Firm performance, executive pay and the return on
effort
Abstract
The agency framework predicts a strong positive relationship between executive effort and pay,
and firm performance. This paper studies how changes in the Norwegian earnings tax, which
influence executives’ return on effort, affect firm performance and executive earnings for 8700
firms. Reduced marginal earnings tax, implying increased return on effort, increases firm
performance. Supportive of the predictions from the agency model, such tax changes increase the
fixed wage and reduce the piece-rate on performance. For non-Norwegian executives, which may
potentially be facing an alternative labour market, the piece-rate on performance is not affected
by changed tax legislation.
Key-words: Firm performance, executive pay determination, panel data, tax changes
JEL-codes: G34, J33, L25
Word count: 10375
INTRODUCTION
One of the most important frameworks for understanding executive pay is the agency
framework. Firstly, this framework has provided a strong link between firm performance,
executives’ effort, and their remuneration contracts. Secondly, this framework has been hugely
influential when boards develop and establish executives’ contracts. The essence of the
framework has been to establish that the presence of unverifiable executive effort in a world
where firm performance is affected by uncertainty, executives’ need risk compensation but also
incentives to perform. However, after the financial crisis of 2008, executive performance pay
schemes comprising hefty bonuses and stock options have been strongly criticised.1 Paradoxically
this criticism, albeit well-founded in specific circumstances, may be less relevant as generalised
critique of the agency framework.
In this paper we exploit empirically the relation between the return on executives’ effort
and firm performance. If executive remuneration is set according to a simple agency model, then
unanticipated changes in the return on executives’ effort should affect the performance of firms.
Furthermore, such changes should also cause changes in the executives’ remuneration.
Theoretically it is easy to derive the necessary empirical predictions. However, researchers usually
face problems when they empirically want to reveal causal effects of changes in the return on
executives’ effort. First, neither effort nor the ex ante return on effort are usually observable.
Second, the observed variation in the return on effort often follows from firms’ optimising
behaviour and thus is far from exogenous. We argue that changes in the tax legislation provide us
with the necessary exogenous variation in the ex ante return on effort.
Recently we have seen a boost in the number of studies trying to explain or understand
the increase in CEO pay (Conyon and Murphy, 2000; Murphy, 2002; Bebchuk and Fried, 2003,
2004; Baker and Hall, 2004; Frydman and Saks, 2007; Gabaix and Landier, 2008). For example,
motivated by a simple competitive model, Gabaix and Landier (2008) show that when the U.S.
CEO pay is 6 times bigger in 2003 than in 1980 this can be explained by similar firm market value
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growth. Admittedly the U.S. growth in CEO pay is an extreme case. Even in the UK we observe
smaller growth (Conyon and Murphy, 2000).
Most theoretical agency models yield predictions that imply a positive relationship
between performance and pay, and such a positive relationship between performance and
management compensation has been observed over decades (Deckop, 1988; Jensen and Murphy,
1990; Kaplan; 1994; Murphy, 1999; Aggarwal and Samwick, 1999; Mengistae and Xu, 2004).
Furthermore, several authors have studied how insurance incentive trade-offs in remuneration
schemes, i.e., as performance becomes more uncertain, then the pay-performance sensitivity
drops (Garen, 1994; Aggarwal and Samwick, 1999; Mengistae and Xu, 2004).
Empirical tests of how executive remuneration affects firm performances, i.e., studies of
how changes in managerial pay-performance sensitivities affect subsequent performance, are
much less prevalent (see Murphy, 1999). Leonard (1990) and Abowd (1990) find some evidence
supporting the notion that increased pay-performance sensitivity positively affects performance.
Leonard (1990) finds for example that firms incorporating a bonus as part of the executive’s pay
(performance-pay) performed better than non-bonus firms. However, firms introducing a bonussystem did not achieve better performance than others, and this makes it more debatable whether
one really observed a causal impact of bonus pay on performance. Similarly, Lam and Chng
(2006) find some evidence supporting the notion that managerial stock options improve firm
performance.
Even for ordinary workers the evidence on the relationship between performance pay and
performance is mixed. Performance pay is mostly associated with (strongly) improved
performance (for example, Lazear, 2000). Performance pay induces more effort (Eriksson and
Villeval, 2008), but efficiency wages, over time the repeated interaction between workers and
firms, reduce the attraction of performance pay. Furthermore, in Israel Gneezy and Rustichini
(2000) found that if one introduced incentive pay it was important to ensure that the incentives
were strong enough, otherwise they worked against their purpose. Finally, certain studies raise
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doubt on the good performance of performance-pay for all kinds of work tasks, for example
Bloom (1999), Stajkovic and Luthans (2003) and Kuvaas (2006). Kuvaas argue that such pay
systems perform better when job tasks are less complex.2
While we do not observe the introduction of a specific bonus scheme and use this to
conduct an empirical test, we argue that by observing exogenous variation in the return on
executives’ effort, we are able to study how effort affects performance. Our idea is that changes
in the tax system over time in a country provide variation in the return to effort of an executive.
An already influential literature exists that links tax reforms to labour supply responses (Feldstein,
1995a, 1995b; Aaberge et al., 1995; Blundell et al., 1998). To further motivate the relation
between taxes and firm performance, we present a simple agency model predicting reduced effort
and performance from increased marginal earnings taxes. Since such tax changes are exogenous
to the executives, they provide the necessary exogenous variation for us to measure the impact
on firm performance. This idea is simple, and it should be easily adaptable to numerous
countries. Several countries tax high earnings differently than low earnings (for example, the
Nordic countries), and even in the UK politicians contemplate introducing a top tax for high
earners (which also makes our analyses timely and pertinent).
Using data from a smallish egalitarian open economy such as Norway is highly beneficial
for several reasons. First, Norway is ranked by OECD (2008) among the countries with smallest
wage dispersion, so if we identify performance effects from changed executive return to effort,
this is a strong result given the compressed wage structure of Norway. Second, the marginal
earnings tax in the Norwegian system increases non-monotonically as earnings grow, and for
executives located in the earnings distribution close to the threshold levels, tax changes provide
sizeable variation in the return to effort (see Section I for details). It is this discontinuity we will
explore and exploit in our empirical analyses. Third, the timing is beneficial for our purpose,
since the tax legislation for the next year is made public at the end of the current year, but
contract renegotiations between the executive and the firm’s board occur after the spring next
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year (after the annual general meeting). Fourth, our data comprise a mixture of small and large
firms included in the capital data base of Statistics Norway (Raknerud et al., 2004), and the pay of
their executives vary enough for identification purposes.3
The structure of the remainder of the paper is as follows. In Section I we briefly describe
changes in the Norwegian tax legislation. Section II provides an axiomatic illustration, while we in
Section III discuss and derive the econometric specifications. Section IV provides a description
of the data. How changed executive return to effort affects firms’ return to sale is analysed in
Section V. In Section VI we analyse how changed return to effort affects executives’ fixed pay.
How the piece-rate on performance is affected by changed return to effort is analysed empirically
in Section VII. Section VIII provides a brief conclusion and discussion.
I. CHANGES IN THE NORWEGIAN EARNINGS TAX LEGISLATION
Our data cover the period of 1995 to 2005. Changes in the tax system occurred each of these
years, either by changed threshold levels or by changed marginal tax levels (or by both). In Table
1 we see the development of the marginal earning tax during the period 1995 to 2005 for our
executives living in the main area of Norway (the development in the northernmost
municipalities is slightly different).
[INSERT TABLE 1 AROUND HERE]
All our executives face a marginal tax rate of at least 29.1 percent. If they earned between 126943
and 212000 Nok in 1995 they faced a marginal tax of 35.8. This is the most common tax level in
Norway. Then if they received earnings between 212000 and 239000 the marginal tax increased
to 45.3 percent (a top tax of 9.5 percent is added), while earnings above 239000 Nok implies a
marginal tax rate of 49.5 percent in 1995.
From Table 1 we can infer three observations. First, we see in Table 1 how the threshold
levels move steadily upward. Secondly, we see the drastic changes from 1998 to 2000, when the
authorities changed threshold levels, the tax rate as well as tax groups, and that minor changes
4
occur 2004-2005. Thirdly, while the marginal tax rate increases within tax groups during our
period of observation, the differences between groups within the same year are greater.
Table 1 also reveals that the moving of the threshold levels may contribute to serious tax
changes for the affected executives. Particularly during the period 1995 to 1999 rather small
changes in the earning level yielded considerable changes. Consider the for example changes
from 1998 to 1999. If you earned 250000 Nok in 1998 you faced a marginal tax rate of 45.3
percent. In 1999 the same earning level implied a marginal tax rate of 35.8, i.e., a drop close to 10
percent. On the other hand, if you earned 245000 Nok in 1998, implying a marginal tax rate of
35.8 percent, and anticipated a 10 percent earnings increase from 1998 to 1999, then your
anticipated earnings of 269500 Nok implied a marginal tax rate of 49.3 percent. But even in 2004
can an anticipated 15 percent drop in earnings (following industry business cycle fluctuations)
imply a marginal tax drop of 7.5 percent. We argue that tax changes such as these, then yield
sizeable variation in the return to effort, and that these exogenous tax changes thus can be used
to identify how sensitive firms’ performance are to changes in the return to effort. Furthermore,
we should be able to test if these tax reforms affect the remuneration schemes of executives.
II. THE MODEL
The motivation for our empirical study is found in the simple agency textbook-model of Cahuc
and Zylberberg (2004:323-326). It assumes that the executive’s utility function is of the constant
absolute risk aversion-type (CARA), i.e., more explicitly as given by Equation 1):
1) U[W-C(e)]=-exp{-a[W-C(e)]},
where U denotes the utility, W denotes total renumeration, e equals effort, while C(e) expresses
the cost linked to supplying effort. C(e) is assumed given by the quadric function: C(e)=0.5ce2,
where c>0.
By supplying effort e the executive ensures a production of Y= e + ε, where ε expresses a
normal random variable with zero mean and standard error σ. Thus one cannot derive effort by
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observing the achieved production. When the owners of the firm determine the executive’s
earnings scheme we assume they only consider linear contracts of the form W = w + bY. The
earnings scheme offered to the executive comprises two elements, w (a fixed wage) and b (the
piece-rate on performance).
The executive chooses e to maximise expected utility:
2) EU=-exp{-a[w+be-C(e)-0.5ab2σ2]},
which implies that C’(e*)=b, i.e., e*=b/c. Note that the fixed wage does not affect effort.
The owners of the firm take the executive behaviour for granted and chooses b and w so
that they maximise expected profits, i.e., EΠ=E[y-W]=(1-b)e* - w. When determining b and w,
they are facing two constraints – the incentive-compatible constraint (C’(e*)=b) and the
participation constraint (EU≥OO, OO denote outside options). The participation constraint
quite simply expresses the condition necessary for the executive to accept the contract.
By expressing OO in money terms, x=-ln(-OO)), and then transforming the participation
constraint, we can express this as:
3) w+be-C(e)-0.5ab2σ2 ≥ x.
Since e* is determined independent of the fixed part of the earnings, the optimal value of b is
found simply choosing b so that e*-C(e*)-0.5ab2σ2 –x is maximised subject to C’(e*)=b. Then one
derives the optimal w from the participation constraint. The optimal b becomes:
4) b*=1/[1+acσ2],
while the optimal w becomes:
5) w*=x-{[1- acσ2]/2c}{1/[1+acσ2] 2}.
Equations 4) and 5) then show the classical results that if risk aversion, effort cost or risk
increases, then the owners of the firm should put less weight on the piece-rate on performance
and more weight on the fixed wage element.
In this model we introduce earnings taxes. This is done in a very simple way, by just
introducing a proportional tax, t, on executive’s earnings. The tax revenue generated is
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redistributed to non-executives. As a reference case we also assume that the executive’s outside
option is affected by introduction of the tax. If the executive’s outside option is determined
outside of Norway, it is less likely it will be affected by the introduction of taxes. But for most
executives in Norway, the domestic labour market is the primary arena for work.
When taxes are incorporated into the executives’ utility function:
6) U[(1-t)W-C(e)]=-exp{-a[(1-t)W-C(e)]}.
As before, the executive chooses e to maximise expected utility:
7) EU=-exp{-a[(1-t)w+(1-t)be-C(e)-0.5a(1-t) 2b2σ2]},
which implies that C’(e**)=(1-t)b, i.e., e**=(1-t)b/c. Introducing taxes in such a way clearly reduces
the optimal effort. Owners still chooses b and w to maximise expected profits, which now is
expressed as EΠ =E[Y-W]= (1-b)e** - w. Above we see that the incentive-compatible constraint
changes, and so do the participation constraint:
8) (1-t)w+(1-t)be-C(e)-0.5a(1-t) 2b2σ2 ≥ (1-t)x.
The optimum solutions for b and w are derived as previously, but the introduction of a
proportional tax changes these to:
9) b**=(1-t)/[1-t+acσ2],
and:
10) w**=x-{[1-t- acσ2]/2c}{(1-t)/[1-t+acσ2] 2}.
Using 9) and 10) as well as the expressions for the expected profits, effort and total earnings, and
then differentiating w.r.t. t leave us with three simple predictions: ∂b**/∂t<0, ∂w**/∂t>0 and
∂Π/∂t<0. Increased taxes should imply that profits as well as the piece-rate on performance are
reduced, while the fixed wage increases. Furthermore, performance drops due to lowered effort.
If we assume that the executive’s outside option is not affected by introduction of the tax,
then ∂b**/∂t=0, while ∂w**/∂t>0 and ∂Π/∂t<0. The reason is that while the provision of effort is
not affected by the increased tax rate, the willingness to take the contract is, and the owners of
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the firm have to pay more to get an executive to accept the job. This implies increased fixed
wages and thus lowered profits (for the owners).
III. THE ECONOMETRIC MODELS
We estimate two kinds of econometric models – a performance equation and a log earnings
equation. The performance equation is described quite straightforwardly by Equation 11):
11) (Π/I)ft= βt + β1Anticipated_reduced_tax ft + β2∆wimt-1 + βx’X t +εft,
where Π/I denotes return on sales in percent (the operating profit per revenue), ∆wimt-1 denotes
previous period industry- and county-specific average relative wage growth, the X’s express other
controls, βt expresses year dummies, and εft expresses a standard error term. In one specification
we even add γf on the right-hand side in 11), thus capturing permanent performance differences
between firms (fixed firm effects). The estimation of 11) and thus of β1 directly test the
hypothesis that ∂Π/∂t<0.
The tax variable expresses a dummy taking the value of 1 if the anticipated tax rate (i.e.,
the tax rate given the new legislation contingent on the executive’s earnings from the previous
period adjusted for previous period industry- and county-specific average relative wage growth) is
less than previous period’s observed tax rate (i.e., the tax rate given the “old” legislation contingent
on the executive’s earnings from the previous period), otherwise the dummy takes the value 0
(see Section IV for details). The dummy is related to firm f’s executive and thus is firm-specific
and may of course vary over time. For most executives it takes the value 0, since tax legislation
changes only affect a minority of the executives.
The inclusion of industry- and county-specific previous period average relative wage
growth is thought to capture the impact of local industry-specific shocks to performance. In one
specification we ignore the fixed effect, while in other specifications we let the X-vector vary to
study the robustness of our results. The X-vector comprises firm-specific variables as log
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workforce size and log capital, and other variables as, log county employment, industry plant
entry rate, pay-roll tax rate and executive-specific characteristics.
The earnings equation is similarly described by Equation 12):
12) Wft= αt + αp (Π/I)ft-1 + α1Anticipated_reduced_tax ft-1 + α2∆wimt-1 + αx’Z t +θf+νft,
where log W expresses daily earnings (in 1000s), Π/I denotes the return on sales in percent,
∆wimt-1 denotes previous period industry- and county-specific average relative wage growth, the
Z’s express other controls, αt expresses year dummies, and νft expresses a standard error term.
The inclusion of industry- and county-specific average relative wage growth is once again thought
to capture the impact of local industry-specific shocks to payment. As above we let the X-vector
vary to study the robustness of our results. In one specification we add θf to the right-hand side
of 12) to capture permanent payment differences between firms.
In 12) we assume that the firm performance of previous period affects executives’
remuneration current period, i.e., bonuses and performance-related rewards are paid the
consequent year.
In Equation 12) (Π/I)ft-1 is clearly endogenous and thus is instrumented by variables
thought to affect Π/I)ft-1 but not earnings directly. The impact of these variables on performance
has previously been documented through the regressions of Equation 11). The estimation of 12)
provide evidence on how the fixed wage element responds to tax changes (∂w/∂t>0 implies
α1<0).
To study how the piece-rate on performance depends on taxes we estimate Equations 13)
and 14) separately for i) those that do not anticipate tax changes, and ii) those that anticipate
reduced marginal earnings tax, respectively:
13) Wft= αt + αpNo (Π/I)ft-1 + α2∆wimt-1 + αx’X t +νft,
14) Wft= αt + αpReduced (Π/I)ft-1 + α2∆wimt-1 + αx’X t +νft,
where the notation is as previously defined. From our discussion in Section II we expect in the
reference case (i.e., whenever outside options are affected by the tax reforms) the piece-rate on
9
performance for those unaffected by the tax reforms is higher than the piece-rate on
performance for those that anticipate reduced tax, in other words αpReduced>αpNo>0.
How can we test whether the outside options are affected by the tax reforms? A very
simple approach, which we follow, would be to re-estimate 13) and 14) separately for foreign and
domestic executives. Foreign citizenship executives may be facing outside options less affected by
Norwegian tax rules, i.e., changes in the tax legislation should not affect their piece-rate on performance. If this is the case, then for domestic executives we should observe αpReduced>αpNo>0,
while we similarly for foreign executives should observe that αpNo=αpReduced >0.
IV. DATA AND MEASURES
Our analyses are based on the linking of two data sets. The first data set is Statistics Norway’s
Capital-database (for documentation, see Raknerud, Rønningen and Skjerpen [2004]). This
database comprises information on key economic figures (e.g., capital, value added, measured in
running prices) for all manufacturing joint-stock firms. The unique feature of this data set is that
one is not limited to book-values, but key variables are measured in running prices (taking account
of depreciation). The unique firm-identifying number of the firms makes it possible to link
information on executives, e.g., wages, educational qualification and nationality from the data
system described below. This data set contains more than 50 000 observations during the period
1995 – 2005 on more than 10 000 firms.
The second data set, or more precisely, data system, is based on public administrative
register data. It comprises all employers and their employees in Norway 1995–2005 (roughly
150000 employers and 1800000 employees each year) employed May 15th each year. This data set
is a similar to an integrated register based data system, Current System for Social Data (CSSD),
linked by Statistics Norway, comprising information from public administrative registers (except
CSSD is not restricted to employment spells active on May 15th). This linked employer-employee
data set provide information on workers (gender, educational qualifications, occupation (2003-
10
2005)), jobs (for example spell length in days and thus seniority, spell-specific earnings and thus
combined with spell length the daily wage, weekly working hours (intervals, exact hours 20022005), spell length and exact weekly working hours), firm-and establishment identifying numbers
and on establishment-characteristics such as industry (5-digit NACE), sector and municipality.
The annual job-specific earnings measure is particularly important for our purposes. This
reports the total earnings during a job spell during a year. It incorporates fringe benefits reported
to the tax authorities as well as returns to stock options (when exercised).
For research purposes, it is a nice feature of the Norwegian public administrative registers
that each individual, each establishment and each firm are identified by unique identifying codes
(separate number series). In our data, these original numbers are replaced by encrypted numbers.
From 2003 and on ward this data system comprises information on occupation, thereby
making it possible to directly identify the chief executive officer of a firm. However, previous to
2003, no direct identification is possible. We have chosen the following simple strategy for
identifying the chief executive:
First, we start in 2003 - 2005 when we directly identify the executive(s). If repeated
observations of executives are found, we pick the one in the firm with highest earnings. We then
define this individual as the executive in the firm backwards in time to 2003 contingent that he or
she is defined as an executive. From 2003 we then define this individual as the executive in the
firm backwards in time as long as we can follow him or her in that firm. Next, when a firm is left
with no executive, we identify the new executive as the individual with the highest earning in the
firm. Then we follow this individual backwards in time. This process is repeated until we have
found executives in all firms for all years. We admit that this method is likely to classify some
workers as executives before they really become ones. However, to classify the highest earner as
the executive is equally wrong, since this will maximise executive turnover (quitting) and it will
increase the probability that we miss executives which perform badly. Badly performing executives
under a strong incentive scheme may receive lower earnings than an ordinary high paid fixed wage
11
worker. We particularly want to avoid maximised executive turnover, since we want to focus on
the impact of taxes on effort, performance and pay, and thus need three consecutive years of
observations. Therefore firms changing the executive frequently will drop out of our regressions.
The anticipated marginal tax rate for year t is based on the tax rules for year t contingent
on previous period (t-1) total earnings adjusted for the anticipated wage growth. A dummy for
anticipated reduced tax takes the value of 1 if the anticipated marginal tax rate is less than the
observed marginal tax rate (based on the tax rules for year t-1 contingent on previous period (t-1)
total earnings), otherwise the dummy takes the value of 0. An executive’s expected wage growth
from t-1 to t is calculated as the average wage growth of other executives within the same industry
(2-digit) and same county from t-2 to t-1.4
Next we have to deal with the difficulties of measuring firm performance. We started out
with the intention of focussing on two performance measures – return on sales (operating profits
per revenue) and return on capital (operating profits per capital). Due to extreme variation in the
reported levels of capital (operating profits per capital varied between -800 to +800), we found it
extremely difficult to make sensible and robust analyses. Thus our chosen performance measure is
return on sales. However, in the regressions we do control for capital and size differences.
In our analyses we introduce several control variables measuring local labour market
characteristics. Variables such as log local employment and log vacancies per unemployed are
defined at the municipality level (roughly 350 municipalities) as yearly averages over monthly
observations. We will also study the impact of the pay-roll tax rate on performance. During our
period of observation the pay-roll tax rate in Norway varies basically between 5 zones, and some
changes, albeit small, occur with the tax rate of a zone. However, municipalities change zones,
thus creating exogenous variation in the pay-roll tax rate at the municipality level.
To eliminate potential outliers we run two regressions, a performance regression and a log
earnings regression, controlling for our basic controls variables as for example log capital, log
workforce size, and then discard all observations outside +/- 5 times the residual mean square
12
error. No restriction is imposed on the sign of performance measure. More than 15 percent of
our firms experience negative operating results, and thus discarding these observations would
create serious selection effects.
Our final data set comprises 57000 observations of over 8700 firms. These firms are
mainly part of the manufacturing, construction and trade industries, and are geographically located
all around in Norway.
V. THE IMPACT OF CHANGING EXECUTIVES’ RETURN TO EFFORT ON
FIRMS’ PERFORMANCES
We start in this section by briefly presenting descriptive statistics on key variables. In Table 2 we
have split our sample of firms into three categories – all firms, those firms where the executive
can anticipate a reduction in the marginal tax, and those firms where the executive does not
anticipate any changes in the marginal tax (from here on called No-change firms). We see that the
average roughly 15 percent of the firms have executives which can anticipate marginal earnings
tax reductions.
[INSERT TABLE 2 AROUND HERE]
When we compare those firms where the executive can anticipate a reduction in the marginal tax
with the No-change firms, we see that these are equal along most dimensions, for example
workforce size, capital, productivity (value added per hour), worker pay, union density, and being
multi-plant firm. However, the return to sales, yearly executive earnings and the nationality of the
executive (foreign vs. Norwegian) differ between the two groups. Firms where the executive can
anticipate a reduction in the marginal tax have higher return to sales (on average 20 percent
higher), executive remuneration is higher (12 percent higher) and their executives are more often
non-Norwegians (30 percent higher) although a large majority of these executives is still
Norwegians. Due to the large variance across firms, however, these differences are not
significant.
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As pointed out above, the return to sales varies quite a lot between the firms. Not all
firms, where the executive can anticipate reduced tax, will have higher return to sales than what is
found among the No-change firms. This is also seen in Figure 1, which depicts the density
function estimate of the return to sales for these two groups of firms. On average, the return on
sales density from firms where the executive can anticipate reduced tax are distributed to the right
of the return on sales density of the No-change firms.
[INSERT FIGURE 1 AROUND HERE]
In Table 3 we look closer on the yearly distribution of the return on sales for those firms
where the executive can anticipate a reduction in the marginal tax and the No-change firms.
Table 3 reveals that, with one exception, the return on sale is always higher among firms where
the executive can anticipate a reduction in the marginal tax compared to the No-change firms.
The exception is found for 2004, when the return to sales is equal for the two groups of firms.
[INSERT TABLE 3 AROUND HERE]
Thus on average the raw figures show supportive evidence of the notion that a reduced
marginal earning tax facing executives actually acts beneficial on firm performance. But, since the
firm variation in the return to sales is large, these differences found above are by large
insignificant. But these figures do not take into account potentially important firm differences
such as firm structure and size, local market conditions, and executive characteristics.
To address this, we run several return on sale regressions, where we regress return on sale
on a dummy for anticipated reduction in the marginal tax rate, pay-roll tax, local labour market
conditions, workforce size and capital, value added per hours, year dummies, and executive
characteristics such as gender, years of education, years of potential experience and years of
seniority. Table 4 presents the results of these regressions. Models 1-5 are estimated using OLS,
while Model 6 is estimated using OLS on first-differenced observations (to take care of fixed firm
effects). In all models the robust standard errors are adjusted for firm clustering.
[INSERT TABLE 4 AROUND HERE]
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We start out in Model 1 by estimating the return on sale regression on the dummy for anticipated
reduction in the marginal tax rate and year dummies. The return on sales is significantly 1.29
percentage points higher among firms where the executive can anticipate a tax reduction than
among the No-change firms. Since the average return to sale is around 4-5 percent, this impact is
quite large (around 25 percent).
In the next 3 models, Models 2-4, we add control for the executives’ previous period
wage growth within industry, controls expressing local labour market conditions and industry
characteristics, and controls expressing firm characteristics such as workforce size, capital, value
added per hours (productivity), average worker pay and union density. Nothing much happens
concerning the tax impact. The return on sales is still in Model 4 significantly 1.25 percentage
points higher among firms where the executive can anticipate a tax reduction than among the
No-change firms.
In Model 5 we even add controls for executive characteristics such as woman, foreign
citizenship, years of education (and squared), years of potential experience (and squared), years of
seniority (and squared). Introducing executive characteristics matters for the estimates, and the
importance of the changed return to effort drops. However, the return on sales is still
significantly 0.8 percentage points higher among firms where the executive can anticipate a tax
reduction than among the No-change firms (and thus 20 percent higher).
Finally, in Model 6 we re-estimate Model 5 on first-differenced observations and thus
take care of fixed firm effects. The importance of the changed return to effort drops sharply, but
still affects the return to sales significant and positive. When an executive can anticipate a tax
reduction then their firms achieve on average 0.3 percentage points higher return to sales than
the No-change firms.
The conclusion to this section is clear. When an executive can anticipate a tax reduction
then their firms achieve higher return to sales than the No-change firms. This result survives a
wide range of controls and specifications, and even is robust to the inclusion of fixed firm effects.
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Thus it is a strong result. The impact is in most cases sizeable, and thus is of importance. Even in
the fixed effect specification, which indicated the least impact, we are left with 8 percent higher
return to sales when an executive can anticipate a tax reduction. Other specifications indicate a
gain of 20-25 percent in the return to sales.
VI. THE IMPACT OF CHANGING EXECUTIVES’ RETURN TO EFFORT ON
EXECUTIVES’ FIXED PAY
In the previous section we documented that the anticipated marginal earnings tax affects firm
performance negatively, i.e., if an executive can anticipate a tax reduction then their firms achieve
higher return to sales. In this section we study if changes in the return to effort - anticipated tax
changes – also affect the remuneration schemes of executives as predicted in Sections II and III.
In Table 5 we study how previous period anticipated reduction in the marginal earnings tax and
previous period return on sales are related to current period earnings by running earnings
regressions. Our dependent variable, earnings, is measured in 1000 Nok. In these regressions the
dummy for previous period anticipated reduction in the marginal earnings tax captures the
impact on the fixed pay.5
[INSERT TABLE 5 AROUND HERE]
The return to sale is to be considered endogenous in the regressions, and thus has to be
instrumented. We argue that previous period’s log value added per hours and industry plant entry
rate can act as instruments for the return to sales in the earnings regressions. These variables
affect return to sale (for example, improved productivity yields higher return to sales) and the
over-identification and strength test results indicate strong and valid instruments (see Table 5)(we
admit the entry rate could perform better, but the inclusion of this makes it possible to conduct
over-identification tests). One should particularly note that statistically neither log value added
per hours nor industry plant entry rate should be included in the earnings regressions (as
indicated by the over-identification test).
16
In Model 1 we estimate the earnings regression using OLS. The return on sales has no
impact on earnings. If an executive previous period anticipated a reduction in the earnings tax, he
receives significantly lower earnings, by close to 25000 Nok. In Model 2 we instrument return to
sales, and we then identify a strongly significant and positive piece-rate on performance. If a gain
in the return to sales by 1 percentage point is achieved then executive earnings increases by over
14000 Nok. In this case, however, an anticipated reduction in the earnings tax then yields a drop
in earnings over 42000 Nok. This indicates a reduction in the fixed pay (not performance related)
of around 8 percent.
In the next 6 models, which act as robustness checks, we add firm characteristics (Model
3), executive characteristics (Model 4), controls for industry (Model 5) and county (Model 6), and
even incorporate fixed firm effects (Model 7 and 8) (by running regressions on first-differenced
observations). With the exception of Model 3, these models all indicates a significant drop in
earnings if an executive can anticipate a marginal tax reduction. In our preferred specification,
Model 4, the executive receives 9000 Nok less. Thus our conclusion is that if executives
anticipate increased return to effort – a reduction in the marginal tax –, then their fixed pay is cut.
This reduction in pay is significant, but cannot be defined as large, but then again, as indicated by
note 5, we know this reduction is underestimated.
VII. THE IMPACT OF CHANGING EXECUTIVES’ RETURN TO EFFORT ON
EXECUTIVES’ PIECE-RATE ON PERFORMANCE
In the previous section we studied how executive fixed pay depended on the return to effort. In
this section we ask how changed return to effort affects the piece-rate on performance. To
answer this question we split our observations into two groups – those where executives
anticipate a reduction in earnings taxes and the No-change firms – and run separate but identical
regressions on these two groups of firms. According to Sections II and III we would expect to
17
find a lower piece-rate on performance in the No-change firm group than in the groups of those
that anticipate a tax reduction.
We estimate two sets of models by 2SLS, using the same set of instruments for the return
to sales as was used in the 2SLS-regressions of the previous section. The robust standard errors
are adjusted for firm clustering. Model 1 is equivalent to Model 3 of Table 5, while Model 2 (our
preferred specification) is equivalent to Model 6 of Table 5. Table 6 presents the results from
these 2SLS-regressions. We see that the instruments perform equally well as in the regressions of
the previous section.
[INSERT TABLE 6 AROUND HERE]
More importantly Table 6 shows that in all the four regressions the estimated piece-rate
on performance, i.e., the parameter associated with the return on sales, is strongly significant and
positive. For firms where the executive anticipates a tax reduction the piece-rate varies between
6.8 and 7.7, i.e., if the return to sales increases by 1 percentage point then the executive receives
6.6-7.7 thousand Nok more in remuneration. For the No-change firms, however, the piece-rate
on performance varies between 3.4 and 3.9. These piece-rates are clearly significantly lower than
for the firms where the executives anticipate a tax reduction.
Thus changed return to effort, caused by the changes in the tax legislation, affects the
piece-rate on performance. If executives can anticipate increased return to effort, i.e., a tax cut,
then they will consequently experience that their piece-rate on performance is raised and
becomes twice as large as originally.
In Section 3 we saw that changed return to effort, caused by tax changes, should only
affect the piece-rate on performance whenever executives’ outside options where affected by the
tax changes. Thus we should not expect to find any impact of tax changes on the piece-rate on
performance for executives facing internationally determined outside options. Our approach to
test this is to study foreign citizenship executives and Norwegian executives separately. The idea
is that foreign executives to a larger extent than Norwegians face outside options determined
18
internationally. We thus estimate Model 1 of Table 6 separately for foreign and Norwegian
executives (the limited number of observations on foreign executives forces us to estimate a
more parsimonious model, and this exclude more complex models such as Model 2 of Table 6).
Table 7 presents the results.
[INSERT TABLE 7 AROUND HERE]
For Norwegian executives Table 7 presents the equivalent results to those that were presented in
Table 6. This should come as no surprise, since these executives comprise the major bulk of the
observations. For firms where the Norwegian executives anticipate a tax reduction the piece-rate
is 7.9. The piece-rate on performance for the No-change firms with Norwegian executives is 3.9.
For foreign executives, however, we find that executives employed by the No-change
firms are estimated to have a piece-rate on performance equal to 3.9, while executives anticipating
a tax cut experience a piece-rate on performance equal to 5.6. These figures reveal two findings.
First, the former estimate is identical to the estimate based on the comparable Norwegian
executives. Second, while the former point estimate is clearly lower than the latter, it is not
significantly different from the piece-rate on performance for foreign executives anticipating a tax
cut. This implies that executives’ outside options matter for the remuneration policies offered to
executives.
VIII. CONCLUSIONS
In this paper we have studied empirically the impact of changes in the Norwegian earnings tax
system, i.e., changes which affect the return on executives’ effort, on firm performance and on
executive pay for 8700 firms during the period 1995 to 2005. As predicted from our simple
theoretical model, if one changes the return on effort then firm performance changes as well.
Reducing the marginal earnings tax thus increases the return on executives’ effort, which again
manifests in increased effort and thus improved firm performance.
19
When we study how these tax changes manifest in changed executive payment policies,
we actually find that the piece-rate on performance increases when the marginal tax is expected
to rise and that the fixed pay drops. These findings are supportive of the prediction from our
simple agency model, and thus imply that such return on effort considerations are important
whenever executive remuneration is set.
On average, reduced marginal earnings tax implies 0.8 percentage points increased return
on sales, a reduction in the fixed pay of 9000 Nok and an increased piece-rate on performance of
3.5. This may seem like small changes, but one has to remember that the average anticipated tax
reduction is only 3.7 percentage points, so the impact is actually quite large. If an executive’s
return to effort was increased by 20 percentage points (one could imagine such a pay policy
change due to new owners following a takeover), i.e., an increase 5.3 times as strong, our
estimates would imply that the return to sale increased by 4.3 percentage points, the fixed pay
would by cut by 48600 Nok and the piece-rate on performance would increase by 19 percentage
points. The important result is that firm performance and remuneration actually changes
following an increase in the return to effort, since one can easily imagine other situations when
the return to effort changes more.
What can we learn from our study? Firstly, we argue our results are supportive of the
notion that executive pay should be performance-sensitive, since this clearly is beneficiary for
firm performance. This has to be the implication when we observe that firms’ performance varies
with executives’ return to effort. However, our study does not provide evidence on how
performance sensitive remuneration should be. Thus the current trend to curb executive pay may
very well be a move towards improved remuneration schemes, but one should avoid disbanding
executive performance pay all together.6
Secondly, it clearly reveals that executives (as do workers) react to financial incentives,
whether they are intended or not. Thus one should be aware that the structure of the tax system,
influences individuals return to effort, and thus directly influence individuals’ effort on the job.
20
This again implies that some changes to the tax system are more costly than others, and that the
progressive tax systems of the Scandinavian welfare countries have certain negative aspects
associated with it. This does not imply that these systems, by ensuring a high degree of
redistribution and financing public welfare, are not without merit, just that there exist some
negative consequences that are downplayed. Previous literature has focused on the impact of tax
reforms on earnings and labour supply. Our study shows that whenever politicians and public
authorities consider implementing tax reforms, the evaluation should extend beyond tax revenue,
earnings- and labour supply-considerations. In our case, only a minority of the executives is
affected by these tax changes, and one cannot draw tax system inferences based on our analyses.
Our empirical analysis is unfortunately not without caveats, but the correction of these
will be left for future research. We have studied just one performance measure, and one can easily
argue that other measures would be equally relevant. Our analyses rest on observations from a
limited number of firms, all having at least five employees (including the executive). Clearly a
larger and more representative sample would have improved the strength of our findings. We
have largely ignored issues as other remuneration schemes, for example stock options and relative
performance evaluation pay (Holmstrøm, 1982; Gibbons and Murphy, 1990; Joh, 1999),
insurance-incentive trade-offs (see introduction) and how incentives depending on size (Baker
and Hall, 2004). However, stock options and relative performance pay are not widespread in
Norway, and our earnings measure comprises the taxable value of stocks and stock options.7 Our
sample of firms comprises a mixture of small and large firms, and it is clear that the piece-rate on
performance may differ depending on firm size. We also know that as the firms get bigger, the
more likely it is that the executives’ remunerations comprise performance pay, relative
performance evaluation, stocks and stocks options (see note 7). Our approach in this paper has
been to control for size differences, since addressing the potential size-dependence of executive
contracts would greatly complicate our analyses.
21
In spite of these caveats we argue that our findings are interesting and potentially
important, since it is one of the few empirical studies that link the performance of firms to the
return to executive’s effort.
ACKNOWLEDGMENTS
Thanks to my colleagues at the Institute for Social Research in Oslo and conference participants
at the ESEM 2008 in Milan for fruitful discussions and helpful suggestions. This work was
financed by the Norwegian Research Council under grant number 173591/S20.
NOTES
1. Arguably an extreme case, fixed CEO pay schemes are enforced spring 2009 in all government-owned businesses
in Sweden.
2. A related literature on how wage dispersion between workers within a firm affects performance also provides
3.
4.
5.
6.
7.
rather mixed evidence. Heyman (2005) find evidences implying a positive impact of wage dispersion on
performance in Sweden, while Winter-Ebmer and Zweimüller (1999) identified an U-shaped relationship in
Austria. Finally, Bloom (1999) and Grundt and Westergaard-Nielsen (2008) find a negative correlation between
wage dispersion and performance and between wage dispersion growth and performance growth.
We restrict our analyses to firms where we are able to link information on executives and where these executives
are employed for at least three consecutive years. To make our analyses more robust and eliminate outliers, we
have discarded all observations of firms outside +/- 5 times the mean square error from test regressions (see
Section IV on data issues). This leaves us with a sample of over 8700 firms and 57000 observations.
Consider year 2000 as an example. Based on an executive’s total earnings, I, from 2000 and given the tax
legislation for 2000 the executive faced a marginal earnings tax t2000. From 1999 to 2000 executives employed
in his industry and county received 4% earnings growth. Thus this executive anticipates earnings in 2001 equal
to 1.04*I (i.e. a 4 % growth in earnings). Given this anticipated earnings and the new tax legislation of 2001 he
anticipates a new marginal tax t2001. The dummy takes the value 1 if t2001<t2000, otherwise it is zero.
Ideally we would have included a cross-term between return to sales and the tax reduction dummy in the
regressions, but since the return to sales will have to be considered an endogenous variable, this would also
make the cross-term endogenous, thus forcing us to instrument both. We have experimented with different
instruments, but our efforts have proved unfruitful in that respect that we have not been successful in
establishing strong and robust instruments for the cross-term. Thus we discard the cross-term from our
regressions. Since the cross-term is positively related to the return to sales, the exclusion of the cross-term will
make the parameter associated the tax reduction dummy biased toward zero. So if we identify a negative
parameter associated with the tax reduction dummy (as indicated by our model), our estimate will be
underestimated (it should be even more negative).
Our analyses may potentially have implications for the payment schemes targeting non-executives, since the same
mechanism affecting executives may equally well affect non-executives.
Using available representative questionnaire data (the Norwegian Workplace and Employment Relations Survey
(NWERS 2003) Dale-Olsen (2008) finds that roughly 3.5 percent of Norwegian workplaces have managerial pay
set using a relative performance evaluation scheme and 3.1 percent of executives receive stock options. This is,
however, sensitive to size. Over 10 percent of workplaces with more than 500 employees provide manager
contracts incorporating relative performance evaluation. Close to 24 percent of workplaces with more than 500
employees provide manager contracts incorporating stock options.
22
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24
FIGURE 1
CHANGES IN THE RETURN TO EFFORT AND THE DENSITY OF THE RETURN TO
SALES
0.08
0.07
0.06
0.05
0.04
0.03
0.02
0.01
0
dmnorm_d
dmlow_d
Note: dmnorm_d denotes the density of the return to sales for firms where executives do not anticipate any changes
to their marginal earnings tax. dmlow_d denotes the density of the return to sales for firms where executives
anticipate reduced marginal earnings tax. The kernel density estimate is based on the Epanechnikov kernel, where the
width of the density window around each point is so-called ”optimal”, i.e., the width that would minimize the mean
integrated squared error if the data were Gaussian and a Gaussian kernel were used.
TABLE 1
TAX REFORMS DURING 1995-2005 FOR MOST WORKERS. MAIN AREA.
Year Threshold Threshold Threshold Low
Main Top Top tax Tax 0 Tax 1 Tax 2 Tax 3
tax 1
2
Main
1
2
1995 126943
212000
239000
29.1
35.8
9.5
13.7
29.1
35.8
45.3
49.5
1996 132033
220500
248500
29.1
35.8
9.5
13.7
29.1
35.8
45.3
49.5
1997 139518
233000
262500
29.1
35.8
9.5
13.7
29.1
35.8
45.3
49.5
1998 148500
248000
272500
29.1
35.8
9.5
13.7
29.1
35.8
45.3
49.5
1999 161200
269100
29.1
35.8
13.5
0
29.1
35.8
49.3
277800
762700
29.1
35.8
13.5
19.5
29.1
35.8
49.3
55.3
2000 166500
2001 166500
289000
793200
29.1
35.8
13.5
19.5
29.1
35.8
49.3
55.3
2002 190400
320000
830000
29.1
35.8
13.5
19.5
29.1
35.8
49.3
55.3
2003 190400
347000
872000
29.1
35.8
13.5
19.5
29.1
35.8
49.3
55.3
354300
906900
29.1
35.8
13.5
19.5
29.1
35.8
49.3
55.3
2004 190400
2005 190400
381000
800000
29.1
35.8
12.0
15.5
29.1
35.8
47.8
51.3
Note: Main area comprises all of Norway except the northernmost county and municipalities bordering to this
county (this northernmost area is also characterised by having zero pay-roll tax). The marginal tax rate varies quite
strongly for very low earning levels. All executives in our sample face a marginal tax rate of at least 29.1 percent.
TABLE 2
DESCRIPTIVE STATISTICS 1995-2005.
All
Anticipated reduced marginal tax
Average reduction in marginal tax
Return on sale
Yearly executive earnings (1000)
Log number of employees
Log capital value
Log value added per hour
Average worker earnings(1000)
Multi-plant firm
Union density
Foreign citizenship executive
Mean
15.1
4.2
521.6
2.9
7.5
-1.4
215.2
10.9
32.1
4.5
Std.
35.8
10.5
386.5
1.1
2.0
0.5
77.2
31.1
31.4
20.7
Anticipated reduction in
marginal earnings tax
Mean
Std.
-3.7
4.7
4.9
9.8
571.4
543.9
2.9
1.1
7.6
2.0
-1.3
0.5
228.3
78.1
10.0
30.0
31.5
31.1
6.0
42.4
No change in
anticipated marginal tax
Mean
Std.
4.1
10.6
512.8
350.5
2.9
1.1
7.5
2.0
-1.4
0.5
212.9
76.7
11.0
31.3
32.2
31.5
4.2
35.4
Number of observations
57515
8685
48830
Note: All money terms in 2005 Nok. All rates, operating margin and return on capital are expressed in percent.
Population of firms restricted to those firms where we can observe an executive for at least three consecutive years
and at least 5 employees during the period 1995 to 2005.
TABLE 3
FIRMS’ RETURN TO SALES
Reduction in anticipated marginal tax
No anticipated change in marginal tax
Year
Mean
Std.
Mean
Std.
1996
5.16
8.09
4.71
8.64
1997
5.19
8.84
5.16
9.66
1998
5.98
7.11
4.52
9.81
1999
4.60
9.56
2.37
11.81
2000
5.93
9.10
3.26
11.13
2001
4.32
8.57
3.28
11.33
2002
3.56
10.40
3.16
11.66
2003
4.24
11.49
3.33
11.18
2004
4.81
10.06
4.83
11.34
2005
5.34
10.27
4.14
10.32
Number of observations
8685
48830
Note: Population of firms restricted to those firms where we can observe an executive for at least three consecutive
years and at least an additional employee during the period 1995 to 2005. All money terms in 2005 Nok (in 1000).
Return to sale is reported in percent.
TABLE 4
FIRM PERFORMANCE AND TAX REFORMS. OLS AND OLS-FIRST-DIFFERENCED
REGRESSIONS.
Anticipated reduction in marginal tax rateft
Industry executive wage growthimt-1
Executive directly identifiedft
Pay-roll tax ratemt(%-points)
Log (vacancy/unemployment) mt
Log local employment mt
Industry plant-entry rateit(%-points)
Log workforce sizeft
Log capitalft
Log value added per work hourft
Average worker earningsft (in 1000)
Union densityft
Multi-plant firmft
Model 1
1.2935a
(0.1577)
Model 2
1.2925a
(0.1577)
0.3492
(0.2999)
Model 3
1.2849a
(0.1572)
0.3412
(0.2988)
0.3328
(0.2385)
0.1532 a
(0.0235)
0.1099
(0.1062)
-0.0790
(0.0555)
8.3677
(6.5223)
Model 4
1.2513 a
(0.1290)
-0.2293
(0.2874)
-0.6301 a
(0.1870)
0.0021
(0.0210)
-0.1200
(0.0888)
-0.2843 a
(0.0460)
-1.9689
(5.813)
1.6793 a
(0.1269)
-0.4785 a
(0.0571)
13.4074a
(0.3432)
-0.0298 a
(0.0021)
-2.8924 a
(0.2545)
-4.0412 a
(0.2582)
Model 5
0.8212a
(0.1285)
-0.1063
(0.2838)
-0.8215a
(0.1854)
-0.0099
(0.0206)
-0.1042
(0.0878)
-0.2221a
(0.0454)
0.8200
(5.6706)
1.8278a
(0.1292)
-0.4957a
(0.0571)
13.3852a
(0.3373)
-0.0284a
(0.0019)
-2.6608a
(0.2511)
-3.8566a
(0.2543)
Model 6
0.3109a
(0.1090)
-0.3650
(0.2094)
0.1988
(0.1616)
-0.0388
(0.0547)
0.0448
(0.0915)
0.1013
(0.0728)
6.2096
(6.0664)
6.3678a
(0.3191)
-0.5318a
(0.1433)
15.159 a
(0.5502)
-0.0063a
(0.0016)
-0.3120
(0.3981)
-1.3187a
(0.2865)
Additional controls
Year dummies
Yes
Yes
Yes
Yes
Yes
Yes
Executive characteristics
Yes
Yes
First-differenced obs. (fixed firm effects)
Yes
R2
0.006
0.006
0.008
0.330
0.342
0.348
F (Firms)/N (observations)
8751/57107
Note: Model 1 – 5: OLS. Model 6: OLS on first-differenced observations. Dep. Variable: firm return on sales in
percentft. All regressions include an intercept. Executive characteristics are: woman, foreign citizenship, years of
education (and squared), years of potential experience (and squared), years of seniority (and squared). In Model 6
woman, foreign citizenship, years of education (and squared), years of potential experience and years of seniority are
dropped from the executive characteristics vector Full results are available from the author upon request. All
standard errors are adjusted for firm-clustering. Robust standard errors presented in parenthesis. a and b denote 1 and
5 percent level of significance, respectively.
TABLE 5
EXECUTIVE FIXED PAY AND CHANGED RETURN TO EFFORT
Ant. reduction in marginal taxft-1
Return on salesft-1
(%-points)
Executive directly identifiedft
Pay-roll tax ratemt
(%-points)
Log local employment mt
Industry executive wage growthimt-1
Model
1
-24.862a
(5.207)
-0.005
(0.036)
282.957a
(10.198)
4.995a
(0.791)
47.176a
(3.464)
310.627a
(89.195)
Model
2
-42.045a
(6.588)
14.431a
(3.024)
289.116a
(11.064)
3.279a
(1.318)
47.991a
(3.562)
307.114a
(88.942)
Model
3
-3.805
(3.793)
4.126a
(1.027)
69.439a
(5.873)
-1.050
(0.837)
21.355a
(1.859)
243.994a
(81.399)
129.895a
(6.030)
10.160a
(2.051)
1.738a
(0.140)
-76.298a
(12.049)
87.615a
(16.014)
Model
4
-9.397b
(3.725)
4.031a
(1.011)
62.444a
(6.014)
-0.950
(0.833)
19.851a
(1.900)
241.362a
(82.013)
120.533a
(6.140)
9.834a
(2.013)
1.637a
(0.142)
-86.910a
(11.720)
83.919a
(15.704)
Model
5
-8.064 b
(3.725)
3.771 a
(0.940)
62.789 a
(6.049)
-0.569
(0.758)
19.592 a
(1.866)
236.945 a
(81.795)
121.260 a
(5.815)
8.651 a
(1.636)
1.662 a
(0.145)
-85.467 a
(11.508)
77.446 a
(14.086)
Model
6
-7.399b
(3.618)
3.627 a
(0.912)
62.999 a
(6.021)
-3.250 b
(1.301)
11.391 a
(2.316)
237.105 a
(81.671)
121.251 a
(5.833)
9.161 a
(1.689)
1.615 a
(0.145)
-75.663 a
(11.186)
72.069 a
(13.888)
Model
7
-6.046 b
(2.516)
0.039 a
(0.014)
28.984 a
(3.705)
-3.965 a
(1.538)
5.730 a
(2.082)
130.005 b
(56.954)
25.815 a
(4.850)
6.042 a
(2.181)
0.566 a
(0.125)
-6.898 a
(6.235)
-10.259
(28.014)
Model
8
-6.086 b
(2.537)
0.046
(0.554)
28.905 a
(3.694)
-3.962 a
(1.539)
5.709 a
(2.082)
130.050 b
(57.012)
25.885 a
(4.565)
6.028 a
(2.122)
0.566 a
(0.125)
-7.038
(6.080)
-10.042
(28.206)
OLS
2SLS
2SLS
2SLS
2SLS
2SLS
OLS
2SLS
Log workforce sizeft
Log capitalft
Average worker earningsft (in 1000)
Union densityft
Multi-plant firmft
Estimation method:
Instruments:
Log value added per work hourft-1
Industry plant entry rateit-1
14.069a
-65.802
17.148 a 17.208 a 17.705 a 17.747 a
-59.163 -56.792 -64.283 -65.968
18.248 a
-52.533
Instrument strength/overidentification
F-test of excl. instru. first stage
Cragg-Donald F-stat
Hansen J chi square P-value
279.2
253.9
267.2
283.3
288.8
0.396
0.293
0.364
0.317
0.321
88.57
331.4
0.453
Additional controls
Year dummies
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Executive characteristics
Yes
Yes
Yes
Yes
Yes
Industry (1-digit SIC)
Yes
Yes
Region (18 counties)
Yes
First-differ. obs. (fixed firm effects)
Yes
Yes
F (Firms)/N (observations)
8751/57107
Note: Model 1: OLS, Model 2 – 6: 2SLS. Model 7-8: 2SLS on first-differenced observations. Dependent variable:
yearly earnings (in 1000)ft. Model 2 – 8: additional endogenous variable: firm return on sales in percentft-1. All
regressions include an intercept. Executive characteristics are: woman, foreign citizenship, years of education (and
squared), years of potential experience (and squared), years of seniority (and squared). In Models 7-8 woman, foreign
citizenship, years of education (and squared), years of potential experience and years of seniority are dropped from
the executive characteristics vector Full results are available from the author upon request. All standard errors are
adjusted for firm-clustering. Robust standard errors presented in parenthesis. a and b denote 1 and 5 percent level of
significance, respectively.
TABLE 6
EXECUTIVE PIECE-RATE ON PERFORMANCE AND CHANGED RETURN TO
EFFORT. 2SLS.
Return on salesft-1
(%-points)
Executive directly identifiedft
Pay-roll tax ratemt
(%-points)
Log local employment mt
Industry executive wage growthimt-1
Log workforce sizeft
Log capitalft
Average worker earningsft (in 1000)
Union densityft
Multi-plant firmft
Model 1
Ant.
No
reduction in
anticipated
marginal tax
changes
3.846 a
7.726 a
(1.109)
(1.022)
45.739 a
71.129 a
(17.754)
(5.918)
0.230
-1.240
(1.060)
(0.855)
12.855 a
22.273 a
(2.577)
(1.913)
407.322
229.503
(280.906)
(82.347)
129.547 a
129.769 a
(8.642)
(6.200)
3.511
10.688 a
(2.293)
(2.168)
1.408 a
1.760 a
(0.125)
(0.145)
-26.074
-80.996
(14.140)
(12.567)
64.882 a
89.345 a
(19.479)
(16.429)
Model 2
Ant.
No
reduction in
anticipated
marginal tax
changes
6.821 a
3.386 a
(1.022)
(0.910)
33.140 a
64.670 a
(14.632)
(6.078)
-1.572
-3.476 a
(1.729)
(1.348)
6.462 b
12.030 a
(2.776)
(2.409)
398.443
223.401 a
(283.412)
(83.044)
123.257 a
121.015 a
(8.572)
(6.072)
3.404
9.603 a
(2.241)
(1.784)
1.294 a
1.640 a
(0.120)
(0.151)
-34.084 b
-79.504 a
(14.672)
(12.567)
50.990 a
73.773 a
(17.713)
(14.366)
Instruments:
Log value added per work hourft-1
Industry plant entry rateit-1
12.732 a
-8.228
17.573 a
-66.414
13.527 a
-27.295b
18.147 a
-71.419
158.8
0.928
232.2
0.262
158.8
0.610
263.1
0.320
Instrument strength/overidentification
F-test of excl. instru. first stage
Hansen J chi square P-value
Additional controls
Year dummies
Yes
Yes
Yes
Yes
Executive characteristics
Yes
Yes
Industry (1-digit SIC) and region (18 counties)
Yes
Yes
F (Firms)/N (observations)
3921/5035
8779/43590
3921/5035
8779/43590
Note: All models estimated by 2SLS. Dependent variable: yearly earnings (in 1000)ft. Additional endogenous variable:
firm return on sales in percentft-1. All regressions include an intercept. Executive characteristics are: woman, foreign
citizenship, years of education (and squared), years of potential experience (and squared), years of seniority (and
squared). Full results are available from the author upon request. All standard errors are adjusted for firm-clustering.
Robust standard errors presented in parenthesis. a and b denote 1 and 5 percent level of significance, respectively.
TABLE 7.
EXECUTIVES’ OUTSIDE OPTIONS AND CHANGED RETURN TO EFFORT. 2SLS.
Return on salesft-1
(%-points)
Executive directly identifiedft
Pay-roll tax ratemt
(%-points)
Log local employment mt
Industry executive wage growthimt-1
Log workforce sizeft
Log capitalft
Average worker earningsft (in 1000)
Union densityft
Multi-plant firmft
Foreign citizenship
Ant.
No
reduction in
anticipated
marginal tax
changes
3.866 b
5.567 a
(2.072)
(1.711)
101.721 a
28.554
(47.268)
(22.237)
-9.262
-7.376 b
(5.186)
(2.985)
32.238 a
22.767 a
(11.984)
(7.431)
-82.958
98.209
(143.222)
(142.466)
164.942 a
186.484 a
(25.018)
(25.647)
13.076
8.762
(9.645)
(5.641)
1.215 a
1.628 a
(0.247)
(0.162)
18.247
-108.919
(69.319)
(63.241)
9.540
104.817
(86.080)
(64.796)
Norwegians
Ant.
No
reduction in
anticipated
marginal tax
changes
7.895 a
3.852 a
(1.149)
(1.067)
39.630 a
73.867 a
(15.395)
(6.127)
0.396
-1.008
(1.078)
(0.878)
12.460 a
22.150 a
(2.607)
(1.940)
420.502
237.279 a
(289.091)
(89.208)
128.637 a
126.422 a
(8.738)
(6.231)
3.366
10.757 a
(2.298)
(2.241)
1.411 a
1.767 a
(0.129)
(0.155)
-27.750
-78.946 a
(14.285)
(12.665)
67.869 a
89.914 a
(19.380)
(16.632)
Instruments:
Log value added per work hourft-1
Industry plant entry rateit-1
16.970 a
122.462b
15.254 a
-39.209
12.595 a
-10.042
17.700 a
-68.215
Instrument strength/overidentification
F-test of excl. instru. first stage
25.7
153.2
151.0
215.1
Hansen J chi square P-value
0.103
0.173
0.654
0.313
F (Firms)/N (observations)
134/149
860/2078
3818/4886
8560/41512
Note: All models estimated by 2SLS. Dependent variable: yearly earnings (in 1000)ft. Additional endogenous variable:
firm return on sales in percentft-1. All regressions include an intercept and year dummies. Note that in the first
regressions (on foreign citizen executives anticipating tax cuts), lack of observations forced us to exclude two year
dummies to achieve a full rank covariance matrix of moment conditions. Full results are available from the author
upon request. All standard errors are adjusted for firm-clustering. Robust standard errors presented in parenthesis. a
and b denote 1 and 5 percent level of significance, respectively.