Do incentive payments encourage
innovation? A meta-analysis study
Presented by:
Zahra Lotfi
Friedrich Schiller University
MAER-NET 2015
1
Outline
• Introduction
• Literature
• Method
• Results
• Conclusion
2
Introduction
• Human capital has been considered as a key resource
in innovative organizations. Successful innovative
organizations should know how to reward people
(Gupta and Singhal, 1993).
• Reward systems are effective in fostering
innovation: long-term perspective, autonomy
and motivation to take risks.
• Which type
payments?
of
reward
system?
Variable
• In variable payment, the payment of individuals
are linked to firm performance, like bonus and
stock option.
3
Introduction
• Previous studies about the effect of variable pay on innovation:
variable payments are proper stimulant for innovation activity
create long-term commitment
variable payment can not encourage innovation focus on short
term, reduce intrinsic motivation, autonomy and freedom.
• A systematic overview of studies that examine the relation
between variable payments and innovation is needed
• Meta-analysis: summerizes and integrates the results of emprical
studies
4
Literature
Positive impact of variable compensation on innovation:
• Variable monetary reward considered as an effective motivational tool for
improving innovation.
• Apply principal-agent theory: To align the interests of agents and
principals, the agents‘ payment linked to firm performance.
• Regarding innovation activities, the payments of agents should be linked
to innovation activity of firms rather than firm‘s financial performance
(Balkin et al., 2000)
• Long-term payment such as stock option has been found to be effective in
promoting innovation (Francis et al., 2010; Lerner and Wulf, 2007; Yanadori and Marler,
2006)
5
Literature
Positive impact of variable compensation on innovation:
• Long-term pay can encourage employees to focus on the firm‘s long-term
success (Chang et al. 2015).
• Long-term pay decrease the fear of failure in executives (Francis et al., 2010).
• Stock option can prevent myopic decision of CEOs (Sanders and Hambrick 2007).
• Stock option can encourage CEOs to take risky decisions (Sanders and
Hambrick 2007).
6
Literature
Negative impact of variable compensation on innovation:
• Variable compensation negatively affect innovation.
• Monetary compensation can undermine intrinsic motivation of people in
interesting tasks (e.g. Amabile, 1998; Fehr and Gächter, 2001; Frey and Oberholzer-Gee,
1997; Lepper et al., 1973).
• When intrinsically motivated people are paid stock options or bonus for
doing interesting tasks like innovation, they might lose their interests in
what they are doing and only focus on the reward.
• When monetary rewards are perceived as controlling, people are under
pressure to achieve specific goals  so their intrinsic motivation get
reduced in interesting tasks.
7
Literature
Negative impact of variable compensation on innovation:
• Multitasking problem Individuals focus on the tasks that increase firm’s
share price and value in short run in order to increase their payment, so
it’s unlikely that they invest in innovation activity.
• Top managers focus on the tasks that enhance the firm’s profit in the
shortest way they can impress boards and increase their payment
• R&D employees increase only the numbers of patents to ehance their
payment without considering the quality of innovation.
8
Method
• Meta-regression analysis can review all existing empirical studies
comprehensively and by aggregating the results of various studies it
can provide more authentic estimates than the individual study.
• We considered previous empirical studies that examine the relation
between variable pay and innovation.
• We searched Google scholar, Elsevier, Business Source Premier and
Jstor databases for the combination of the specific keywords.
• Variable payment of managers, employees and R&D heads are
included.
• Compensation: long-term and short-term compensation; Innovation:
patent, R&D intensity and innovation performance
• The final sample consists of 43 studies that report 301 estimation of
compensation-innovation relationship.
9
Method
• Meta-regression model:
𝑟𝑖 = 𝛽0 + 𝛽1 𝑆𝐸𝑖 + 𝑣𝑖
Dependent variable: Effect size measures the strength and direction of
compensation-innovation link; Partial correlation
r
t
t 2  df
Independent variable: effect size standard error
(1  r 2 ) 2
Vr 
df  1
𝑆𝐸𝑖 =
𝑉𝑟
10
Method
Reporting Bias analysis:
• Reporting bias: the propensity in reporting statistically significant results.
• Basically authors and journals prefer to publish significant results and
results which are more in accord with theories (Card and Krueger, 1995)
• Reporting bias can be considered as a threat to an empirical inference and
validity of policy implication that are drawn from empirical results.
• Meta-regression analysis (MRA) helps us to detect the publication bias
and identify the precision effect regardless of bias (Stanley, 2008).
• Reporting bias can be distinguished by two tests: Funnel plot test and FAT
test (Egger et al., 1997; Stanley and Doucouliagos, 2010)
11
Method
0
10
20
1/std_r
30
40
50
• Funnel Plot Test:
-.5
0
r
.5
1
12
Method
FAT test (funnel asymmetry testing)
𝑟𝑖 = 𝛽0 + 𝛽1 𝑆𝐸𝑖 + 𝑣𝑖
𝑡𝑖 = 𝛽1 + 𝛽0 (1 𝑆𝐸𝑖 ) + 𝑒𝑖
H0: 𝛽1 = 0  reporting bias
H0: 𝛽0 = 0 genuine effect between compensation and innovation
that adjusted for reporting bias
• Weighted Least Square (WLS) has been applied
• Fixed effect and Random effect models
• Fixed effect model: the weight assigned to each study is inverse of
effect size variance (within-studies variance)
• Random effect model: weigh each study by inverse of effect size
variance (within-studies variance and between-studies variance)
13
Results
FAT test:
𝑡𝑖 = 𝛽1 + 𝛽0 (1 𝑆𝐸𝑖 ) + 𝑒𝑖
FAT Test
Dependent variable: t-statistics
(1)
(2)
Sub-effect, fixed
effect
Sub-effect,
random-effect
Precision
0.072***
(3.41)
Constant (Reporting bias)
-0.46
(-0.64)
Number of observation
301
R-squared
0.037
0.07***
(4.97)
-0.410
(-1.14)
301
0.073
14
Results
FAT test:
Table . FAT-PET-MAR Test
Dependent variable: t-statistics
(1)
(2)
US studies, fixed
effect
Non US studies, fixed
effect
0.08**
0.054*
(3.01)
(2.02)
-0.7
0. 41
(-0.80)
(0.48)
Number of observation
233
68
R-squared
0.07
0.12
Percision
Constant (Reporting bias)
15
Method
Moderator Variables:
– Theoretical aspect
– Publication outlet : Journal, type of journal, impact factor of
Journal, publication year
– Sample characteristics: US firms or non US firms, type of
industry, compensation of different groups
– Methodological aspect: Methods of analysis, industry fixed
effect, time period fixed effect and time-lag effects
– Data: Database, Types of data
– Control variables: innovation specifications, compensation
specifications, time period of sample, Firm characteristics, CEO
characteristics
16
𝑡𝑖 = 𝛽1 + 𝛽0 (1 𝑆𝐸𝑖 ) + 𝑒𝑖
Specific Model
Input-based measure of
innovation
Output-based
measureof innovation
precision
0.122***(5.14)
0.391***(4.25)
-----
Crowding-out Theory
2.64*** (4.49)
2.612(0.57)
4.62 (1.60)
Finance
-2.6*** (-3.51)
-4.505 (-1.72)
1.66 (0.99)
Working paper
-2.46*** (-3.56)
-2.3 (-0.54)
-----
-2.24*(-2.48)
6.151 (0.45)
1.075 (0.27)
Cross section
1.639** (2.69)
-----
-----
Innovation performance
-3.246* (-3.14)
-----
-----
-----
-2.683 (-1.35)
-2.844***(-5.13)
Stock
1.945** (2.64)
-0.87 (-0.58)
4.525*** (8.12)
Bonus
-----
-3.338* (-2.15)
-----
-0.233 (-0.33)
246.3 (0.39)
-509.8 (-1.34)
273
113
Dependent Variable: t-
statistics
Employees
Stock option
Constant
N.observation
128
17
Conclusions
• No evidence of publication bias can be found among the selected
studies.
• There is positive relationship between incentive pay and
innovation; However this association is weak.
• The variation in estimated association between compensation and
innovation across studies is due to differences in some study
characteristics.
• The variable compensation are more effective in managers rather
than employees.
• The way innovation and compensation are measured affect the
compensation-innovation link.
• There are differences in the effect of short and long-term
compensation on input and output-based measure of innovation.
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Thanks for your attention!
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