Performance Pay and Wage Inequality

Evidence from Germany and Great Britain∗

Pieter De Vlieger

University of Michigan

May 28, 2016

Abstract

This paper tests of model of performance pay and wage inequality, proposed by Lemieux,

MacLeod and Parent, in Germany and Great Britain. In this model, changes in the return to skill

motivates employers to adopt performance pay schemes, which, in turn, translates into higher

wage inequality. I test the empirical predictions using the German Sozio-Oekonomisch Panel

(1984-2008) and British Panel Household Survey (1993-2008). I address non-standard issues of

misclassification to show I am less likely to detect the empirical predictions of the model. The

results are consistent with performance pay as a channel to deal with underlying changes in

returns to skill in Germany, but I find no evidence that supports this role for performance pay

in Germany. Using reweighting and novel RIF decomposition techniques, I assess the effect of

performance pay on the wage distribution. In Great Britain, I find performance pay has led

to a widening of the wage distribution in the upper half, which is consistent with the model of

performance pay. Despite a large increase in the dispersion of earnings in Germany, performance

pay does not seem to be a main contributor. Taken together, these results indicate that different

labor markets may deal with changes in the returns to skill in different ways, despite similar

trends in these returns.

∗pdvl@umich.edu. I want to especially thank Stephen Machin, under whose supervision I started this project. Ialso want to thank John Bound, Charlie Brown, Gabor Kezdi, Jeff Smith, and Mel Stephens for useful commentsdiscussions and seminar participants at the University of Michigan. All remaining errors are my own.

1 Introduction

Performance pay forms has become increasingly important in the labor market, representing an in-

creasing fraction of the workforce and share of the total wage bill over time (Bryson et al. [2008]).

This has sparked a great deal of research interest in the impact of performance pay on a number

of labor market outcomes (see, for instance, Brown and Heywood [2002] for a cross-country anal-

ysis and Bryson et al. [2012] for more recent evidence on the US and Europe). One particular

outcome that has received a lot of attention is wage inequality. Piketty and Saez [2003] find that

performance pay workers are usually concentrated in the upper tail of the wage distribution where

increases in wage inequality have been particularly dramatic. Similarly, Bell and Van Reenen [2010]

provide evidence that performance pay and bonuses are associated with extreme wage inequality

in Great Britain, especially in the financial sector. However, also when taking a broader look at

the workforce, it is a well known result that performance pay jobs are associated with higher mean

wages compared to non-performance pay jobs (Pencavel [1978]; Brown [1990]; Booth and Frank

[1999]; Bryson et al. [2012]) and that performance pay leads to higher within-firm wage inequality

(Lazear [2000]).

Lemieux, MacLeod and Parent [2009], henceforth referred to as LMP, posit a model where

employers adopt performance pay schemes as an endogenous response to changes in the returns to

skills. On the one hand, technological advances have decreased monitoring costs, making it easier

for employers to implement performance pay schemes (Bloom and Van Reenen [2011]; Henneberger

et al. [2007]). On the other hand, Skill Biased Technological Change (SBTC) has increased the

productivity wedge between skilled and unskilled workers in recent decades (Autor et al. [2006]).

As introducing performance pay typically leads to sorting of more productive workers into these

types of jobs (see, for instance, Lazear [2000] for firm-level evidence), benefits may now exceed

technology investments. They test this model using PSID survey data from 1976 through 1998,

and find evidence that supports performance pay as an endogenous response to SBTC. Furthermore,

they show the increase in performance pay accounts for about a quarter of the increase in wage

inequality over this period.

It is not clear, however, whether performance pay is used as a means to channel changes in

the returns to skill across different countries – in other words, whether this model of performance

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pay holds more generally in other countries. There are several reasons why it might not. First,

several institutional factors of the labor market, such as unions or the minimum wage, have been

shown to affect wage inequality (DiNardo et al. [1996]). These differ across countries and could

limit the extent to which performance pay can channel changes in the returns to skill. Second,

output is often hard to measure in many of the jobs that use performance pay schemes, casting

some doubt on monitoring as the main driver. Second, Finally, the SBTC hypothesis has been met

with some skepticism (Card and DiNardo [2002]), and it’s an open question whether this model of

performance pay and wage inequality can fit more recent trends where especially highly educated

workers have seen wage increases.

In this paper, I use micro survey data from Germany and Great Britain to test this model

of performance pay and wage inequality. As Machin and Van Reenen [1998] point out, rising

wage inequality is not strictly a US phenomenon, and the use of performance pay can be quite

heterogeneous across countries (Brown and Heywood [2002]). I focus on Germany and Great

Britain for two key reasons. First, these countries have had similar experiences to the US in

terms of wage inequality, but provide a different institutional setting and timing of wage inequality

trends. (Dustmann et al. [2009]). Second, the SOEP (Sozio-Oekonomisch Panel) in Germany

and the BHPS (British Household Panel Survey) in Great Britain provide data sources that are

very similar to the PSID, both in terms of scope and set-up. I focus on the period 1985-2008 for

Germany and 1991-2010 for Great Britain.

Additionally, I adjust my performance pay measures for misclassification error building on

the work of Bound et al. [2001] and Card [1996]. I show that I am less likely to detect the empirical

implications of the model, but also that several specifications may suffer from omitted variable

bias. I use outside information on the use of different types of performance pay jobs and try to

quantify the impact of misclassification on the final estimates. Finally, I investigate the impact of

performance pay jobs on the distribution of wages using a reweighting approach as proposed by

DiNardo et al. [1996] and use novel Recentered Influence Function (RIF) techniques as proposed

by Firpo et al. [2009] to decompose changes at various percentiles of the distribution.

I find two key results. First, this model of performance pay and wage inequality fits the

British data quite well. Wages in performance pay jobs are more closely linked to observable skills,

such as higher levels of education, and this relationship has become more pronounced over time.

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In Germany, however, this is not the case. While wages in performance pay jobs are also typically

linked to productive characteristics, such as high education levels, this has not changed over time.

Second, the decomposition results further confirm this schism. Performance pay jobs in Great

Britain account for about 10% of the increase of the variance in the wage distribution from the

early 90s to the late 00s, and percentile decompositions confirm that these changes are concentrated

in the upper half of the wage distribution. While performance pay jobs in Germany account for

about 7% of the increase of the variance of the wage distribution between the mid 80s and late

00s, much of this dispersion is concentrated in the lower half of the distribution, a finding at odds

with the SBTC hypothesis. Overall, these results suggest that despite similar experiences in terms

of returns to skills and wage inequality, there are cross-country differences in how these returns to

skills are channeled through performance pay.

This paper proceeds as follows. Section 2 shortly describes the LMP model, and the key

empirical implications and econometric specification it gives rise to. Section 3 describes the data,

the performance pay measure, and the misclassification adjustment. Section 4 presents the empir-

ical tests fo the model. Section 5 presents RIF regressions and decomposition results. Section 6

concludes.

2 Performance pay: model and empirical implications.

This section provides a stylized overview of the model presented by LMP. In a first step, the model

considers wage setting at the job match level and distills four empirical implications. In a second

step, the model considers effects across the labor market and beyond the job-match alone.

2.1 Wages, selection rules and implications at the job level.

At the job match level, firms are free to choose between fixed and performance pay wage setting.

On the employer side, setting up performance pay contracts entails a cost-benefit analysis between

monitoring costs and returns to monitoring. Exerting effort also entails a cost-benefit analysis for

the worker. This situation can be represented by a utility function for worker i (equation 1) and

an output function for employer j (equation 2).

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Uij = wij − exp(eij − αi) (1)

yij = kj + β · γj · eij (2)

As eij represents the effort level by the worker and αi represents the ability of that worker, the

utility function captures the incentive to exert effort according to one’s ability. The worker-specific

ability αi is modeled as a single-index type of ability and assumed to follow a normal distribution,

αi ∼ N(αi, σ2i ). The expected ability and variance are assumed to be common knowledge once

characteristics (such as schooling and experience) are observed.

The output function (equation 2) assumes a minimum level of output kj , regardless of

the worker providing effort. The parameters γj and β represent job-specific and market-specific

marginal returns respectively.1 This results in two key equations, where mj functions as an inter-

cept2 and Mj the monitoring cost: a fixed wage equation 3 and a performance pay wage equation

4.

wFWij = mj + β · γj ·(αi − σ2i

)(3)

wPPij = (mj −Mj) + β · γj · αi (4)

This leads to a selection rule, equation 5, that describes when, in expectation, wages under

performance pay contracts exceed wages in fixed wage contracts. Overall, performance pay jobs are

associated with high market-wide and job-specific returns to effort and a high (conditional) variance

of ability. Changes in the incidence of performance pay can happen either through a decrease of

monitoring costs (the right hand side) or an increase in benefits (the left hand side).

β · γj · σ2i ≥Mj (5)

In order to compare the returns to expected skills (such as education and experience), it is

1Some jobs are more sensitive to effort than others, which translates to heterogeneity in γj . One example, forinstance are cleaning services (higher return to effort) and parking guard (lower return to effort). Broad, market-wide changes to returns (such as SBTC) can change the return to effort in all jobs at the same time. For instance,machines can speed up the work professional cleaning services provide, while computers might make the work ofparking guards more efficients (e.g. when cars check out of the parking lot). It is these two different channels thatthese two parameters attempt to capture.

2More specifically, mj = kj + β · γj · log(β · γj).

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useful to rewrite equation 3 and 4. First, it is reasonable to assume the variance is increasing in

expected ability. For simplicity, LMP assume that σ2i = δ · αi and plug this in equation 3. Equation

4 is rewritten to be more comparable.

wFWij = mj + β · γj · (1− δ)︸ ︷︷ ︸Lower skill return

·αi (6)

wPPij = (mj −Mj)︸ ︷︷ ︸Lower Intercept

+β · γj · αi + β · γj · (αi − αi)︸ ︷︷ ︸Additional error component

(7)

This leads to three empirical implications of performance pay at the job level:

(I.1) The wage intercept is lower for performance pay jobs.

(I.2) Observable skills are rewarded more in performance pay jobs.

(I.3) The variance term of the worker ability is higher in performance pay jobs.

2.2 Implications across jobs and for error components.

The results so far hold the job match constant. Relaxing this condition, leads to testable implica-

tions for job characteristics and changes in market-wide returns to effort β. First, LMP argue that

job characteristics might be rewarded less in performance pay jobs. They find returns to tenure are

lower in performance pay jobs, and therefore interpret tenure as a job characteristic. Nevertheless,

they acknowledge tenure could possibly be a productive worker characteristic as well. Second, the

variance of the firm-specific component might also be rewarded less in performance pay jobs. Third,

increases in β leads to increasing returns to education being more pronounced in performance pay

jobs than in non-performance jobs. In other words, LMP expect to find returns to education to

increase faster in performance pay jobs than in non-performance pay jobs.

(I.4) The return to observable job characteristics may be lower in performance pay jobs.

(I.5) The variance of the firm-specific component may be smaller in performance pay jobs.

(I.6) If returns to education increase, they do so faster in performance pay jobs.

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2.3 Econometric Specification.

The implications can be tested using split sample regressions and pooled interaction regressions.

The split sample regression can be written as

wgijt = agt + xit · bgt + zijt · cgt + εgijt (8)

where g indexes performance pay or non-performance pay jobs, wgijt stands for the wage for worker

i in jobmatch j at time t, and xit and zijt stand for the observable worker and the observable job

characteristics respectively. The model assumes the error term consists of different error compo-

nents.

εgijt = dgt · ζi + νgij + ugijt (9)

where ζi is the unobserved (demeaned) ability of the worker, dgt is the return to this unobserved

ability, νgij represents an error component for the jobmatch and ugijt represents the idiosyncratic

error.

Alternatively, when adding in a vector of control variables, it is possible to pool all observa-

tions and impose the coefficients across these control variables are equal. Using interactions, the

model can then be tested using an indicator PPijt taking on the value 1 when worker i is in a

performance pay job j at time t.

wijt = at + appt × PPijt + xit · bt + xit · bppt × PPijt + zijt · ct + zijt · cppt × PPijt + εijt (10)

Both regressions will be used to test the model’s implications on the coefficients. The variance

analysis on the error components will be performed using the error terms from the split sample

regressions reported in equation 9.

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3 Data and empirical strategy.

3.1 The SOEP and BHPS.

The SOEP and the BHPS are similar in scope, spirit and subject to the PSID.3 Households that

constitute a representative sample of the population as a whole are surveyed on a yearly basis.

There is entry and exit, so these panels are typically unbalanced. The PSID, SOEP and BHPS

started in 1968, 1984 and 1991 respectively and with about 5,000 households each. I focus on the

period 1985-2008 for the SOEP and the period 1991-2008 for the BHPS, the years for which the

data was made available to me.

There are two key advantages in using the BHPS and the SOEP to test the model. First,

it provides representative samples for relatively long periods and, second, the data are broadly

comparable to the PSID. However, some of the issues that arise with PSID data also carry over.

First, outcomes are self-reported and may therefore suffer from measurement error. Second, the

survey is relatively small relative to the total population and participation to these surveys is

entirely voluntary. As Bell and Van Reenen [2010] point out, people at the top end of the wage

distribution are probably more likely to opt out and could therefore be underrepresented in these

samples. Similary, Dustmann et al. [2009] find some differences in terms of wage inequality pattern

when comparing the SOEP to administrative income data. Third, even though the design of

these household surveys is broadly similar, there are inevitable differences in questions and actual

practices across countries. Therefore, these datasets can only present broad comparisons between

these countries.

The sample selection follows LPM for both datasets. Males aged 20 to 65 are retained, and

public sector employees are excluded, since the public sector wage sector wage-setting process may

work differently. Additionally, there is evidence of differences in performance pay practices across

the private and public sector (see, e.g. Bryson et al. [2008] for some descriptive evidence on Great

Britain). Wages are available on a yearly (BHPS) or monthly basis (SOEP). It is natural to rescale

these numbers to hourly earnings to account for hours worked. This means we need positive wages

and working hours for the analysis. Following LMP, we restrict the hourly wages to be between 1.00

3There is a large body of research that uses these panel surveys for cross-country analysis (see, e.g., Lersch (2012),Jones et al. (2009), Brynin and Longhi (2009), among others). One particularly interesting study, Fraessdorf et al.(2008), investigates the effect of capital income inequality on income inequality.

7

and 100.00 pounds (euros) in Great Britain (Germany).4 The East-German sample of the SOEP is

excluded, as it is customary to focus on the West-German sample when analyzing the SOEP (see,

e.g., Dustmann and van Soest [1998]).

3.2 Defining performance pay.

I use the measurement framework of LMP to define performance pay jobs, which consists of two

steps. In a first step, survey responses are used to see whether workers are ever paid for performance.

In a second step, a job spell is categorized as a performance pay job if the worker ever gets paid

for performance over this spell.5 This framework likely underestimates the number of performance

pay jobs, as some workers on these contracts may never meet the requirements to get paid for

performance. These worker-job matches would incorrectly be categorized as non performance pay

jobs.

Both the SOEP and BHPS contain questions on variable wage schedules. I follow Booth

and Frank [1999] to identify performance payments in the BHPS, using the question whether the

respondent’s pay “ever includes incentive bonuses or profit related pay”. This question is available

in all waves. In the SOEP, I use a similar question that asks whether respondents’ pay ever includes

profit-related bonuses or “Gratifikation”. This question is available in all waves. These measures

may misclassify performance pay jobs, as both question refer to piece rates, profit sharing and

other types of perfomance pay.6 In Germany, other bonus measures (such as Christmas bonuses)

are available, but typically do not depend on performance and are therefore not included in the

analysis.

The definition also poses a key problem that is typical of duration data – the endpoint

4LMP restrict the sample to earnings between 1.50$ and 100.00$. This accounts for about XXX percent of thesample in Germany and 0.1 percent of the sample in Great Britain. Additionally, I restrict observations in GreatBritain to have annual hours worked between 1,000 and 4,500 hours and observations in Germany to have monthlyearnings above than 600 euros. These sample restrictions result in an additional loss of XXX percent of the sample inGermany and 3.5 percent of the sample in Great Britain. The excluded observations are mainly situated in the lowertail of the earnings distribution. The results are robust to the inclusion of the upper tail outliers – these upper tailoutliers are driven by low reported hours worked, not high earnings. To stay true to the setup of LMP, subsequentresults will be based on the restricted interval.

5Both the LMP and the tenure variables in the SOEP are based on the employer-employee relationship. TheBHPS, however, asks people how long they have held their current occupational status, treating promotions as an“occupational job change”. This means the tenure variable needs to be adjusted for promotion, as in Blundell et al.(2008). I do this using the job history files of the BHPS.

6The PSID sample in LMP also suffers from misclassification error. Yet, LMP argue they are more likely to classifya job as a performance pay job. Therefore, their misclassification problem is the reverse of the one discussed in thispaper.

8

problem. If a person moves out of a performance pay job after joining the panel survey or enters a

performance pay job just before leaving the panel survey. We may misclassify these job matches as

non-performance pay jobs if actual performance pay is received before or after the observed waves.

I adjust for this issue using the three step endpoint correction method used in LMP, and – in line

with their results – find that this adjustment does not adjust the results. Therefore, the reported

regressions results do not adjust for this endpoint problem.

3.3 Misclassification Error.

As mentioned above, the performance pay indicator covers not only piece rates and similar types

of performance pay, but also profit sharing and other types of pay where final renumeration does

not necessarily strictly depend on the worker’s effort level. I investigate what this misclassification

error means for the regression equations 8, 9 and 10 building on the methodology of Bound et al.

[2001] and Card [1996].7 For simplicity, I leave out job characteristics, although the results carry

over in a straightforward way.

Denote PP ∗it as the true performance pay status and PPit as the observed performance

pay status. I define π as the true fraction of performance pay workers and p as the observed

fraction of performance pay workers. In the scenario considered here, π < p. I further define

P(PPit = 1|PP ∗it = 1) ≡ q1 and P(PPit = 1|PP ∗it = 0) ≡ q0 with q0 < q1. Since misclassification is

only showing up for workers reporting being in a performance pay job, q1 = 1. The split sample

econometric specification now contains some error for both split samples. When considering the

return to the observable productive characteristics, I show the following relationships hold:

if PP = 1 : E[wPPit |Xit] = 1p{q1πb

PP ∗t + q0(1− π)bnPP

∗t }xit

if PP = 0 : E[wnPPit |Xit] = 11−p{(1− q1)πb

PP ∗t + (1− q0)(1− π)bnPP

∗t }xit

(11)

The coefficients are contaminated by misclassification, but the final extent of its effect depends

on the actual misclassification rates. Outside data is not available for Germany, but the WERS in

Great Britain provides some notion of possible misclassification.8 It covers years 1998 and 2004, and

7To be clear, I assume away any misclassification error that arises from survey response mistakes. Misclassificationonly arises from the questions pooling different types of performance pay. The technical details are presented in moredetail in appendix XXXX.

8It is far from an ideal measure though, as the percentages apply to firms and establishments, not workers. Larger

9

indicates that about 35% of workplaces use profit-related pay, about 20% use employee ownership

schemes, and the use of performace-related pay in British workplaces went up from about 20% to

45% [Kersley et al, 2006].

To fix ideas, I assume about 50% of the reported performance pay jobs are actual performance

pay jobs, with the remainder consisting of profit sharing pay and similar payment schemes, so

q0 = 0.50. About 70% of my sample reports being in a performance pay job, so I set p = 0.70, with

π = 0.35. As mentioned before, q1 = 1.00. Under this scenario, the estimated coefficient on xit is

0.5× bPP ∗t + 0.5× bnPP ∗

t for the performance pay sample and bnPP∗

t for the non performance pay

sample. Therefore, misclassification only biases coefficients in the performance pay sample and are

less likely to confirm the testable implications.

The impact of misclassification error on the interacted regressions is somewhat more involved.

Suppose the misclassification error can be characterized as PP ∗it = γ0 +γ1PPit+xitγ2 +ηit. If γ2 is

zero, I show that γ1 = πp

[q1−p1−p

], or, under our assumptions, γ = 0.50. Nevertheless, it is reasonable

to assume γ2 is different from zero. Then the observable characteristics in equation 10 will show up

in this regression, but also in the performance pay indicator. This will lead to interactions of these

observables that are now part of the error term, leading to omitted variable bias. Additionally, the

formula for γ1 also changes to

γ1 =Cov(PP ∗it, PPit)− (q1 − q0)c′VXXcV ar(PP ∗it, PPit)− (q1 − q0)2c′VXXc

(12)

Whenever adjustments are made below, they will be made using the simplification that γ2 is

zero. A more completely discussion can be found in the appendix.

3.4 Descriptive statistics and evidence.

Table 1 presents descriptive statistics for both Great Britain and Germany, comparing sample

means between performance pay and non-performance pay jobs. Workers in performance pay jobs

typically earn more, work longer hours, are better educated and stay with their employer longer

than their counterparts in non-performance pay jobs. Unionization rates in Great Britain are higher

firms may not provide performance pay to all their workers, and even when they provide it to all workers, theseoutcomes do not reflect on workers directly. Nevertheless, they present a useful point of departure. See the appendixfor more detail.

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for performance pay jobs, reflecting the well known positive association between unionization and

firm size.9 Unionization rate questions are not available for most waves in the SOEP (see, e.g.,

Goerke and Pannenberg [2008] that use NACE codes to identify unionization membership in the

SOEP).10 Differences in marital status and minority rates across performance pay workers and non-

performance pay workers are not very pronounced. Table 2 also highlights workers in performance

pay jobs tend to be higher educated.11

Figure 1 presents graphical evidence on trends and importance of performance pay in the

British (panel [a]) and German (panel [b]) workforce. On the one hand, performance pay and

performance pay jobs incidence in Great Britain increased in the early nineties, largely flattening

out in the subsequent years. This is largely consistent with the findings of Bryson et al. [2008]

and Bloom and Van Reenen [2011]. On the other hand, Germany has experienced a more steadfast

growth in performance pay and performance pay jobs, especially after 1995. Both figures also

highlight end-point problems do exist in the data, but affect performance pay job incidence in

a minimal way.13 Furthermore, panels [c] and [d] (figure 1) confirm that trends and changes in

performance pay incidence are robust to redefining our measure of performance pay jobs as having

received performance pay at least one out of five or two times while being observed on the job.

Figure 2 presents wage distributions and the change in the standard deviation of log wages

across performance pay and non-performance pay jobs. Panel [a] confirms that British performance

pay wages have a higher mean, median and variance than their non-performance pay counterparts.

Panel [b] shows that these differences are markedly stronger in Germany. This could be explained

in two ways. First, Booth and Frank (1999) point out that the wording of the BHPS question

we use to identify performance pay does not strictly distinguish between incentive pay and piece

rates or profit sharing. The extent to which this happens in Germany may be different. Second,

performance pay has permeated the labor market much more in Great Britain, while performance

pay in Germany is largely part of the rising upper tail inequality in the German wage distribution

9This is not the first paper to find higher unionization rates in Great Britain for performance pay jobs. Boothand Frank [1999] provide similar results based on BHPS data and use the same variable to identify performance pay.

10It is therefore worthwhile to note here that all regressions in the subsequent analysis will control for (1-digit)industry (10 in Great Britain, 17 in Germany) and (1-digit) occupation codes (10 in both Great Britain and Germany).

11For Great Britain, we keep the seven educational levels as defined by the BHPS.12 For Germany, we define fourdummy variables: University (university/ technical college, college abroad), Technical (technical college), Vocational(apprenticeship, vocational school, etc.), None.

13End-point problems in the period 2005-2008 pose no real problem in the BHPS.

11

(Dustmann et al. [2009]).

Panel [c] and [d] present the standard deviation of wages across performance pay jobs and

non performance pay jobs, and cast some doubt on the link between performance pay and wage

inequality in Germany. Increases in wage inequality seem to be concentrated in performance pay

jobs in Great Britain (panel [c]), but Germany exhibits strong increases in wage inequality in both

jobs. If anything, wage inequality increases faster in non performance pay jobs than in performance

pay jobs (panel [d]). presents graphical evidence of the link between wage inequality (as measured

by the standard deviation of log hourly earnings) and performance payment.

4 Regression framework.

4.1 Effect of performance pay on wages.

Table 3 presents a simple framework to analyze the effect of performance pay on wages. All five

regression specifications control for standard worker specific controls (education, a quadratic in po-

tential experience and dummies for ethnicity, marital status and for region) and job characteristics

(union status, a quadratic in job tenure and dummies for industry and occupation).14 The speci-

fications also control for year effects. The first four columns highlight that performance pay jobs

are associated with higher wages, even when controlling for receiving performance pay during the

current year (both under an OLS and a fixed effects framework). Column five introduces job match

fixed effects alongside worker fixed effects and estimates the effect of receiving performance pay

over the past year. The positive and statistically significant coefficients provide evidence that, even

after taking into account individual and job-specific characteristics, performance pay is associated

with higher wages and does not merely replace base pay in non-performance pay jobs. Overall, the

effects are consistent with positive sorting into performance pay as predicted by Lazear (1986) and

line up very closely with the results LMP find using the PSID.

14These regional fixed effects (or regional dummies) capture local determinants such as local unemployment levels.Industry dummies capture the union status of workers for Germany. Education is defined in terms of degrees andreported for three categories (high, medium, low). In Germany these are set to university (high), technical (medium)and vocational (low); in Great Britain, these are set to first or higher degree (high), HND, HNC or teaching (medium)and A-level (low). Lower levels of education in Great Britain are included in the regressions but not reported forpresentation issues. The estimated effects are consistent with the reported results unless otherwise noted.

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4.2 Returns to worker and job characteristics.

Table 4 sets up an OLS regression framework and tests an identical specification on a split sample

(a sample of workers in performance pay jobs and a sample of workers in non-performance pay

jobs). The first two columns report results for Great Britain, the third and fourth column show

coefficients for Great Britain adjusted for misclassification. The final two columns report results

for Germany. The dependent variable, yit, is the log of hourly earnings and is indexed by worker (i)

and wave (t). The indicator PPit takes on two values; 1 if the worker i is in a performance pay job

during wave t and 0 if not. Education are sets of dummy variables for the relevant education levels

in Germany and Great Britain. The marginal effects of experience and tenure are evaluated at

one year. Overall, the results indicate that the returns to observable skills, such as experience and

education, tend to be higher under performance pay contracts than under non-performance pay

contracts. Whereas LMP found tenure to get rewarded less in performance pay jobs and therefore

interpreted it as a job-specific characteristic, there seem to be no real difference between the return

on tenure across both types of jobs in Germany or Great Britain. Adjusting for misclassification

error in Great Britain does not change the results substantially.

Table 5 pools all observations for each country in one sample, forcing the effect of the controls

(such as union or marital status) to be the same for both groups. The columns are as in table 4,

but the second column now adjusts for misclassification in the interacted indicator. A performance

pay job dummy and a set of interaction variables (education, experience and tenure interacted with

the performance pay job dummy) provide tests for the model implications.15

The first prediction, that performance pay jobs have lower intercepts than non performance

pay jobs, does not hold in any of the two countries. The intercept difference is minor and not

statistically insignificant in Great Britain. The high incidence of performance pay jobs in Great

Britain could possibly have leveled the intercepts. This intercept is much higher and statistically

significant at 1% for Germany, providing a much clearer rejection of the model. The second predic-

tion, a higher return to worker observables in performance pay jobs, is borne out in both countries

for high levels of education and experience. In contrast with the PSID results in LMP, tenure is

15These interaction variables entail six interaction variables for Great Britain and three interaction variables forGerman education levels. Two interaction variables are added for experience and tenure each, as the quadratic formsare also imposed on these interaction variables. As before, the effects of these quadratic interaction variables forexperience and tenure and evaluated at one year.

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rewarded the same in both jobs. As the predictions of LMP were not as clearcut for this prediction,

I do not interpret this finding as support in favor or against the model. Adjusting for misclassi-

fication error does not change the point estimates in a meaningful way. The standard errors for

this column are bootstrapped, since the standard error computation for misclassification in the

interacted regression is not straightforward to compute (see appendix for details). Therefore, not

too much weight should be given to the standard errors.16 Overall, the predictions of the model

turn out to largely be confirmed and the misclassification correction shows there was a downward

bias in the original estimates.

Table 6 provides coefficient estimates that tests implication I.6, whether returns to education

have increased more rapidly in performance pay jobs than in non performance pay jobs. The

education variables are interacted with time period dummies for 3-year bins. Only the final period

interactions are reported for both countries to keep the table parsimonious, following LMP. The

prediction that returns to education have increased at a faster pace in performance pay jobs hold in

Great Britain, but less so in Germany. In the BHPS, these returns are largely captured by highly

educated workers. This is in line with prediction I.6, as it is mostly these education levels that have

witnessed large returns over this period in Great Britain. Misclassification error is not adjusted for

in these regressions, but would be largely have the same effect as it does in table 5.

In conclusion, we tested four of the six implications. The evidence on observable job charac-

teristics (I.4) was inconclusive in both countries, in part because the prediction was not clearcut.

Performance pay jobs in both countries reward observable skills more, in line with prediction I.2.

The intercept and SBTC predictions (I.1 and I.6 respectively) were firmly rejected in Germany,

while prediction I.6 holds up well in Great Britain. There seems to be sorting of workers across

both types of jobs in Germany, but changes in the returns to education do not seem to have changed

this as predicted by the LMP model.

16The point estimates on the experience and tenure variables are somewhat different. This is largely because itwas not straightforward to implement the bootstrap procedure where these variables were evaluated at one year.Therefore, these are the first order effects of the polynomial only (i.e. the linear part of the polynomial). The valuesfor these linear components are not very different between the adjusted and unadjusted regression before multiplyingthe relevant coefficients and standard errors by the relevant factor.

14

4.3 Variance component analysis of unobservables.

In order to test the final two predictions of the model, I perform an error component analysis using

the predicted error terms from equation 8. Conceptually, the idea is to estimate and compare the

variance of the worker and the jobmatch components from these error terms. Splitting up these

error components as in equation 9, I can rewrite

εppijt = dppt · ζi + νppij + uppijt

εnppijt = dnppt · ζi + νnppij + unppijt

The first equation is the error term of the regression that uses the performance pay job

sample; the second is the error term of the regression that uses the non-performance pay job

sample. The subscript j is added to capture jobmatch fixed effects. The structure imposed in this

step assumes an unobserved ability component (ζi) of worker i, a firm-specific wage component

(vij) and an idiosyncratic error term uijt. We present two different specifications, a model with a

worker component and a model with a worker and a job component, that are estimated separately

across two samples. One sample includes all workers, while a second sample focuses on people who

switched jobs only. Focusing on this switchers subsample is interesting as the underlying variance of

the person-specific component ζi is, in essence, the same for both performance and non-performance

pay jobs.

Table 7 presents the results of these different specifications for both the BHPS and the

SOEP. Consistent with prediction I.3, the variance of the worker-specific component is larger in

performance pay jobs than in non performance pay jobs. This holds for both samples and for both

the German and British datasets. Similar to the job characteristic findings in the previous section,

we do not find supportive evidence for prediction I.5, namely that the variance component of the

job-specific error component is smaller for performance pay jobs. Similar to the finding for job

characteristics, this could be interpreted as an inconclusive result that neither rejects or supports

the model’s predictions. Taken together, the predictions of the model hold up relatively well for

Great Britain, but are largely rejected by the German data.

15

4.4 Robustness checks.

Table 8 presents robustness checks for Great Britain. Since the performance pay job measure may be

crude, I use the two different definitions that are presented in figure 1 – having received performance

pay at least one out of five or two times while being observed on the jobmatch (column 1 and 2).

As there are some worries about the wording of the BHPS question, a subsample analysis of the

last 10 waves is performed using another, somewhat cleaner, question that asks about performance

pay (column 3).17 Finally, I also consider adding public sector workers to the sample (column 4).

The results show are robust to these different specifications, with the exception of the intercept.

Whereas it was insignificant before, it is not positive and significant, contradicting prediction I.1.

Table 9 presents robustness checks for Germany. The first two columns report the same

robustness checks as the first two columns in table 8. The final two columns include public sector

workers and females into the sample. Overall, the results for Germany are robust to these different

specifications.

Finally, it is possible the interval set out for the dependent variable (between 1 and 100 pounds

or euros) may be poorly chosen. However, the results are robust to winsorizing or trimming the

dependent variable or including the outliers above 100 pounds or euros (results not presented).

5 Decomposition of the wage distribution.

The central question this section explores is how changes in performance pay relate to wage inequal-

ities. A novel decomposition technique based on reweighting and Recentered Influence Functions

(RIFs) proposed by Firpo et al. [2009] is used, since this technique allows for decompositions at

percentiles and standard deviations, whereas the classic decomposition proposed by Oaxaca-Blinder

hold at the mean only [1973].

5.1 Rif-regressions and reweighting.

A RIF-regression is a standard regression where the dependent variable is replaced by the Recen-

tered Influence Function (RIF) of a statistic of interest of that same dependent variable (e.g. in

this case, a percentile or the variance of the log hourly earnings). A RIF is defined as RIF (y; v) =

17“Does your pay include performance related pay?” This question is only available for the last 10 waves.

16

v(Fy) + IF (y; v), where y is the dependent variable of interest (for instance, earnings), v(Fy) is

the statistic of interest of y and IF (y; v) denotes the Influence Function, defined such that the ex-

pected value of the RIF aggregates back to the statistic of interest. Assuming that the conditional

expectation of this RIF is a linear function of the covariates and that can be estimated using OLS,

this approach allows to now use classic Oaxaca-Blinder methods to go beyond the mean.

E[RIF (y; v)|X] = Xω

Yet, this does not provide a complete strategy to assess the differences in compositions and in

returns. Consider the overall difference of distributional statistic v(Fy) across the performance and

the non-performance pay group;

4vO = v(FY1|PP=1)− v(FY0|PP=1)︸ ︷︷ ︸

4vS

+ v(FY0|PP=1)− v(FY0|PP=0)︸ ︷︷ ︸4v

X

where 4vS is the wage structure effect and 4v

X denotes the composition effect. In order to ob-

tain a complete strategy to estimate these two effects, the counterfactual distributional statistic

v(FY0|PP=1) needs to be obtained. This is the distributional statistics that would have prevailed

if performance pay workers had been paid as non-performance pay workers. A popular method to

obtain this counterfactual is the reweighting method proposed by DiNardo et al. [1996], and it the

method used in the analysis.18

5.1.1 Quantile RIF-regressions.

The recentered influence function for quantile regressions is represented by the sum of the distri-

butional statistic of interest (Qτ ) and the influence function:

RIF (y;Qτ ) = Qτ +τ − 1[y ≤ Qτ ]

fY (Qτ )

This RIF can be estimated for given samples using sample quantiles and kernel methods (to

estimate the local densities). Once this sample RIF is obtained, it can be equated to the linear

18This counterfactual is straightforward to compute and consistent under the ignorability assumption [Firpo, 2007],which states that the treatment effect (in this case performance pay and non-performance pay) and the error termof the linear specification are conditionally indepent.

17

function of covariates to the obtain the estimated coefficients of this linear function:

ˆω(g, τ) = [∑i∈G

XiXti ]−1[∑i∈G

ˆRIF (Ygi;Qg,τ )Xi], g = PP,NPP

The results of these RIF quantile regressions are presented in figure 4 (for Germany) and

figure 5 (for Great Britain) and show a relatively strong consistency across both countries. The

quantile regressions for education levels highlight that bonus performance pay jobs are typically

associated with higher returns to schooling for higher degrees (panel [a] and [b] for Great Britain

and panel [a] and [b] for Germany). Vocational degrees Germany typically get higher returns to

education in non-performance pay jobs across the entire wage distribution, unless workers are in the

20% of the wage distribution. In this case, the returns are identical. Finally, the quantile regressions

in Great Britain portray lower returns in performance pay jobs compared to non-performance pay

jobs for all degrees in the bottom deciles of the wage distribution (10-15%).

5.1.2 Reweighting.

The reweighting approach applied is that of DiNardo et al. [1996], where the reweighting factor

can be defined and rewritten as

ψ(X) =dFXPP

dFXNPP

=Pr(PP = 1|X)/Pr(PP = 1))

PR(PP = 0|X)/Pr(PP = 0)

These probabilities can be obtained by taking the sample shares of performance and non-performance

pay jobs for the unconditional probabilities and by running a logit or probit model for the condi-

tional probabilities.19 Plugging in this reweighting factor in the distribution of non-performance

pay jobs gives:

FY C,PPNPP

(y) =

∫∫FYNPP |XNPP

(y|PP,X)ψ(PP,X)dFXNPP(X) (13)

=

∫∫FYNPP |XNPP

(y|PP,X)dFXPP

(X)

dFXNPP(X)

dFXNPP(X) (14)

=

∫∫FYNPP |XNPP

(y|PP,X)dFXPP(PP |X)dFXNPP

(X) (15)

19As proposed by DiNardo et al. (2006) we use a model that uses the standard set of controls used in the regressionsof table 4 and adds a set of interaction variables between education levels and experience and ethnicity.

18

In order to assess the changes over time, we compare reweighted results for performance pay at the

beginning of the sample to reweighted results at the end of the sample. Observations are pooled

across different years to minimize reweighting errors. In practice, the first three (1991–1993) and

last six (2003-2008) years are pooled together for the BHPS and the first five (1985–1989) and last

five (2004–2008) years are pooled together for the SOEP.20 Figure 3 plots the actual wage densities

by compensation and the counterfactual wage distribution if workers in performance pay jobs werer

paid as in non performance pay jobs.

Table 10 presents the results for Great Britain (upper panel) and Germany (lower panel).

The effect of performance pay on the dispersion of wages can be calculated by computing what

share the difference of column (6) and (3) represents of the difference of column (4) and (1). We find

that in Great Britain about 30% of the increased dispersion in the variance of wages can be acribed

to performance pay, while for Germany this number is about 20%. Yet, when considering the

percentile changes, there is a stark difference in experiences between Great Britain and Germany.

In Great Britain, the overall contribution in the 90-10 percentile log wage gap, or the difference

between column (6) and column (3), can be largely attributed to changes in the upper half of the

wage distribution. This is where the returns to education have increased substantially over this

time period and therefore consistent with the model proposed by LPM. In contrast, the overall

contribution in the 90-10 percentile log wage gap in Germany is largely explained by the lower half

of the wage distribution. Since returns to education largely increased in the upper half of the wage

distribution, this finding is somewhat at odds with the model proposed by LPM. Therefore, these

decomposition results are consistent with the model of performance pay and wage inequality doing

a reasonably good job for the British data, while doing a poorer job in Germany.

5.2 RIF decompositions.

The results above imply that increasing returns to education play a role in the increased incidence

of performance pay in Great Britain, while the results for Germany are somewhat moot. For Great

Britain, we would then expect that the increase in percentile or variance gaps is driven by changes in

returns rather than in composition. This can be explored by rewriting the aggregate decomposition

20Changing these periods alters the reweighting errors as the reweighting factor is consistent for large samples, butthe results are robust to using different time periods.

19

as

4vO = v(FY1|PP=1)− v(FY0|PP=1)︸ ︷︷ ︸

4vS

+ v(FY0|PP=1)− v(FY0|PP=0)︸ ︷︷ ︸4v

X

where 4vS is the wage structure effect and 4v

X is the composition effect. Given the property of

RIF that the expected value of the function equals the distributional statistic of interest and that

this expectation is linear in the covariates, it is possible to rewrite the wage structure effect and

the composition effect as (with the superscript c denoting counterfactual);

4vX = (XC

PP − XNPP ) · ωNPP,v + XCPP · (ωCPP,v − ωNPP,v) = 4v

X,p + 4v

X,e

4vS = XPP · (ωPP,v − ωCPP,v) + (XPP − XC

PP ) · ωCPP,v = 4v

S,p + 4v

S,e

It can be shown that the sum of these two equations equals the total difference, 4vO. Fur-

thermore, in both the equations the second term, 4v

X,e and 4v

S,e reflect error terms. Firpo et al.

[2011] define the first term as the “specification error” and the second term as the “total reweight-

ing error”. The reweighting error should go to zero in large samples as the reweighting factor is

estimated consistently; non-zero values reflect that the reweighting of the non-performance pay

sample does not result in a perfect counterfactual.21 The specification error results from assuming

the linear model. As in the previous subsection, observations are pooled over the same years to

minimize these errors.

The results are presented in table 11 for Great Britain and table 12 for Germany. For

both tables, the upper panel shows the results for the first period, while the lower panel shows

the results for the final period. The specification and reweighting errors are not presented. The

impact of performance pay can be assessed by comparing the upper panel to the lower panel. For

instance, the first two columns of table 11 highlight that the wage structure has become a more

important determinant of the 90-10 percentile log wage gap over time, as it largely explains the

wage gap in the final period. When considering columns (3) through (6), it is clear that this effect

is almost entirely explained by the upper half of the wage distribution. The gap in the lower part

of the distribution has narrowed, which is mostly explained by compositional effects, not wage

structure effects. The final two columns report the results for the variance and largely confirm

21Firpo [2010] shows that the reweighting method is efficient, as long as the reweighted functional is smooth.

20

this interpretation. This strengthens the hypothesis that performance pay has had an effect on

the dispersion of wages, largely driven by wage structure effects in the upper half of the wage

distribution. This is consistent with changes in the returns to skill and the model proposed by

LMP.

The results for Germany are less straightforward to interpret. The upper panel, presenting the

results for years 1985 through 1989, highlight that the wage structure effects are relatively similar

when comparing the actual distribution to the counterfactual distribution where everybody is paid

as workers in non performance pay jobs. The composition effects are also similar, with an exception

being the 90-50 percentile log wage gap. When considering the lower panel, though, the modest

unadjusted gaps mask substantial changes in the importance of composition and wage structure

effects. These changes, however, are not explained by performance pay. The gaps between the

changes (both compositional and wage structure) are minor when comparing the actual distribution

to the counterfactual distribution where all workers are not paid for performance. This highlights

that, while there have been important changes over time in Germany, the channel of performance

pay does not seem a major channel through which changes firms (or the labor market as a whole)

have dealt with changes in the returns to skill. One important implication is that, even though

countries such as the United States, Great Britain and Germany have witnessed similar trends in

wage inequality (see, for instance, Dustmann et al. [2009]), caution is advised when extrapolating

ways in which labor markets deal with changes in the return to skill. While performance pay seems

to have played a role in the United States and Great Britain to channel these changes, it has not

in Germany. Which channels have been important, remains an open question.

6 Conclusion.

In this paper I investigate the link between performance pay and wage inequality. To my knowledge,

there is currently no research evidence relating this link for Great Britain and Germany. I test the

model proposed by LMP on survey panel data in these countries, and investigate the effect of

misclassification on the empirical strategy proposed by LMP. I find that for certain regression

strategies, misclassification can lead to omitted variable bias, and that misclassification in general

decreases the likelihood of confirming the empirical predictions proposed by LMP. When adjusting

21

for these issues, I find that performance pay seems to play a similar role in Great Britain and the

United States, where it is used to channel changes in the returns to skills. While Germany has had

somewhat similar trends (see Dustmann et al. [2009]), performance pay does not play a similar

role in this labor market.

RIF decompositions, that allow me to decompose changes at various distributional statistics

of the wage distribution, show that performance pay in Great Britain can explain about 30% of

the increase in the dispersion of wages. As LMP point out, this is not a causal effect, but can be

interpreted as the additional increase in dispersion that was the result from workers and employers

switching to performance pay contracts. Consistent with the LMP model, most of this increase

is explained by changes in the top of the wage distribution , where the returns to education have

increased over this same period, and changes in the wage structure. For Germany, I find performance

pay jobs explain about 20% of the increase in the dispersion of wages. Nevertheless, this increase

is driven by changes in the bottom of the distribution, which does not lign up with the model of

LMP. Decomposing these results highlights that Germany has seen important changes in the wage

distribution, and that both compositional and wage structure effects are important. Nevertheless,

performance pay jobs explain very little of these changes.

Taken together, the results indicate the labor markets may deal with changes in the returns

to skill in different ways. The three countries considered in this study and LMP have witnessed

similar trends in wage inequality and changes in the returns to skills and education. Nevertheless,

the single model of performance pay and wage inequality does not seem to hold uniformly. In the

United States and Great Britain, performance pay seems to have been an important channel to

deal with these changes in the returns to skills and education. In Germany, on the other hand, it

has had very little impact, indicating firms and the labor market have channeled changes in the

returns to skill in different ways. How these changes have been channeled, and the importance of

different institutions, is not the subject of this paper, but possibly an interesting avenue for future

research.

22

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25

Tab

le1:

Su

mm

ary

stat

isti

cs

BHPS

SOEP

1991

–200

819

85–2

008

Non

-PP

job

sP

Pjo

bs

Non

-PP

job

sP

Pjo

bs

Hou

rly

earn

ings

8.23£

10.1

8£11

.61e

17.4

0e

(4.3

2)(6

.07)

(5.4

1)(8

.49)

Exp

erie

nce

(yea

rs)

21.9

920

.57

19.2

219

.01

(13.

04)

(11.

95)

(11.

36)

(10.

02)

Ten

ure

(yea

rs)

5.15

5.68

10.4

412

.61

(6.7

1)(6

.21)

(9.5

8)(9

.63)

Age

39.3

438

.45

40.4

741

.37

(11.

95)

(10.

83)

(10.

90)

(9.5

9)M

arri

ed0.

590.

600.

730.

76(0

.49)

(0.4

9)(0

.45)

(0.4

2)N

onw

hit

e0.

030.

020.

130.

03(0

.18)

(0.1

5)(0

.34)

(0.1

8)U

nio

niz

ed0.

210.

25—

—(0

.41)

(0.4

3)A

nnu

alh

ou

rsw

orke

d2,

341

2,36

12,

299

2,38

7(5

24)

(467

)(3

93)

(433

)

Work

ers

3,35

93,

714

7,77

32,

786

Job

matc

hes

5,15

06,

535

10,7

493,

066

Ob

serv

ati

on

s9,

726

18,5

9740

,287

20,3

10

Notes.

The

sam

ple

consi

sts

ofm

ale

sb

etw

een

20

and

65

yea

rsold

that

are

emplo

yed

inth

epri

vate

sect

or.

The

hourl

yw

ages

are

rest

rict

edb

etw

een

1and

100

pounds

or

euro

s.In

Gre

at

Bri

tain

,th

esa

mple

imp

ose

san

addit

ional

rest

rict

ion

on

the

annual

hours

work

edto

be

bet

wee

n1,0

00

and

4,5

00.

InG

erm

an,

the

sam

ple

imp

ose

san

addit

ional

rest

rict

ion

on

the

month

lyea

rnin

gs

tob

eab

ove

600e

.T

he

rep

ort

edfigure

sre

pre

sent

sam

ple

mea

ns.

Educa

tion

inth

ista

ble

isre

port

edin

appro

xim

ate

yea

req

uiv

ale

nts

.In

Gre

at

Bri

tain

,(p

ote

nti

al)

exp

erie

nce

isdefi

ned

as

age

min

us

educa

tion

min

us

6.

InG

erm

any,

exp

erie

nce

isav

ailable

as

vari

able

.

26

Table 2: Summary statistics: Education in Great Britain and Germany

BHPS SOEP

1991–2008 1985–2008

Non-PP jobs PP jobs Non-PP jobs PP jobsHigher degree 0.02 0.03 University 0.06 0.17

(0.14) (0.17) (0.24) (0.37)First degree 0.09 0.13 Technical 0.05 0.12

(0.29) (0.34) (0.22) (0.32)HND, HNC, training 0.08 0.09 Vocational 0.68 0.63

(0.27) (0.29) (0.47) (0.48)A level 0.22 0.26 None 0.21 0.08

(0.41) (0.44) (0.41) (0.28)O level 0.28 0.27

(0.45) (0.44)CSE 0.07 0.07

(0.26) (0.25)None 0.23 0.16

(0.42) (0.36)

Notes. The academic qualifications for Great Britain are declared as reported in the BHPS. The academicqualifcations for Germany are defined as follows. University is defined as university, technical college(TH) or college abroad; Technical is defined as technical college (Fachhochschule); Vocational is definedas apprenticeship, vocational school, health care school, civil service training, other training.

27

Table 3: Regression estimates of the effect of Performance Pay on earnings

Estimation method

OLS Fixed Effects(1) (2) (3) (4) (5)

BHPS

Performance Pay job 0.0895*** 0.0595*** 0.0257*** 0.0192*** —(0.0074) (0.0088) (0.0062) (0.0067)

Performance Pay received — 0.0449*** — 0.0105** 0.0087*

in current year (0.0069) (0.0044) (0.0046)Worker fixed effect No No Yes Yes YesJob-match fixed effect No No No No YesN 28,323 28,323 28,323 28,323 28,323Jobmatches 11,685 11,685 11,685 11,685 11,685

SOEP

Performance Pay job 0.1521*** 0.1214*** 0.0721*** 0.0639*** —(0.0064) (0.0067) (0.0082) (0.0083)

Performance Pay received — 0.0840*** — 0.0221*** 0.0213***

in current year (0.0067) (0.0036) (0.0035)Worker fixed effect No No Yes Yes YesJob-match fixed effect No No No No YesN 60,597 60,597 60,597 60,597 60,597Jobmatches 13,815 13,815 13,815 13,815 13,815

Notes. Standard errors (clustered at the jobmatch level) are reported in parentheses. Significancelevel are starred at 10% (*), 5% (**) and 1% (***). All specifications include a full set of industrydummies (10 for Great Britain and 17 for Germany), occupation dummies (9 for Great Britain and10 for Germany), year dummies (18 for Great Britain and 24 for Germany), regional dummies (19for Great Britain and 16 for Germany) a quadratic in experience and tenure, degree levels (as definedin the text), dummies for being married, for being nonwhite and for union status (in Great Britainonly). “Performance pay job” indicates whether performance pay had been received during the jobmatch, “performance pay received in current year” indicates whether performance pay had beenreceived that actual year.

28

Table 4: Skills-related wage differentials and Performance Pay jobs

BHPS BHPS adjusted SOEP1991–2008 1991–2008 1985–2008

PP jobs non-PP jobs PP jobs non-PP jobs PP jobs non-PP jobs(1) (2) (3) (4) (5) (6)

Constant 1.6647*** 1.6396*** 1.6899*** 1.6396*** 1.8237*** 1.4380***

(0.0830) (0.0590) (0.1762) (0.0590) (0.0810) (0.0704)Experience 0.0073*** 0.0056*** 0.0116*** 0.0056*** 0.0039*** 0.0027***

(0.0005) (0.0006) (0.0018) (0.0006) (0.0007) (0.0003)Tenure 0.0106*** 0.0103*** 0.0119*** 0.0103*** 0.0069*** 0.0078***

(0.0012) (0.0015) (0.0034) (0.0015) (0.0007) (0.0005)High Education 0.3995*** 0.3422*** 0.4569*** 0.3422*** 0.3221*** 0.2249***

(0.0205) (0.0250) (0.0480) (0.0250) (0.0234) (0.0197)Medium Education 0.3052*** 0.2226*** 0.3878*** 0.2226*** 0.2510*** 0.1534***

(0.0208) (0.0240) (0.0479) (0.0240) (0.0236) (0.0180)Low Education 0.1776*** 0.1608*** 0.1944*** 0.1608*** 0.0721*** 0.0571***

(0.0156) (0.0165) (0.0353) (0.0165) (0.0169) (0.0067)

N 18,597 9,716 18,597 9,716 20,310 40,287

Notes. Standard errors (clustered at the jobmatch level) are reported in parentheses. Significance level are starred at10% (*), 5% (**) and 1% (***). All specifications include a full set of industry dummies (10 for Great Britain and 17for Germany), occupation dummies (9 for Great Britain and 10 for Germany), year dummies (18 for Great Britainand 24 for Germany), regional dummies (19 for Great Britain and 16 for Germany) a quadratic in experience andtenure, degree levels (as defined in the text), dummies for being married, for being nonwhite and for union status (inGreat Britain only). The reported effects of experience and tenure are evaluated at 20 and 10 years respectively usingthe polynomial models imposed. All specifications are OLS (Ordinary Least Squares).

29

Table 5: Interaction models across the different survey panels

BHPS BHPS adjusted SOEP1991–2008 1991–2008 1985–2008

OLS OLS OLS(1) (2) (3)

PP job 0.0127 0.0084 0.0651***

(0.0260) (0.1290) (0.0214)Experience 0.0062*** 0.0219*** 0.0026***

(0.0005) (0.0043) (0.0003)Experience x PP job 0.0019** 0.0104 0.0042***

(0.0010) (0.0098) (0.0015)Tenure 0.0104*** 0.0189*** 0.0076***

(0.0014) (0.0063) (0.0004)Tenure x PP job 0.0012 0.0022 -0.0011

(0.0021) (0.0150) (0.0014)High Education 0.3065*** 0.3111*** 0.2236***

(0.0228) (0.0672) (0.0188)High x PP job 0.0801*** 0.1932 0.1085***

(0.0282) (0.1314) (0.0262)Medium Education 0.2025*** 0.2042*** 0.1498***

(0.0233) (0.0678) (0.0170)Medium x PP job 0.0813*** 0.2038 0.1058***

(0.0295) (0.1422) (0.0262)Low Education 0.1565*** 0.1644*** 0.0524***

(0.0163) (0.0577) (0.0067)Low x PP job 0.0250 0.0500 0.0229

(0.0219) (0.1064) (0.0173)

N 28,290 28,290 60,597

Notes. Standard errors (clustered at the jobmatch level) are reported in paren-theses. Significance level are starred at 10% (*), 5% (**) and 1% (***). Allspecifications include a full set of industry dummies (10 for Great Britain and 17for Germany), occupation dummies (9 for Great Britain and 10 for Germany),year dummies (18 for Great Britain and 24 for Germany), regional dummies (19for Great Britain and 16 for Germany) a quadratic in experience and tenure,degree levels (as defined in the text), dummies for being married, for being non-white and for union status (in Great Britain only). The specifications also includeinteractions between the performance-pay dummy and the educations levels (7 inGreat Britain and 3 in Germany), experience (quadratic) and tenure (quadratic).The reported effects of experience and tenure are evaluated at 20 and 10 yearsrespectively using the polynomial models imposed (both for the interacted andthe non-interacted variables). The acronyms OLS and FE stand for OrdinaryLeast Squares and fixed effects (worker fixed effects).

30

Table 6: Interaction models across the different survey panels including time effects

BHPS SOEP1991-2008 1985-2008

OLS OLS(1) (2)

PP job 0.0119 0.0746***

(0.0266) (0.0219)Experience 0.0060*** 0.0027***

(0.0005) (0.0003)Experience x PP job 0.0025** 0.0038**

(0.0010) (0.0015)Tenure 0.0104*** 0.0075***

(0.0014) (0.0004)Tenure x PP job -0.0001 -0.0011

(0.0021) (0.0014)High Education 0.4148*** 0.2418***

(0.0447) (0.0219)High x PP job 0.0159 0.1587**

(0.0552) (0.0728)High Education x 2006–2008 -0.1738*** 0.0520

(0.0597) (0.0648)High x PP job 0.1435** -0.0753x 2006–2008 (0.0689) (0.0776)Medium Education 0.2376*** 0.2280***

(0.0514) (0.0351)Medium x PP job 0.0120 0.0582

(0.0685) (0.0521)Medium Education x 2006–2008 -0.0391 -0.0405

(0.0683) (0.0449)Medium x PP job 0.0817 0.0598x 2006–2008 (0.0851) (0.0586)Low Education 0.1862*** 0.0647***

(0.0277) (0.0105)Low x PP job -0.0216 -0.0088

(0.0365) (0.0211)Low Education x 2006–2008 -0.0571 -0.0010

(0.0408) (0.0205)Low x Performance Pay job 0.0742* 0.0469**

x 2006–2008 (0.0450) (0.0182)

N 28,323 60,597

Notes. Standard errors (clustered at the jobmatch level) are reported in parentheses. Significance levelare starred at 10% (*), 5% (**) and 1% (***). All specifications include a full set of industry dummies (10for Great Britain and 17 for Germany), occupation dummies (9 for Great Britain and 10 for Germany),year dummies (18 for Great Britain and 24 for Germany), regional dummies (19 for Great Britain and 16for Germany) a quadratic in experience and tenure, degree levels (as defined in the text), dummies forbeing married, for being nonwhite and for union status (in Great Britain only). The specifications alsoinclude interactions between the performance-pay dummy and the educations levels (7 in Great Britainand 3 in Germany), experience (quadratic), tenure (quadratic) and a set of interaction variables betweena period dummy and the performance pay dummy and the different education levels. A period is definedas three years, so there are six periods for Great Britain and eight periods for Germany. The reportedeffects of experience and tenure are evaluated at 20 and 10 years respectively using the polynomial modelsimposed (both for the interacted and the non-interacted variables). The acronyms OLS and FE standfor Ordinary Least Squares and fixed effects (worker fixed effects).

Table 7: Variance component models by type of job

Performance Pay jobs Non-performance pay jobsVariance component (1) (2) (3) (4)

BHPS

Full sampleWorker 0.1001*** 0.0851*** 0.0785*** 0.0663***

(0.0030) (0.0031) (0.0030) (0.0033)Jobmatch — 0.0261*** — 0.0235***

(0.0012) (0.0021)Idiosyncratic 0.0541*** 0.0423*** 0.0648*** 0.0537***

error (0.0006) (0.0006) (0.0012) (0.0012)

Job-changers sampleWorker 0.0844*** 0.0707*** 0.0664*** 0.0540***

(0.0040) (0.0041) (0.0040) (0.0043)Jobmatch — 0.0240*** — 0.0266***

(0.0018) (0.0031)Idiosyncratic 0.0525*** 0.0419*** 0.0673*** 0.0540***

error (0.0009) (0.0008) (0.0018) (0.0017 )

SOEP

Full sampleWorker 0.0792*** 0.0492*** 0.0614*** 0.0350***

(0.0026) (0.0039) (0.0013) (0.0016)Jobmatch — 0.0327*** — 0.0343***

(0.0034) (0.0013)Idiosyncratic 0.0284*** 0.0267*** 0.0332*** 0.0269***

error (0.0003) (0.0003) (0.0003) (0.0002)

Job-changers sampleWorker 0.0686*** 0.0463*** 0.0690*** 0.0507***

(0.0044) (0.0062) (0.0049) (0.0054)Jobmatch — 0.0262*** — 0.0274***

(0.0050) (0.0033)Idiosyncratic 0.0276*** 0.0255*** 0.0351*** 0.0264***

error (0.0006) (0.0006) (0.0011) (0.0009)

Source PSID. Lemieux et al. (2009)

Notes for BHPS and SOEP. Standard errors (clustered at the jobmatch level) arereported in parentheses. Significance level are starred at 10% (*), 5% (**) and 1%(***).

32

Tab

le8:

Rob

ust

nes

sch

ecks

inB

HP

S

20%

PP

ormore

50%

PP

ormore

Alt.question

PP

Publicwork

ers

(1)

(2)

(3)

(4)

Per

form

ance

Pay

job

0.03

92**

0.04

71***

0.036

9***

0.0

616

***

(0.0

186)

(0.0

097)

(0.0

092

)(0

.023

4)E

xp

erie

nce

0.00

65***

0.00

64***

0.00

50***

0.00*

**

(0.0

005)

(0.0

005)

(0.0

005)

(0.0

00)

Exp

erie

nce

xP

erfo

rman

ceP

ayjo

b0.

0013

*0.

0015

**

0.002

5*0.

0(0

.000

8)(0

.000

7)(0

.0007

)(0

.00)

Ten

ure

0.01

06***

0.01

06***

0.01

19***

0.0*

**

(0.0

014)

(0.0

014)

(0.0

016

)(0

.00)

Ten

ure

xP

erfo

rman

ceP

ayjo

b0.

0011

0.00

12-0

.0002

-0.0

*

(0.0

021)

(0.0

021)

(0.0

024

)(0

.00)

Hig

h0.

3149

***

0.31

56***

0.31

03***

0.334

4***

(0.0

220)

(0.0

214)

(0.0

246

)(0

.017

7)H

igh

xP

erfo

rman

ceP

ayjo

b0.

0660

**

0.06

37***

0.07

87***

0.0

533

**

(0.0

261)

(0.0

244)

(0.0

274

)(0

.023

1)M

ediu

m0.

2094

***

0.20

95***

0.19

69***

0.231

7***

(0.0

228)

(0.0

225)

(0.0

260

)(0

.017

9)M

ediu

mx

Per

form

ance

Pay

job

0.06

85**

0.06

73**

0.09

60***

0.0

513

**

(0.0

261)

(0.0

373)

(0.0

313

)(0

.024

7)L

ow0.

1636

***

0.16

29***

0.15

77***

0.186

4***

(0.0

158)

(0.0

152)

(0.0

177

)(0

.014

9)L

owx

Per

form

an

ceP

ayjo

b0.

0120

0.01

270.

026

1-0

.005

6(0

.020

1)(0

.018

2)(0

.0209

)(0

.020

2)

N28

,290

28,2

9021,

103

36,6

80

Notes.

Sta

ndard

erro

rs(c

lust

ered

at

the

jobm

atc

hle

vel

)are

rep

ort

edin

pare

nth

eses

.Sig

nifi

cance

level

are

starr

edat

10%

(*),

5%

(**)

and

1%

(***).

All

spec

ifica

tions

incl

ude

afu

llse

tof

indust

rydum

mie

s(1

0),

occ

upati

on

dum

mie

s(9

),yea

rdum

mie

s(1

8),

regio

nal

dum

mie

s(1

9)

aquadra

tic

inex

per

ience

and

tenure

,deg

ree

level

s(a

sdefi

ned

inth

ete

xt)

,dum

mie

sfo

rb

eing

marr

ied,

for

bei

ng

nonw

hit

eand

for

unio

nst

atu

s.C

olu

mn

1(2

)te

sts

the

spec

ifica

tion

of

table

5,

but

defi

nes

the

dep

enden

tva

riable

as

hav

ing

rece

ived

per

form

ance

pay

at

least

one

out

of

five

(tw

o)

tim

esw

hen

obse

rved

on

asp

ecifi

cjo

bm

atc

h.

Colu

mn

3use

sa

subsa

mple

of

the

last

10

wav

esand

adiff

eren

tques

tion

todefi

ne

per

form

ance

pay

(see

text

for

det

ails)

.C

olu

mn

4ex

pands

the

sam

ple

by

incl

udin

gpublic

sect

or

work

ers.

All

spec

ifica

tions

are

esti

mate

dusi

ng

OL

S(O

rdin

ary

Lea

stSqaure

s).

33

Tab

le9:

Rob

ust

nes

sch

ecks

inS

OE

P

20%

PP

ormore

50%

PP

ormore

Publicsecto

rFemale

work

ers

(1)

(2)

(3)

(4)

Per

form

an

ceP

ayjo

b0.

0849

***

0.10

14***

0.09

90***

0.044

2***

(0.0

103)

(0.0

105)

(0.0

202

)(0

.0173

)E

xp

erie

nce

0.00

26***

0.00

25***

0.00

31***

0.003

0***

(0.0

003)

(0.0

003)

(0.0

003

)(0

.0003

)E

xp

erie

nce

xP

erfo

rman

ceP

ayjo

b0.

0048

***

0.00

59***

0.002

7*0.0

049

***

(0.0

012)

(0.0

012)

(0.0

014)

(0.0

012)

Ten

ure

0.00

76***

0.00

76***

0.00

78***

0.007

6***

(0.0

004)

(0.0

004)

(0.0

004

)(0

.0004

)T

enu

rex

Per

form

ance

Pay

job

-0.0

004

-0.0

003

-0.0

028

**

-0.0

003

(0.0

014)

(0.0

014)

(0.0

014

)(0

.0012

)H

igh

0.22

61***

0.22

55***

0.25

41***

0.229

8***

(0.0

187)

(0.0

187)

(0.0

138

)(0

.0153

)H

igh

xP

erfo

rman

ceP

ayjo

b0.

0950

***

0.09

38***

0.07

49***

0.108

8***

(0.0

236)

(0.0

236)

(0.0

224

)(0

.0218

)M

ediu

m0.

1522

***

0.15

13***

0.14

58***

0.142

0***

(0.0

169)

(0.0

169)

(0.0

138

)(0

.0140

)M

ediu

mx

Per

form

an

ceP

ayjo

b0.

0929

***

0.09

99***

0.10

10***

0.101

2***

(0.0

237)

(0.0

232)

(0.0

238

)(0

.0225

)L

ow0.

0518

***

0.05

01***

0.05

04***

0.057

7***

(0.0

066)

(0.0

066)

(0.0

063

)(0

.0058

)L

owx

Per

form

ance

Pay

job

0.02

45*

0.03

27**

0.0

171

0.0

325

**

(0.0

136)

(0.0

132)

(0.0

166)

(0.0

142

)

N60

,597

60,5

9779

,059

84,4

01

Notes.

Sta

ndard

erro

rs(c

lust

ered

at

the

jobm

atc

hle

vel

)are

rep

ort

edin

pare

nth

eses

.Sig

nifi

cance

level

are

starr

edat

10%

(*),

5%

(**)

and

1%

(***).

All

spec

ifica

tions

incl

ude

afu

llse

tof

indust

rydum

mie

s(1

7),

occ

upati

on

dum

mie

s(1

0),

yea

rdum

mie

s(2

4),

regio

nal

dum

mie

s(1

6)

aquadra

tic

inex

per

ience

and

tenure

,deg

ree

level

s(a

sdefi

ned

inth

ete

xt)

,dum

mie

sfo

rb

eing

marr

ied

and

for

bei

ng

nonw

hit

e.C

olu

mn

1(2

)te

sts

the

spec

ifica

tion

of

table

5,

but

defi

nes

the

dep

enden

tva

riable

as

hav

ing

rece

ived

per

form

ance

pay

at

least

one

out

of

five

(tw

o)

tim

esw

hen

obse

rved

on

asp

ecifi

cjo

bm

atc

h.

Colu

mn

3and

4ex

pand

the

sam

ple

by

incl

udin

gpublic

sect

or

work

ers

and

fem

ale

(pri

vate

sect

or)

work

ers

resp

ecti

vel

y.A

llsp

ecifi

cati

ons

are

esti

mate

dusi

ng

OL

S(O

rdin

ary

Lea

stSqaure

s)

34

Tab

le10:

Eff

ect

ofp

erfo

rman

cep

ayjo

bs

onm

easu

res

ofw

age

ineq

ual

ity

BHPS

1991–1993

2006–2008

Act

ual

Cou

nte

rfac

tual

Eff

ect

ofA

ctu

alC

ounte

rfac

tual

Eff

ect

of%

Exp

lain

edd

isp

ersi

ond

isp

ersi

onP

Pjo

bs

dis

per

sion

dis

per

sion

PP

job

sby

PP

job

s(1

)(2

)(3

)(4

)(5

)(6

)(7

)

Vari

an

ce0.

2259

0.20

630.

0196

0.25

530.

2270

0.02

83(0

.008

2)

(0.0

100)

(0.0

129)

(0.0

100)

(0.0

126)

(0.0

161)

Per

centi

legap

s90-

101.1

875

1.15

520.

0323

1.23

331.

1520

0.08

13(0

.021

2)

(0.0

356)

(0.0

415)

(0.0

254)

(0.0

348)

(0.0

431)

50-

100.5

746

0.56

690.

0077

0.57

620.

5786

-0.0

024

(0.0

157)

(0.0

257)

(0.0

301)

(0.0

174)

(0.0

273)

(0.0

323)

90-

500.6

129

0.58

830.

0246

0.65

710.

5734

0.08

37(0

.018

1)

(0.0

254)

(0.0

312)

(0.0

186)

(0.0

242)

(0.0

305)

G-S

OEP

1985–1989

2004–2008

Act

ual

Cou

nte

rfac

tual

Eff

ect

ofA

ctu

alC

ounte

rfac

tual

Eff

ect

ofd

isp

ersi

ond

isp

ersi

onP

Pjo

bs

dis

per

sion

dis

per

sion

PP

job

s(1

)(2

)(3

)(4

)(5

)(6

)

Vari

an

ce0.

1225

0.09

090.

0316

0.22

480.

1810

0.04

38(0

.005

0)

(0.0

057)

(0.0

076)

(0.0

073)

(0.0

093)

(0.0

118)

Per

centi

legap

s90-

100.8

596

0.71

670.

1429

1.22

481.

0536

0.17

12(0

.020

6)

(0.0

268)

(0.0

338)

(0.0

232)

(0.0

323)

(0.0

398)

50-

100.3

742

0.34

220.

0320

0.60

580.

5504

0.05

54(0

.012

2)

(0.0

155)

(0.0

197)

(0.0

191)

(0.0

221)

(0.0

292)

90-

500.4

855

0.37

450.

1110

0.61

900.

5032

0.11

58(0

.017

8)

(0.0

232)

(0.0

293)

(0.0

190)

(0.0

262)

(0.0

295)

Rew

eighte

ddis

trib

uti

ons

usi

ng

the

DF

Lappro

ach

.T

he

pro

pen

sity

score

use

sa

ver

yflex

ible

model

.C

on-

trols

incl

ude

educa

tion,

aquadra

tic

poly

nom

ial

inex

per

ience

and

tenure

;a

whit

e/nonw

hit

eand

mar-

ried

/si

ngle

dum

my;

occ

upati

on,

indust

ry,

yea

rand

regio

nfixed

effec

ts;

and

inte

ract

ions

bet

wee

nsc

hooling

and

exp

erie

nce

.B

oots

trapp

edst

andard

erro

rsare

obta

ined

by

runnin

g1,0

00

iter

ati

ons

usi

ng

bala

nce

dsa

mple

sof

1,0

00

per

form

ance

and

non-p

erfo

rmance

pay

jobs

each

(i.e

.su

bsa

mple

sco

nta

in2,0

00

obse

rva-

tions)

.

35

Tab

le11

:D

ecom

pos

itio

nre

sult

s:B

HP

S

90–10logwagegap

90–50logwagegap

50–10logwagegap

Variance

Act

ual

Rew

eigh

ted

Act

ual

Rew

eigh

ted

Act

ual

Rew

eigh

ted

Act

ual

Rew

eigh

ted

1991–1993

Un

adju

sted

Ch

ange

0.0

872

0.08

720.

0548

0.05

480.

0324

0.03

240.

0369

0.03

69(0

.0558

)(0

.055

8)(0

.045

4)(0

.045

4)(0

.035

5)(0

.035

5)(0

.016

2)(0

.016

2)

Com

posi

tion

effec

t0.0

402

0.04

380.

0029

0.00

240.

0373

0.04

130.

0120

0.01

50(0

.0308

)(0

.050

9)(0

.027

4)(0

.044

2)(0

.021

2)(0

.034

3)(0

.009

4)(0

.015

9)W

age

Str

uct

ure

0.04

71

0.04

960.

0519

0.03

01-0

.004

90.

0194

0.02

500.

0094

(0.0

575

)(0

.073

1)(0

.048

7)(0

.065

3)(0

.040

1)(0

.048

9)(0

.016

7)(0

.021

5)

2006–2008

Un

adju

sted

Ch

ange

0.0

604

0.06

040.

0862

0.08

62-0

.025

8-0

.025

80.

0243

0.02

43(0

.0457

)(0

.045

7)(0

.033

9)(0

.033

9)(0

.037

0)(0

.037

0)(0

.018

4)(0

.018

4)

Com

posi

tion

effec

t0.0

046

-0.0

176

0.00

240.

0016

0.00

22-0

.019

30.

0066

0.00

40(0

.0380

)(0

.133

5)(0

.029

6)(0

.081

0)(0

.030

5)(0

.092

7)(0

.013

1)(0

.039

0)W

age

Str

uct

ure

0.05

88

0.09

630.

0838

0.00

66-0

.027

90.

0168

0.01

770.

0269

(0.0

574

)(0

.264

0)(0

.033

9)(0

.023

0)(0

.051

0)(0

.138

0)(0

.021

5)(0

.061

4)

Rew

eighte

ddis

trib

uti

ons

usi

ng

the

DF

Lappro

ach

.T

he

pro

pen

sity

score

use

sa

ver

yflex

ible

model

.C

ontr

ols

incl

ude

educa

tion,

aquadra

tic

poly

nom

ial

inex

per

ience

and

tenure

;a

whit

e/nonw

hit

eand

marr

ied/si

ngle

dum

my;

occ

upati

on,

indust

ry,

yea

rand

regio

nfixed

effec

ts;

and

inte

ract

ions

bet

wee

nsc

hooling

and

exp

erie

nce

.R

ecen

tere

dIn

fluen

ceR

egre

ssio

ndec

om

posi

tions

dec

om

pose

educa

tion,

aquadra

tic

poly

nom

ial

inex

per

ience

and

tenure

;a

whit

e/nonw

hit

eand

marr

ied/si

ngle

dum

my;

occ

upati

on,

indust

ry,

yea

rand

regio

nfixed

effec

ts.

Boots

trapp

edst

andard

erro

rsare

obta

ined

by

runnin

g1,0

00

iter

ati

ons

usi

ng

bala

nce

dsa

mple

sof

1,0

00

per

form

ance

and

non-p

erfo

rmance

pay

jobs

each

(i.e

.su

bsa

mple

sco

nta

in2,0

00

obse

rvati

ons)

.

36

Tab

le12

:D

ecom

pos

itio

nre

sult

s:S

OE

P

90–10logwagegap

90–50logwagegap

50–10logwagegap

Variance

Act

ual

Rew

eigh

ted

Act

ual

Rew

eigh

ted

Act

ual

Rew

eigh

ted

Act

ual

Rew

eigh

ted

1985–1989

Un

adju

sted

Ch

ange

0.1

452

0.14

520.

1290

0.12

900.

0162

0.01

620.

0302

0.03

02(0

.0382

)(0

.038

2)(0

.030

4)(0

.030

4)(0

.025

6)(0

.025

6)(0

.010

1)(0

.010

1)

Com

posi

tion

effec

t0.0

731

0.10

260.

0082

0.03

550.

0649

0.06

720.

0245

0.03

22(0

.0333

)(0

.087

6)(0

.029

9)(0

.068

1)(0

.025

7)(0

.068

9)(0

.010

1)(0

.023

8)W

age

Str

uct

ure

0.07

21

0.06

960.

1208

0.10

29-0

.048

7-0

.033

30.

0057

0.00

69(0

.0487

)(0

.127

3)(0

.039

3)(0

.105

8)(0

.037

5)(0

.090

8)(0

.011

0)(0

.027

2)

2004–2008

Un

adju

sted

Ch

ange

0.0

331

0.03

310.

0143

0.01

430.

0188

0.01

880.

0066

0.00

66(0

.0000

)(0

.050

7)(0

.039

9)(0

.039

9)(0

.037

0)(0

.037

0)(0

.014

2)(0

.014

2)

Com

posi

tion

effec

t-0

.1513

-0.1

544

-0.1

633

-0.1

635

0.01

200.

0091

-0.0

335

-0.0

353

(0.0

559

)(0

.141

6)(0

.031

2)(0

.059

6)(0

.053

4)(0

.121

5)(0

.013

5)(0

.034

0)W

age

Str

uct

ure

0.18

44

0.18

630.

1776

0.14

550.

0068

0.04

080.

0401

0.04

79(0

.0782

)(0

.152

0)(0

.047

7)(0

.090

7)(0

.070

9)(0

.126

6)(0

.019

4)(0

.036

1)

Rew

eighte

ddis

trib

uti

ons

usi

ng

the

DF

Lappro

ach

.T

he

pro

pen

sity

score

use

sa

ver

yflex

ible

model

.C

ontr

ols

incl

ude

educa

tion,

aquadra

tic

poly

nom

ial

inex

per

ience

and

tenure

;a

whit

e/nonw

hit

eand

marr

ied/si

ngle

dum

my;

occ

upati

on,

indust

ry,

yea

rand

regio

nfixed

effec

ts;

and

inte

ract

ions

bet

wee

nsc

hooling

and

exp

erie

nce

.R

ecen

tere

dIn

fluen

ceR

egre

ssio

ndec

om

posi

tions

dec

om

pose

educa

tion,

aquadra

tic

poly

nom

ial

inex

per

ience

and

tenure

;a

whit

e/nonw

hit

eand

marr

ied/si

ngle

dum

my;

occ

upati

on,

indust

ry,

yea

rand

regio

nfixed

effec

ts.

Boots

trapp

edta

ndard

erro

rsare

obta

ined

by

runnin

g500

iter

ati

ons

usi

ng

bala

nce

dsa

mple

sof

1,0

00

per

form

ance

and

non-p

erfo

rmance

pay

jobs

each

(i.e

.su

bsa

mple

sco

nta

in2,0

00

obse

rvati

ons)

.

37

Figure 1: Incidence of Performance Pay

(a) BHPS 1991–2008 (b) SOEP 1985–2010

(c) BHPS 1991–2008 (d) SOEP 1985–2010

38

Figure 2: Wage Inequality in Great Britain and Germany

(a) Distribution of Wages (Great Britain) (b) Distribution of Wages (Germany)

(c) St.Dev. log hourly wages (Great Britain) (d) St.Dev. log hourly wages (Germany)

39

Figure 3: Reweighting procedure: Kernel densities

(a) BHPS 1991–1993 (b) SOEP 1985–1989

(c) BHPS 2003–2008 (d) SOEP 2004–2008

40

Figure 4: Quantile RIF regressions: BHPS

(a) High (First or Higher Degree) (b) Medium (HND, HNC or Teaching)

(c) Low (A Level) (d) O Level

41

Figure 5: Quantile RIF regressions: SOEP

(a) High (University) (b) Medium (Technical)

(c) Low (Vocational)

42