify

neither the quantity nor the temporal flow of economic profits. However, real activities

manipulation always implies engaging in Myopic Management. Basically, it consists on

using some activities to inflate current earnings at the expense of sacrificing the long-

term firm value. Then, for attaining the same objective, managers prefer to use the

strategy that implies worse consequences.

Prior research on Myopic Management has mainly concentrated on the reduction of

R&D investment as well on the factors that influences this practice. In particular,

evidence has shown that managers reduce R&D expenditures when they cannot ensure

positive earnings for the next year (Baber et al.,1991), when the time of retirement is

close (Dechow and Sloan, 1991), when the presence of institutional ownership is not

high (Bushee, 1998) and when managers have to repurchase stock to avoid EPS

dilution.

However, managers are also using other activities to inflate current earnings. In

particular, Aaker (1991) found that companies reduce marketings expenditures used to

enhance brand value while increasing the ones oriented to inflate temporarily the


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results. Bartov (1993) and Herrmann et al. (2003) show that managers increase the sell

of fixed assets; Roychowdhury (2006) demonstrated that managers use price discounts

and zero financing strategies, overproduce and reduce discretionary expenses and

Moorman and Spencer (2008) show that managers delay the introduction of innovations

in order to not reduce earnings.

Although the evidence of myopic management is more established, only few studies

have quantified the financial future impact of engaging in this practice. Pauwels et al.

(2004) demonstrated that sales promotions imply negative long-term effects on !rm

value. Gunny (2005) found that myopic practices are associated with lower ROA in the

subsequent year. Mizik and Jacobson (2007) found that two years after reducing the

marketing expenditure the company began to have negative earnings and that in the fifth

year the market value of the company was reduced by 25%. In the same vein, Mizik

(2009) found that companies that have reduced the marketing expenditure present

greater negative abnormal returns in the future than companies that did not. Chapman &

Steenburgh (2009) show that companies can use marketing to increase quarterly net

income by up to 5% but that this strategy will be reflected in a 7,5% reduction of the

next period quarterly net income.

3. HYPOTHESIS DEVELOPMENT.

Financial analysts are increasingly influencing companies strategies. The severe

consequences of missing their forecast, even by a small quantity, have caused that

managers modify the normal business of their companies (Skinner & Sloan, 2002). In

order to avoid stock prices reduction, to maintain their job and to enhance their

reputation they are even willing to engage in not efficient projects that put at stake the

long-term firm performance (Degeorge et al., 1999). No doubt, licensing out could be

one of these inefficient projects because the trade-off that it implies. Although the main

motivation to license is the revenue it generates, companies only will take benefits from

it if the profit-dissipation effect is lower than the revenue effect. However, if managers

are facing pressure to beat analysts forecast they will put more emphasis on the short

term and, thus, they will have incentives to inflate current earnings even at the expense

of long-term firm performance.

Stein (1989) argues that for managers interested in manipulate short-term earnings the

easier is to reduce intangible assets expenditures because 1) they are not separately


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companies that have engaged in myopic management will present a reduction in their

market share in comparison with the companies that do not.

H2. Companies that license out technology under a pressure situation show a shrinking

market share in the next years compared to companies that do not engage in such

behavior.

4. METHODOLODY

4.1. SAMPLE.

The empirical analysis is based on a sample of innovative U.S. companies. Theoperative criterion to select the sample was the following. First, I focused on the

companies (140) with the larger number of granted patents at the U.S.P.T.O. during the

period 1990-20096. Licensing is not a common established practice in many companies

and sectors. As the main objective of this paper is to analyze if companies license out

their technology to inflate current earnings I focus on companies that own the raw

material to do it: companies with technological assets. Second, because of their rich

information environment and because the size of their market for technology, I

narrowed the initial sample to U.S. companies. Licensing data is per se difficult to find,

however, this search process would be even worse in countries where the information

about companies is less accessible and where markets for technology are no so large.

Third, I used annual data. Since a number of companies present quarterly losses

because the intrinsic seasonality related with their business, I prefer to focus on yearly

analysts forecast. From my point of view, it imposes more pressure on managers and,

thus, more incentives to manage earnings.

4.2. DATA

Licensing data were obtained from four sources: Prompt database, HighBeam Research,

Google and SDC Platinum. The first three sources are based on press news while the

last one is an established licensing database. In Prompt, Google News and HighBeam

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Research I looked for licensing agreements using key words. In particular, I have

always introduced the term licensing agreement plus the company name. For Prompt

and HighBeam Research, I read all the resulting news, in Google I checked them until

the 20th page. After reading the news, I codified these agreements as licensing out,

licensing in or cross licensing. Afterwards, I matched these licensing agreements

with the ones that I obtained from SCD Platinum (For my sample and for the period of

study I found 154 licensing agreements). The final licensing output was 1,729 licensing

agreements of which 840 correspond to license in, 716 to license out and 173 to

cross licensing.

Afterwards, I have matched the licensing data above with the Compustat financial data

and with the analysts forecast data offered by DataStream. These matches reduce the

sample to 107 companies7during the period 1998-2009. (1,2818observations)

4.3. METHODOLOGY.

To test the first hypothesis, I follow the same procedure as in Bushee (1998). First, in

order to create a model for observing changes I took differences for all variables with

respect to the previous year. Second, I converted these changes in dummies: they are

equal to one if they increased with respect to the previous year, equal to zero otherwise.

3) I use a logit9model to predict the probability of observing an increase in the number

of licensing agreements.

To check the second hypothesis I compared the market shares evolution of the

following groups: a) companies that were not able to achieve analysts forecast and that

have increased the number of licensing out contracts with the rest of the sample b)

companies that were not able to achieve analysts forecast in the last year with the rest

of the sample c) companies that have increased the number of licensing out contracts

and that have overcome the analysts thresholds in the previous year with the rest of the

sample.

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4.4. VARIABLES DESCRIPTION.

Dependent variable:

The dependent variable that I use (INCLICOUT) is a dummy variable that equals one if

the firm increases the number of licensing contracts relative to the prior year, and zero if

the firm maintains or decreases them. I decided to use a binary variable because of two

main reasons. First, I consider that is unlikely that the magnitude of the change in the

number of licensing agreements be a linear function of difference between actual

earnings per share and the analysts forecast earnings per share. Second, because in

order to inflate current earnings, the fact that a company increases the number of

licensing contracts by seven does not mean that it is increasing more their current

benefits than a company that just increases the number of licensing out contracts by one.

Everything depends on the economic condition of the contracts. Therefore, in this case,

the magnitudes change could be meaningless10.

Independent variable

The independent variable (EARNINGS_PRESSURE) is an indicator variable equal to

one if the company has not beat the analysts forecast in the previous year (or if it

presents exactly the same results as the analysts predict) and equal to zero if the

company has surpassed the analysts forecast in the previous year.

This variable was developed in the following way. First I calculated the difference

between the actual earnings per share and the mean of the consensus of analysts

forecast during the fiscal year before the results presentation11. I considered that if this

difference is equal to zero or negative, managers will suffer more pressure to attain the

analysts forecast next year. Second, I create a dummy variable that reflect the latter

argument. This variable is equal to one if the difference between the actual earnings per

share and the mean of the consensus of analysts forecast is zero or negative, and zero

otherwise. Evidence has shown that the consequences of missing analysts have been

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5. RESULTS

***INSERT TABLE 2 HERE***

Table 2 shows the main descriptive statistics and correlations of the variables.

Companies establish by mean 0.502 licensing-out contracts per year and they are

involved, as maximum, in 13 licensing out contracts by year.

Table 3 and Table 4 present more detailed information of the licensing out contracts in

the sample. In particular, Table 3 shows that the most of the companies (73.77%) are

not involved in any licensing-out contract per year while the 10.62% of the companies

are involved in less than six per year. This corroborates previous findings that show that

Markets for Technologies are still underdeveloped (Gambardella et al. 2006; Arora et

al. 2010). Table 4 provides evidence that companies belonging to the Electronic &

Other Electrical Equipment, Machinery and Chemical & Allied Products SIC

codes are the ones that license out more technology. Anand & Khanna (2000) have

already shown that these sectors were the three more important in terms of licensing.

However, the proportions of licensing contracts in each sector are quite different. In my

database Electronic & Other Electrical Equipment is the sector more important in terms

of licensing (represents the 22.33% vs 18.15% in Anand & Khanna) followed by

Machinery (19.59% vs 24.2%) and by Chemical (14.05% vs 38.5%). These differencescould be due to a change in the licensing trend over the last 10 years in which the

Chemical sector lost importance.




Table 5 presents a more detailed description of the difference between actual earnings

per share and the mean of the analysts forecast during the year before to the result

presentation (EARNINGS_PRESSUREin magnitude terms). Companies that surpass the

analysts forecast report, as maximum, earnings per share 4.82 higher than the threshold

while companies that fail to beat them report, as maximum, earnings per share 13.7

lower than the analysts forecast. This asymmetry makes that the mean of this variable

be negative (-0.1121) while the median is positive (0.01). Its negative skewness (-6.7)shows that the mass of the distribution is concentrated on the right and that there are


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probability of increasing licensing out. This is in accordance with the Skinner & Sloans

findings (2004) that argues that companies with higher opportunities to growth have

more incentives to engage in myopic management. Contrary to Bushees findings, the

logarithm of the market value (LOGMV) is positively and significantly associated with

the probability of increasing licensing-out contracts20. Therefore, in our case companies

with higher market value are more likely to increase the number of licensing out

contracts. Gross Domestic Product (INCGDPUSA) has a positive relation with the

probability of licensing-out, hence, when GDP is increasing companies will have more

probabilities to license out technology. Finally, over the years it is more likely to

increase the licensing-out of technology. This corroborates that licensing has increased

over the last years21.

Hypothesis 1 proposes that earnings pressure in the previous year makes more likely

that companies increase the number of licensing out contracts during this year. Model 2

support this hypothesis: the coefficient of earnings pressure is positive and significant at

1%. The probability of increasing licensing-out technology increases from 22.6%

(without earnings pressure) to 30% (with earnings pressure)22. The introduction of this

variable also generates an increased in the Pseudo R2 from 0.126 to 0.138.

*** INSERT TABLE 8 HERE***


In order to understand the negative sign of the variable Decrease R&D Intensity I run

a logit regression in which this variable is the dependent one. The only difference with

the Increase Licensing Out regression is that I measure earnings pressure in the

current year and not in the previous one. Previous findings show that when managers

suspect that they will not be able to attain earnings thresholds, one of the first things

that they do is to cut discretionary expenses (Stein, 1998; Mizik, 2009). Therefore, as

cutting R&D expenditures has an immediate effect in earnings, I expect that managers

decreased R&D intensity in order to try to inflate current earnings and, thus, meet

analysts forecast. In other words, I expect that they cut R&D intensity before failing to

meet analyst forecasts.

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Table shows that the coefficient of earnings pressure is positive and significant at the

5%. The probability of decreasing R&D intensity increases in 0.0636 when the variableearnings pressure is introduced. That is, the probability of decreasing R&D intensity

goes from 45,6% to 52%. This suggest that, even reducing R&D intensity and

increasing licensing out have different temporal effects, managers could use both in

order to inflate current earnings and try to improve the financial appearance of the

company.

Regarding the second hypothesis, as the main objective is to compare the evolution of

the companies that have suffered earnings pressure and have increased the number oflicensing out contracts I distinguished the observations that satisfy these two conditions

SUSPECT-from the rest of the sample -NO_SUSPECT-. Table 12 shows the existing

differences between the two subsamples attending to several variables that describe the

size of the companies, their investments in R&D and the financial situation. Looking to

this table we can pointed out several things: 1) suspect firms are in mean larger than the

no suspect ones, 2) suspect firms invest more in R&D and have more patents available

to license, 3) suspect firms as maximum establish 5 licensing out agreements while no

suspect firms establish as maximum 13, 4) suspect firms in mean present lower market

value, book value, EBIT, total assets, net income, ROA, ROE, ROI, common equity,

cash flow from operations and long term debt than no suspect firms, 5) suspect firms are

characterized because in mean present higher current liabilities and short term debt than

the no suspect ones. Previous description is in accordance with prior findings that show

that larger companies are the ones that more invest in own research and the ones that

have a lower rate of licensing out (their market share is higher and thus, the profit

dissipation effect could be worse). On the other hand, no suspect firms are the ones that

present a better financial situation, higher market value, higher cash flow from

operations, while suspect companies are the ones that have higher short-term

liabilities.

*** INSERT TABLE 12 HERE ***


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Taking into account this distinction, I compared the evolution of the market shares23of

these two subsamples in the next 2 years.


Table 13 shows that the percentage of no-suspect companies that decrease their market

share is constant over the period (57%) while this percentage is increasing in the group

of suspect companies (from 52% to 61%). In period t seems that suspect companies

perform even better than the non-suspect ones: only 52% of the suspect companies

decrease their market share with respect to previous year while 57% of the non-suspect

ones do so. In period t+1 suspect companies began to perform worse than the non-

suspect: 58,6% against 57,1%. Finally, in period t+2, suspect firms behave much worse

than the non-suspect ones: 61% of suspect companies decrease their market share while

only 57% of the non-suspect companies do so.

In order to analyze if this decreasing trend in the market share is caused by previous

financial problems, and not caused by licensing out, I compared the evolution of the

market share for those companies that were not able to achieve analysts forecast and

for those that have achieved them. Table 14 shows that the percentage of companies that

decrease their market share is stable and similar for both groups (57% for companies

that were able to meet analysts forecast and 58% for the ones that were not able). It

suggests that the decrease in the market share is independent of the financial situation of

the company. Finally, I compared the evolution of the market share for the companies

that have increased the number of licensing out contracts and have achieved the

analysts forecast in previous year (INC_NOPRESSURE=1) with the rest of the sample.

Table 15 shows that those companies present a decreasing market share trend over the

three-year period (from 44% to 53%) while this trend is quite stable (58%) for the rest

of the sample. Thus, from the latter comparisons, we can observe that 1) companies that

have increased the number of licensing out contracts with respect to the previous year

present a decreasing market shares trend in the following two years, 2) the percentage

of companies that decrease market share is greater (8% in the three years) for the

companies that license out technology and were not able to achieve analysts forecast

than for companies that overcome the earnings threshold 3) missing analysts forecast in

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previous year seems to be independent of the decrease in the market share. Graph 1

compares the evolution of the market share of each group for the next two years.

***INSERT GRAPH 1 HERE***

ROBUSTNESS CHECKS

As a robustness checks I also estimated the model using two alternative methods. First,

using the binary variable, results remain significant under the conditional logit model

(Table 16). Second, using as dependent variable the magnitude of the change in the

number of licensing agreements, the earnings pressure variable was also significant

under the ordinary least squares method (Table 17)24.

6. SUMMARY & CONCLUSION.

The main objective of this paper was to shed light on the relationship between the

company financials situation and its licensing strategy. Over last years financial

analysts have changed the way in which managers run the business. The consequences

of missing analysts expectations have been so severe that managers are increasingly

becoming short-term minded. Accordingly, they have improved their creativity and

have employed several real activities to inflate current earnings even at the expense of

long term performance (Aaker, 1991; Bartov, 1993; Herman et al., 2003;

Roychowdhury, 2006; Moorman & Spencer, 2008). From my point of view, licensing

out technology could be one of these real activities because the trade off that it implies.

On one hand, companies will increase their current benefits by the licensing revenues

(net of transaction costs) but, on the other hand, companies could reduce their market

share or their price-cost margin because they are creating their own competition in the

product market. Since myopic managers by definition put more emphasis on the short

term (revenue effect) than in the long term (dissipation effect), I expect that managers

under the pressure to beat analysts forecast license out their technology just taking into

account the revenue effect. Stein (1989) argue that the easier way to inflate short term

earnings is to reduce intangible assets expenditures because they are not separately

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recorded in the balance sheet and because they are not directly related to production.

Licensing out technology also satisfies these two conditions: 1) companies have not the

obligation of recording licensing revenues as a separate item (then, external observers

just perceive an increase in the earnings) and 2) they do not affect to production in the

short term. Accordingly, from my point of view, licensing out intellectual property

could provide managers the opportunity to inflate current earnings and to take

advantage of them for a period. Nevertheless, companies that engage in such practices

will face negative consequences in the long term.

Based on the latter argument I proposed that 1) managers are more likely to license out

technology when they were not able to attain analysts forecast in the previous year and

that 2) companies that license out technology under a pressure situation will reduce their

market share in the incoming years.

I test these two hypotheses using a panel of 107 U.S. companies during a twelve year

period (1,281 observations). Results confirm that 1) when companies were not able to

achieve analysts forecast in previous years are more likely to increase the number of

licensing out contracts and that 2) companies that license out their technology under a

pressure situation will show a shrinking market share in the next two years compared to

companies that did not. Results also corroborates previous findings that show that

managers decrease R&D expenditures when they suspect that they will not be able to

achieve earnings thresholds in next period.

This paper contributes to innovation literature in two ways. First, because it shows that

the pressure to achieve analysts forecasts is also a potential determinant to license out

technology. Second, because it is the first time that the negative long-term

consequences of licensing (dissipation effect) have been empirically tested (dissipation

effect). This paper also contributes to the Myopic Management Theory in two ways.

First, because it shows that licensing out technology could be used as a real activity that

increases current earnings at the expense of reducing market share in the incoming

years. Second, because it amplifies the temporal horizon of the real activities. As the

best of my knowledge, researchers have focused on the strategies that managers took in

the period previous to the negative earnings surprise. However, this paper suggests that

managers can also follow strategies that have not an immediate effect.

This paper has also several limitations. The first one is related with the earnings

pressures proxy. I considered that companies are supporting earnings pressure if they

have failed to meet analysts forecast (or just matched them) in previous year. However,


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this proxy is not very precise. Ideally, I would have to know the exact moment at which

managers realized that they will not be able to achieve analysts forecast because it is in

this moment when they will begin to take decisions in order to inflate current earnings.

Indeed, it is quite likely that firms that miss analysts forecast have been manipulating

results and dealing with financial difficulties for some time. Nevertheless, it is

impossible to know the exact moment at which companies decide to behave myopically

and to differenciate real earnings from the inflated ones. There are also two limitations

regarding licensing data. First, even though I suspect that the most of the licensing

contracts are related with a patent (patent protection incentivates licensing) I cannot

ensure that it is true. Therefore, licensing agreements could be related with know how,

copyrights, trade secrets or patents. Second, I treated any increase in the number of

licensing out agreements as suspect without knowing specific information about the

contract. In particular, I have defined suspect companies as the ones that have missed

(or have matched) analysts forecast in the previous year and the ones that, at the same

time, have increased the number of licensing contracts from previous years. However, I

cannot ensure that these new licensing contracts are dangerous for the companies

because I do not know if contracts are related with core technology, if entail direct

competitors or if, for instance, both parties operates in the same geographical area. In

the part of future research, I propose to introduce some measures to avoid this problem.

Finally, the results of this paper cannot be generalized to the whole population of

companies. Licensing out is still not a common practice in all the industries and

countries. Companies in the sample are characterized by four main things: 1) they have

the intellectual assets needed to trade in the Markets for Technology, 2) they belong to

industries in which licensing agreements are frequently established, 3) they operate in

the most developed environment (U.S.) for Markets for Technology and 4) they operate

in a country where analysts forecast imposes much pressure. Therefore, results cannot

be applied to other companies that do not satisfy the latter characteristics.

Regarding future research, my plan is the following. First of all, I would like to add to

my data the economic conditions of licensing contracts. Myopic management can also

be reflected in the way in which the fixed fee and the royalties are established. In

particular, I would expect that firms engaged in myopic management establish greater

fixed fee and lower royalties than optimal in order to borrow earnings from the future.

Second, it would be interesting to know the sector at which belongs each company

involved in the licensing contract or the technological proximity among them. It would


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"$

allow to me to analyze deeper the cautions that managers have in order to limit the

dissipation extent. I would expect that companies that are supporting earnings pressure

do not be cautious and license out technology to companies that operates in the same

sector or that are technologically close. Finally, I am interested in analyzing the

application of grant back clauses in licensing contracts. Under these clauses the licensee

is required to disclose and transfer all the improvements made in the licensed

technology to the licensor. No doubt, those provisions limit the extent of the dissipation

effect and motivate companies to license out technology. However, licensees are not

conformable with their application and try to avoid it as far as they can. Accordingly, I

would expect that companies that are supporting the pressure to meet earnings

benchmarks will want to negotiate fast and, thus, will not engage in discussions

regarding rant back clauses.

From a practical point of view, this paper gives some insights that companies should

take into account. First, it is needed to educate managers about the potential long-term

consequences of licensing. It is important that they analyze the decision to license with

caution, focusing on the net benefits of the strategy. Second, it is required to change the

way in which managerial compensation is established in order to incentive managers to

engage in projects that maximize the sum of discounted future profits. Third, the results

also highlight the negative consequences of a decentralized licensing structure. If

companies had an independent licensing department in charged on taking those

decisions and whose incentives be different from the ones of the economic department,

managers could not license out their technology just to benefit from the inflated current

earnings. Finally, it would be good that society meditate about the negative

consequences of imposing earnings pressure to managers. Clearly, this pressure do not

allow managers to focus on long term strategies putting at stake the long term

productivity of companies and, in turn, the one of whole society.


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"%

TABLES

TABLE 1. COMPANIES IN THE SAMPLE

1 3M CENTER 55 HONEYWELL INTERNATIONAL INC

2 ADOBE SYSTEMS INC 56 HUMAN GENOME SCIENCES

3 ADVANCED MICRO DEVICES 57 ILLINOIS TOOL WORKS

4 AIR PRODUCTS AND CHEMICALS INC 58 IMATION CORPORATION

5 ALCOA INC 59 INCYTE CORPORATION

6 ALLERGAN INC 60 INTEL CORPORATION

7 ALTERA CORPORATION 61 INTL BUSSINES MACHINES CORPORATION

8 AMGEN INC 62 INTL FLAVORS & FRAGANCES

9 ANALOG DEVICES 63 ISIS PHARMACEUTICALS INC

10 APPLE INC 64 ITT CORPORATION

11 APPLIED MATERIALS INC 65 JDS UNIPHASE CORPORATION

12 AT&T INC 66 KIMBERLY CLARK CORP

13 AVERY DENNISON CORP 67 KLA-TENCOR CORPORATION

14 BAKER HUGHES INCORPORATED 68 LAM RESEARCH CORPORATION

15 BARD (CR) INC 69 LATTICE SEMICONDUCTOR CORPORATION

16 BECTON DICKINSON & CO 70 LEAR CORPORATION

17 BOEING CO 71 LEXMARK INTL INC

18 BORGWARNER INC 72 LOCKHEED MARTIN CORPORATION

19 BOSTON SCIENTIFIC CORPORATION 73 LUBRIZOL CORPORATION

20 BRISTOL MYERS SQUIBB CORPORATION 74 MATTEL INC.

21 BROADCOM CORPORATION 75 MEDTRONIC INC.

22 BRUNSWICK CORPORATION 76 MERCK & CO.

23 CABOT CORPORATION 77 MICRON TEHCNOLOGY INC

24 CADENCE DESIGN SYSTEMS INC 78 MICROSOFT CORPORATION

25 CALLAWAY GOLF COMPANY 79 MOLEX INCORPORATED

26 CATERPILLAR INC 80 NATIONAL INSTRUMENTS CORPORATION

27 CIENA CORPORATION 81 NATIONAL SEMICONDUCTOR CORP

28 CIRRUS LOGIC, INC. 82 NCR CORPORATION

29 CISCO SYSTEMS INC 83 NIKE INC -CL B

30 COLGATE PALMOLIVE CO 84 NORDSON CORPORATION

31 CONEXANT SYSTEMS INC 85 NORTEL NETWORKS CORP

32 CORNING INC 86 NORTHROP GRUMMAN CORPORATION

33 CYPRESS SEMICONDUCTOR CORPORATION 87 NOVELLUS SYSTEMS INC

34 DANA HOLDING CORPORATION 88 NVIDIA CORPORATION

35 DEERE & CO 89 ORACLE CORPORATION

36 DELL INC 90 PFIZER INC

37 DOW CHEMICAL CO 91 PPG INDUSTRIES INC

38 EASTMAN CHEMICAL COMPANY 92 PRAXAIR INC

39 EASTMAN KODAK CO 93 QUALCOMM INC

40 EATON CORPORATION 94 RAMBUS INC

41 EI DU PONT DE NEMOURS 95 RAYTHEON CO

42 ELI LILLY & CO 96 ROCKWELL AUTOMATION


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"*

43 EMC CORPORATION 97 SANDISK CORPORATION

44 EMERSON ELECTRIC CO 98 TERADYNE INC

45 EXXON MOBIL CORPORATION 99 TEXAS INSTUMENTS INCORPORATED

46 FMC CORPORATION 100 TRIMBLE NAVIGATION LTD

47 FORD MOTOR CO 101 UNISYS CORPORATION

48 GENERAL ELECTRIC COMPANY 102 UNITED TECHNOLOGIES CORPORATION

49 GENTEX CORPORATION 103 WESTERN DIGITAL CORPORATION

50 GOODRICH CORPORATION 104 WEYERHAEUSER COMPANY

51 GOODYEAR TIRE & RUBBER 105 WHEATERFORD INTL LTC

52 HALLIBURTON CO 106 XEROX CORPORATION

53 HARRIS CORPORATION 107 XILINX INC

54 HEWLETT-PACKARD CO

TABLE 3. LICENSING OUTAGREEMENTS BY YEAR

!"#$%&

(%& )%*& +&%,- .%&/%01 2"#-

0 945 73.77 73.77

1 194 15.14 88.91

2 68 5.31 94.22

3 35 2.73 96.96

4 21 1.64 98.59

5 8 0.62 99.22

6 4 0.31 99.53

7 1 0.08 99.61

9 2 0.16 99.77

10 1 0.08 99.84

12 1 0.08 99.92

13 1 0.08 100

TABLE 2. DESCRIPTIVE STATISTICS & CORRELATIONS

MEAN SD MAX MIN 1 2 3 4 5 6 7

1.LICOUT 0.502 1.173 13 0 1.000

2.EARNINGS_PRESSURE -0.112 0.857 4.820 -13.700 0.005 1

3.RDINT 45.691 70.457 659.468 0.004 -0.099 -0.0111 1

4.CL 4,516.180 8,721.251 141,579 12.751 0.173 0.0223 -0.232 1

5.GO 5.957 45.140 1,575 -118.636 0.148 0.0014 -0.0197 -0.013 1

6.LOGMV 9.235 1.641 13.139 1.282 0.148 0.1517 -0.1175 0.5355 0.0142 1

7.GDPUSA 155.319 5.467 163 145 -0.022 -0.0288 0.0246 -0.0818 0.0051 0.037 1


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"@

TABLE 6. EARNINGS_PRESSUREDISTRIBUTION

Freq. Percent Cum.

POSITIVE 716 55.89 55.89

NEGATIVE 565 44.11 100

TOTAL 1,281 100

TABLE 4. LICENSING AGREEMENTS BY SIC-2

SIC2 Definition Freq. Percent Cum.

36 Electronic & Other Electrical Equipment 286 22.33 66.28

35 Machinery 251 19.59 43.95

28 Chemical & Allied Products 180 14.05 19.67

37 Transportation Equipment 132 10.3 76.58

38 Instruments & Related Products 132 10.3 86.89

73 Business Services 84 6.56 100

39 Miscelaneous Manufacturing Industries 48 3.75 90.63

25 Furniture & Fixtures 24 1.87 3.75

26 Paper & Allied Products 24 1.87 5.62

30 Rubber & Miscelaneous Plastic Products 24 1.87 22.48

67 Holding & Other Investments Offices 24 1.87 93.44

1 Agriculture 12 0.94 0.94

24 Lumber & Wood Products 12 0.94 1.87

29 Petroleum & Coal Products 12 0.94 20.61

32 Stone, Clay & Glass Products 12 0.94 23.42

33 Primary Metal Industries 12 0.94 24.36

48 Communications 12 0.94 91.57

TABLE 5. EARNINGS_PRESSURE

Percentiles Smallest

1% -3.43 -13.7

5% -0.97 -11.59

10% -0.51 -7.17 Obs 1,281

25% -0.14 -6.78

50% 0.01 Mean -0.1121

Largest Std. Dev. 0.86

75% 0.08 3.05

90% 0.27 3.35 Variance 0.73

95% 0.47 4.15 Skewness -6.7

99% 1.51 4.82 Kurtosis 89.77


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"B

TABLE 9. MARGINAL EFFECTS AFTER LOGIT.

INCREASE LICENSING OUT(1) (2)

INCLICOUTNO EARNINGS

PRESSUREEARNINGSPRESSURE

DECRDINT (d) -0.0954*** -0.102***

(0.0250) (0.0264)

INCCL (d) 0.0567* 0.0659*

(0.0283) (0.0290)

INCGO (d) 0.0492 0.0602*

(0.0271) (0.0286)

INCGDPUSA (d) 0.181*** 0.165***

(0.0218) (0.0247)

LOGMVE 0.0275** 0.0271**

(0.00959) (0.0102)

YEART 0.0287*** 0.0305***

(0.00313) (0.00381)

SIC28 (d) 0.0253 0.0115

(0.0530) (0.0550)

SIC35 (d) -0.0217 -0.0113

(0.0431) (0.0468)

SIC36 (d) -0.0407 -0.0589

(0.0437) (0.0447)

SIC37 (d) -0.0925* -0.102**

(0.0424) (0.0395)

SIC38 (d) -0.0240 -0.00373

(0.0514) (0.0559)

SIC73 (d) -0.0159 -0.0373

(0.0596) (0.0566)

EARNINGSPRESSURE (d) 0.0740**

(0.0249)

N 1163 1163

pseudo R2 0.126 0.138

Marginal effects; Standard errors in parentheses(d) for discrete change of dummy variable from 0 to 1*p < 0.05, **p < 0.01, ***p < 0.001


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"C

TABLE 10. LOGIT REGRESSION

DECREASE RD INTENSITY(1) (2)

DECRDINTNO EARNINGS


INCCL -0.00618 0.0242

(0.126) (0.127)

INCGO 0.0186 0.00531

(0.116) (0.118)

INCGDPUSA 0.0880 0.104

(0.116) (0.118)

LOGMVE 0.00321 0.0139

(0.0405) (0.0401)

YEART -0.0754*** -0.0679***

(0.0152) (0.0155)

SIC28 0.252 0.272

(0.203) (0.203)

SIC35 0.0581 0.0713

(0.183) (0.182)

SIC36 0.0716 0.0938

(0.156) (0.153)

SIC37 -0.0488 -0.0303

(0.192) (0.192)

SIC38 0.354* 0.394**

(0.144) (0.144)

SIC73 0.0158 0.0302

(0.207) (0.199)

EARNINGSPRESSURE_T 0.256*

(0.127)

_cons 0.0823 -0.217

(0.408) (0.409)

N 1170 1170

pseudo R 0.015 0.018

Standard errors in parentheses*p < 0.05, **p < 0.01, ***p < 0.001


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$D

TABLE 11. MARGINAL EFFECTS AFTER LOGIT.

DECREASE RD INTENSITY(1) (2)

DECRDINTNO EARNINGS


INCCL (d) -0.00154 0.00600

(0.0312) (0.0315)

INCGO (d) 0.00461 0.00132

(0.0288) (0.0292)

INCGDPUSA (d) 0.0219 0.0258

(0.0287) (0.0293)

LOGMVE 0.000797 0.00345

(0.0101) (0.00995)

YEART -0.0187*** -0.0169***

(0.00379) (0.00384)

SIC28 (d) 0.0629 0.0678

(0.0507) (0.0507)

SIC35 (d) 0.0144 0.0177

(0.0455) (0.0453)

SIC36 (d) 0.0178 0.0233

(0.0387) (0.0380)

SIC37 (d) -0.0121 -0.00753

(0.0474) (0.0477)

SIC38 (d) 0.0883* 0.0983**

(0.0358) (0.0357)

SIC73 (d) 0.00393 0.00751

(0.0514) (0.0496)

EARNINGSPRESSURE_T(d)

0.0636*

(0.0313)N 1170 1170

pseudo R2 0.015 0.018

Marginal effects; Standard errors in parentheses(d) for discrete change of dummy variable from 0 to 1

*p < 0.05, **p < 0.01, ***p < 0.001


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$"

TABLE 15. DECREASE MARKET SHARE

INC_NOPRESSURE=0 INC_NOPRESSURE=1

T 0.582897033 0.444444444

T+1 0.578458682 0.505154639

T+2 0.581632653 0.528735632

GRAPH 1. MARKET SHARE EVOLUTION

TABLE 16. ROBUSTNESS CHECKS.CONDITIONAL LOGIT. INCREASE LICENSING OUT.

(1) (2)

INCLICOUTNO EARNINGS PRESSURE EARNINGS PRESSURE

DECRDINT -0.609*** -0.644***

(0.153) (0.163)

INCCL 0.438** 0.438*

(0.164) (0.172)

INCGO 0.247 0.313

(0.158) (0.166)

INCGDPUSA 0.975*** 0.865***

(0.151) (0.159)

LOGMVE 0.0796 0.114

(0.122) (0.133)

YEART 0.165*** 0.174***

(0.0222) (0.0267)

EARNINGSPRESSURE 0.470**

(0.171)N 1163 1163

pseudo R2 0.159 0.180

Standard errors in parentheses*p < 0.05, **p < 0.01, ***p < 0.001


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$$

TABLE 17. OLS, USING THE MAGNITUDE CHANGE IN THE

NUMBER OF LICENSING OUT CONTRACTS AS DEPENDENT VARIABLE

(1) (2)

NO EARNINGS PRESSURE EARNINGS PRESSURE

DECRDINT 0.00123 -0.00901

(0.0790) (0.0857)

CUTCL -0.0000430 -0.0000477

(0.0000232) (0.0000257)

CUTGO 0.00193** 0.00197**

(0.000586) (0.000602)

CUTGDPUSA 0.00888 0.00120

(0.0149) (0.0166)

LOGMVE 0.00987 0.00677

(0.0255) (0.0278)

YEART -0.0262* -0.0155

(0.0130) (0.0152)

SIC28 -0.0181 -0.0633

(0.143) (0.154)

SIC35 -0.00294 0.0150

(0.134) (0.144)

SIC36 -0.0281 -0.0470

(0.129) (0.138)

SIC37 -0.0121 -0.00610

(0.161) (0.173)

SIC38 -0.0249 0.00255

(0.156) (0.168)

SIC73 -0.0112 -0.0562

(0.181) (0.194)

EARNINGSPRESSURE 0.178*

(0.0898)

_cons 0.100 -0.0241

(0.262) (0.308)

N 1128 1027

pseudo R


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$%

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