Transcript DANISH NEIGHBOURS AS NEGATIVES

Job Quality and Effort
Andrew E. Clark (Paris School of Economics and IZA)
http://www.parisschoolofeconomics.com/clark-andrew/
APE/ETE Masters Course
THE BROADEST OF BROAD QUESTIONS
“Have jobs been getting worse?”
or
“Has job quality declined since the (mythical) golden
age of the 1960s and 1970s?”
Nostalgia is a wonderful thing. But it is our duty to look
at the facts, and then try to bring economic analysis
to bear on them.
So what has happened?
1) There are now more jobs (or at least up until
recently…)
Unemployment rates mostly fell in OECD
countries.
Something of an “Anglo-Saxon” phenomenon.
2) The characteristics of these jobs would
broadly seem to be better than in the past.
Which characteristics?
a) Wages have increased in almost all countries.
One major exception is the US. Over the
1985-’95 period, real labour income in the
first decile fell. But so did real labour income
in the fifth decile. Median US real wages
have barely changed over a 25-year period.
b) There was rising earnings inequality, as
measured by D9/D1. This will reduce utility
at a given level of mean income.
OECD Inequality Figures
OECD Inequality Figures
c) Hours of work are trending inexorably
downwards.
c) Hours of work are trending inexorably
downwards.
d) But did jobs become less secure? Five-year
retention rates fell sharply 1980-’95 in Finland,
France and Spain. No strong movement elsewhere.
Although we should note that:
i.
RR is not the only important characteristic,
the consequences of job loss need to be taken
into account (chances of finding another job,
unemployment benefits). The advantages of
flexicurity.
ii. Movement between jobs might allow better
matches.
Subjective evidence on job security from three waves of the ISSP
Overall, good news
might outweigh the
bad.
Unfortunately, work on
the time series of job
satisfaction – workers’
evaluations of their
own jobs – has shown
that this dropped
sharply from the 1980s
and 1990s into the
2000s. An exception is
the US.
What’s gone wrong? One idea here is that it
is what individuals actually do when
they are at work: “job content”. This
captures how hard they work, danger,
interest etc.
I will mostly concentrate on worker effort.
There is a small literature on accidents at work.
Workplace accidents are found to be
i)
Higher (a little) for temporary rather than
permanent workers.
ii) Unrelated to hours of work.
iii) Lower in unionised workplaces.
There is also a more aggregate/macro literature
that has looked at time series movements in
accidents – see Askenazy’s book.
The health-related consequences of work have
worsened in Europe between 1990 and 2000
The US was on the
same trajectory until
the early 1990s
Since 1990 the
number of accidents
and work-related
illnesses have dropped
by 1/3.
Why have the French and American experiences
been so different in recent years?
1) Americans take worker health seriously
(Ergonomics and training have long-run
productivity payoffs).
2) Government and unions take an aggressive stance on
workplace safety. Information on safety violations
made public. So workers won’t work there, or will
ask for higher wages, and insurance premia
(private) rise.
The latter rose from 1.4% of labour costs in 1985 to
2.4% in 1994. Dropped back to 1.6% in 2001.
In France the number of Inspecteurs de Travail has
fallen. The results of investigations are not made
public. There is thus less incentive to make
workplaces safer (insurance is mutual, so we have
the problem of the commons).
Worker Effort
We tend to write production functions as
Q=Q(N,K).
We should probably write Q=Q(Nh,K), or better
Q=Q(N,h,K), as workers and hours aren’t
perfect substitutes.
Even better, let’s write Q=Q(N,h,e,K), where e
shows the level of effort furnished by
workers per hour of work. Firm’s profit rises
with e; worker utility falls with e.
Effort is not contractable: we are in the world of
incentives
Could falling job quality be caused by greater worker
effort?
One way of looking at this is to trace out movements in
overall job satisfaction, and then decompose them.
See regression in handout, using BHPS data from 19922002. This shows two regressions:
Pooled: each observation treated as if it represented a
different person; presents a snapshot of average job
quality in each year.
Panel: Follows the same individual from one year to
another; picks out within subject changes in job
quality.
These regressions include “standard” controls:
age, sex, education, marital status etc.
They also control for job characteristics: region,
occupation, industry and firm size.
They also include a full set of year dummies
(1992 is the omitted category). These plot the
conditional movements in overall job
quality.
This falls pretty much monotonically, both in
pooled and in panel regressions.
Table 6. Overall Job Satisfaction Regressions. BHPS 1992-2002.
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
Male
Age
Age-Squared/100
High Education
Medium Education
Separated
Divorced
Widowed
Single
Pooled
Panel
-0.079*
(0.033)
-0.127**
(0.033)
-0.142**
(0.033)
-0.116**
(0.032)
-0.067*
(0.032)
-0.171**
(0.032)
-0.204**
(0.031)
-0.206**
(0.031)
-0.169**
(0.031)
-0.200**
(0.032)
-0.208**
(0.010)
-0.061**
(0.003)
0.079**
(0.004)
-0.208**
(0.014)
-0.153**
(0.013)
0.005
(0.031)
-0.040*
(0.017)
0.056
(0.040)
-0.116**
(0.014)
-0.165**
(0.042)
-0.241**
(0.054)
-0.256**
(0.069)
-0.231**
(0.085)
-0.195
(0.103)
-0.278*
(0.121)
-0.355*
(0.139)
-0.333*
(0.157)
-0.307
(0.178)
-0.362
(0.197)
-0.009
(0.021)
0.014
(0.010)
0.121*
(0.047)
0.028
(0.039)
0.099
(0.103)
-0.094**
(0.033)
Overall Job Satisfaction
Regression Coefficient
0.000
-0.050
-0.100
-0.150
-0.200
-0.250
-0.300
-0.350
-0.400
1992
1993
1994 1995
1996
1997
1998
Year
Pooled
Panel
1999 2000
2001
2002
Satisfaction: Pay
0.200
Regression Coefficient
0.150
0.100
0.050
0.000
-0.050
-0.100
-0.150
-0.200
-0.250
1992
1993
1994
1995
1996
1997 1998
Year
Pooled
Panel
1999
2000
2001
2002
Satisfaction: Hours of Work
Regression Coefficient
0.050
0.000
-0.050
-0.100
-0.150
-0.200
1992
1993
1994
1995
1996
1997
1998
Year
Pooled
Panel
1999
2000
2001
2002
Satisfaction: Job Security
0.250
Regression Coefficient
0.200
0.150
0.100
0.050
0.000
-0.050
-0.100
-0.150
-0.200
-0.250
1992
1993
1994
1995
1996
1997
1998
Year
Pooled
Panel
1999
2000
2001
2002
Regression Coefficient
Satisfaction: Work Itself
0.050
0.000
-0.050
-0.100
-0.150
-0.200
-0.250
-0.300
-0.350
-0.400
-0.450
-0.500
1992
1993
1994
1995
1996
1997
1998
Year
Pooled
Panel
1999
2000
2001
2002
Worse job content from greater effort?
In an efficiency-wage framework, effort rises
due to:
- Higher wages (but higher wages raise utility)
- Higher unemployment (but endogenous….)
- Falling cost of monitoring
- Falling cost of firing shirking workers
- Greater cost of shirking for workers
I concentrate on the last three.
Employment protection and effort
Consider absenteeism as an indicator of employee
effort.
You can be absent because you’re sick, or because you
shirk (“pulling a sickie”).
 Most popular sick days are Monday and Friday
 Sick days correlated with holidays and sporting
events
 Public-sector sick rates are 44% higher than in the
private sector (selection of worse health to public
sector?).
Effect of a probationary period before permanent job:
Ichino and Riphahn (2005).
There are three states (exogenous)
Sick (S)

Lazy (L)

Healthy and willing to work
1--
The worker decides whether to be absent or not (A=1 or A=0).
A=0
The payoff is the continuation value (asset value) of the job minus the disutility of work. This
disutility depends on whether the worker is sick or lazy).
(A=0 | U=L)
=
W – VL
(1)
(A=0 | U=S)
=
W – VS
(2)
A=1
Firms can decide to check on the worker’s health status.
If check and U=L then the worker is fired and the payoff, , is zero.
If don’t check and U=L then the payoff is W.
P(check) = q
Thus:
(A=1 | U=L)
=
W(1-q)
(3)
(A=1 | U=S)
=
W
(4)
The subtlety here is that q won’t be set high enough
to drive L down to zero, as the firm wants to identify shirkers
(before the end of the probationary period).
Analogously, P(A) when sick, comparing (2) and (4) is S =1.
The overall probability of absence is thus
 = (1 – FL(Wq)) + 
The number of monitored absences is qK, so that the number of identified lazy workers is
B = qN  (1 – FL(Wq)) = qN  L(q)
The firm wants to maximise B by choosing q:
dB/dq = 0 implies that
 q d L
= 1, which defines optimal probationary monitoring, q*.
 L dq
After probation, there is no monitoring (q=0). If the probationary period is called period 0, we
thus have the final (obvious) result that K0 < K1.
In normal efficiency-wage theory, we can’t perfectly
monitor worker effort, and set wages high enough
to maximise profit
(the cost of higher wages is offset by workers’
greater effort due to the higher wages).
Here we don’t want to discourage shirking, but
rather identify the maximum number of shirkers in
the first period, so that we can costlessly sack
them before they receive tenure.
The cost of monitoring is zero in this model.
Test the model on Italian data.
There is a probationary period with little protection
(three months), followed by a sudden jump to a
great deal of employment protection.
Data from a large Italian Bank (18 000 employees).
Information on 545 men and 313 women hired into
white-collar positions (Jan. 1993 to Feb. 1995).
Observed over a full year following hiring.
The workers here are a fairly homogeneous group:
young, with high-school education.
They calculate the number of days absent because
“ill” per week (so that they have 52 x 858
observations).
AF(9%) < AM (5%): for family reasons?
But with a notable jump after 12 weeks of
employment (end of probation).
Jacob. Journal of Labor Economics. October 2013
*2004 CB agreement between Chicago Public Schools
and Chicago Teachers Union
*Gives Principals the flexibility to dismiss teachers with
less than 5 years experience without cause
*Previously could dismiss teachers for enrollment or
budgetary reasons (via LIFO); otherwise very difficult
and time-consuming.
*Annual teacher absence fell by 10%; frequent absence
by 25%
*Effect mostly between, but some evidence of a small
within effect also.
Fixed and Variable Wages
Does effort always rise with wages? It may depend on how wages
are received.
Traditional efficiency wage is e = e(w): effort rises with wages
Labour income might consist of a fixed and variable component. Y
= a + bX, where b is the piece rate for some output X.
Expect effort to rise with b, but what about a?
Mocan and Altindag. Economic Journal. December 2013
Evidence from a group we all know and love: MEPs.
Prior to July 2009, MEP salaries were determined by
their home country: substantial variation across countries.
For example, the salary of a MEP from Poland was
€29,043, whereas the salary of a member from Italy was
€142,512.
Starting with the seventh term in the summer of 2009,
MEP salaries were equalised to €91,983 and then
increased slightly in each subsequent year.
This produced a large exogenous change in non-labour
income for most MEPs.
Other salary elements:
Some MEPs live in their home country and receive travel
expenses.
MEPs also receive allowances for their expenses related
to costs of running their offices.
Each parliamentarian receives a per diem compensation
for each day they attend the parliamentary sessions. This
per diem pay, which was €262 in 2004, was increased
each year and went up to €304 in 2011.
The base salary and per diem are our a and b respectively
The measure of effort here is attending the meeting days
(for example, there were 63 European Parliament meeting
days in 2008).
Examine the attendance record of each MEP (NB. this
within analysis avoids any problem of selection: higher
salaries encouraging less intrinsically-motivated MEPs).
Higher base salary reduces the marginal utility of income,
and may then reduce effort (makes delta income less
valuable compared to delta leisure).
Salary losers from the reform were Italy, Austria and Ireland.
Post-reform difference not zero because these are PPP figures.
Red line shows delta salary between losers and winners; blue
line shows delta attendance between the same two groups
Effort falls with real
salary; no correlation
with per diem (which is
positive in some other
specifications though).
Herfindahl index shows
the extent of competition
faced by MEPs in their
home country (by the
share of votes cast for
each party in the
country’s EU elections).
Does Monitoring Work?
Nagin et al., AER (2002).
Employees are “rational cheaters”, and shirk more as
monitoring falls.
The threat here is dismissal from the job.
Experimental approach. Call-centre operators at 16
sites, who are soliciting donations by ‘phone.
They are paid on a piece-rate plus a base salary: pay
rises with no. successful solicitations (people who are
rung and then say that they will donate).
This number of successful solicitations is self-reported.
Solicitor i rings ten numbers:
No.
1
2
3
4
5
6
7
8
9
10
Self-report
No
Yes
No
No
No
Yes
No
Yes
No
Yes
Some time later (weeks, months), some of these pledges
are actually received. The receipt of money cannot be
linked to information on solicitor and telephone
numbers rung. Say that only 75% of self-reported
pledges are received. We cannot know whether any
one individual cheated by reporting “too many”
successful solicitations.
Check opportunistic behaviour via callback –ring back
some of the numbers above (2, 6, 8, 10) which were
reported as positives. Not all of them, as callback is
expensive.
If the pledge is repudiated, logged as a “bad call”.
Note some bad calls may actually not be cheating, if the
person called has changed their mind.
Operators who “cheat” have pay docked, and may be
fired.
The employer varied the fraction of bad calls that
were reported back to employees and
supervisors (the observable monitoring rate) in
four of the 16 sites.
At the same time, the actual monitoring rate was
increased from 10% to 25% at these four sites.
But the number of bad calls reported back to
employees and supervisors was “as if” the
monitoring rate were 0%, 2%, 5% or 10%.
There was variation both across site and (withinsite) over time in these rates.
As EW theory would predict, the number of “bad calls”
responds to the call-back rate. The greater was the
observed monitoring rate last week, the fewer bad
calls were made this week.
Heterogeneity in worker response. Those with “positive
attitudes” respond less to monitoring.
And attitudes are shown to be function of y*, estimated
from a wage equation: the more others like me earn,
the less positive are my attitudes, and the more
responsive I am to opportunities to cheat.
McVicar, Labour Economics (2008).
Considers job-search effort by the unemployed,
rather than work effort by the employed.
Quasi-experimental: random variation due to the
refurbishment of Benefit Offices in Northern
Ireland.
The unemployed used to look for jobs at Job
Centres, and draw benefits at Benefits Offices.
Benefits were received conditional on evidence
of job search (“Job Seeker’s Agreement).
Efficiency programme combines “jobs and
benefits”, and required the refurbishment of
Benefit Offices.
These were in turn shut for refurbishment
periods, leading to the suspension of
fortnightly monitoring interviews (no
substitute monitoring).
Subsequent periods of zero monitoring of the
unemployed were associated with a 16% fall in
all exits from unemployment.
This effect particularly strong for exits to
employment: consistent with lower job-search
effort.
Temporary Jobs and Work Effort.
Temporary employment is on the rise: stepping
stones to good jobs.
So temporary workers have a greater incentive to
supply effort (the rewards are greater).
Engellandt and Riphahn, Labour Economics,
2005.
Swiss LFS data.
Effort measured by absenteeism and unpaid OT.
Definition of absenteeism pretty stringent:
missed the week prior to the interview
(Yes/No).
Engellandt and Riphahn observe that P(Temp  Perm)
positively correlated with worker effort when
Temporary.
Workers are assumed to prefer permanent to temporary
jobs.
Absence
UOT
Perm
1.2
20.6
Temp
0.8
27.7
Extensions (to standard EW)
What about the workers, who have been pretty mute so far?
Think of a potential role for unions: effort might be bargained
over.
Clark and Tomlinson (2001).
Data from Employment in Britain, 1992.
Measure discretionary effort:
“How much effort do you put into your job, beyond what is
required”?
Immodest replies (N=2700):
Effort
None
Little
Some
Lot
%
3
6
23
68
Regression for Effort
Econometrics shows that effort rises with:
a) Wage
b) Liking hard work (slope of IC)
c) Ease of dismissal
d) Performance pay
Effort falls with
f) Male
g) Unions
These are multivariate results, so the union effect is conditional
on wages.
The Psychology of Effort
Any role for income comparisons: e = e(y/y*)?
I feel hard done by (relatively) by my firm, so I provide less effort.
Clark, Masclet and Villeval (2010)
Survey data from the 1997 ISSP on discretionary effort;
and a gift-exchange game in the laboratory.
Main Results:
1) Field and Experimental produce the same results
2) e = e(y/y*) indeed
3) Rank matters more than ratio (comparisons are ordinal)
4) The more I earned in the past, the less hard I work today for any
given wage (habituation).
Our questions
Q1 : Does worker’s effort depend on how much other workers earn?
Q1’: Does it depend on their rank in the distribution of income?
ei=e(yi, y*,…)
+ Little evidence of the influence of others’ incomes on effort (Charness and
Kuhn, 2005; Güth et al. 2001; Gächter and Thoeni, 2005): wage
compression despite a weak effect of others’ incomes on agents’ behavior
Q2: Are comparisons horizontal (to others) or also vertical (intertemporal; to oneself in the past)?
2. Empirical strategy
Joint use of a lab experiment based on a gift-exchange game and
survey data from the 1997 International Social Survey Program
Experimental data: A direct measure of the willingness to contribute
Better control of the reference group
Survey data: Questions related to the willingness to exert effort
Large sample size with employed people
Possibility of cross-country comparisons
Offers a potential check of the external validity of experimental data
Still unusual (Fehr et al. 03; Brown et al. 05, Carpenter and Seki 05,
Cummings et al. 05)
A lab experiment with between-firm comparisons
Benchmark Treatment: Gift-Exchange Game
N=20 subjects, with 10 firms and 10 a priori similar employees
Stage 1: After being randomly matched with an employee, the firm offers
w  20,21,...,120
a contract
Stage 2: The employee accepts or rejects
In case of rejection, both earn 0
In case of an acceptance, choice of level of effort
ei   0.1,0.2,...,1
Convex cost function
Effort e 0.1
Cost c(e) 0
0.2
1
0.3
2
0.4
4
0.5
6
0.6
8
0.7
10
0.8
12
0.9
15
1
18
Firm’s payoff:
 iF  v  we
Employee’s payoff:
with ‘transportation costs’=20
 iE  w  c(ei )  20
with v=120

Feedback to the employee:
own payoff
Information Treatment
End of stage 1: employees (not firms) receive information on their
reference group’s incomes before accepting the contract
Information set: income levels of 4 other employees
Theoretical predictions
Same SPNE in both treatments:
e*=0.1 => w*=20
Experimental procedures
Regate software, GATE Lyon
120 participants from undergraduate classes in engineering and business
schools
6 sessions (with 20 participants each): 2 sessions in the Benchmark
Treatment (200 obs.) + 4 sessions in the Information Treatment (400 obs.)
10 repetitions with a Perfect Stranger matching protocol
At each of the 10 periods, in the Info Treatment, the set of 5 incomes
come from randomly chosen firms
80 different income distributions
60 minutes
Average earnings: € 14. Show-up fee: € 5
Survey data: 1997 Work Orientations module of the International Social
Survey Program (ISSP: http://www.issp.org)
11,987 individuals aged 16-65 in full or part-time jobs
17 countries
Key variables:
 Earnings: individual, yearly earnings
 Weekly hours of work
 Discretionary effort at work (scaled from 1 to 5):
“I am willing to work harder than I have to in order to help
the firm or organization I work in to succeed”
= Equivalent to effort in the experiment
Country
USA
Canada
Portugal
Switzerland
Denmark
Great Britain
Japan
Hungary
Czech Republic
Norway
East Germany
West Germany
Sweden
Spain
Poland
Italy
France
Total
Employees
interviewed
No.
%
775
546
843
1 727
600
545
607
626
526
1 366
261
648
793
387
564
475
698
11 987
6.47
4.55
7.03
14.41
5.01
4.55
5.06
5.22
4.39
11.40
2.18
5.41
6.62
3.23
4.71
3.96
5.82
100.00
Mean Effort
3.93
3.75
3.71
3.65
3.64
3.63
3.62
3.60
3.60
3.59
3.59
3.52
3.42
3.35
3.26
2.96
2.85
3.55
ei=f(yi,y*, hi)
Reference group income y* = average values by broad demographic groups
(Leyden School- see van Praag and Frijters, 1999)
Average earnings calculated by
- Country (17)
- Sex
- Education: 3 groups (10 or fewer years of education / 11 to 13 /
over 13 years education)
- Age groups: 3 groups (16 to 29 / 30 to 44 / 45 to 65)
 306 reference group income cells (= y*)
Normalized earnings rank = 1- (rank in cell / #obs. in the cell)
3. Results
Effort and Comparison Income
In the experiment, employers do not care about social comparisons
Average income = 53.56 (SD: 19.75) in the Benchmark Treatment
53.09 (SD: 20.04)
Information
The income - effort relationship is positive and steeper in the Information
Treatment (Mann Whitney Tests)
0,9
0,8
Mean effort
0,7
0,6
0,5
Benchmark Treatment
0,4
Info Treatment
0,3
0,2
0,1
0
20-25
26-35
36-45
46-55
56-65
Wage
66-75
76-85
86-95
96-120
The rank-dependence of effort (Random-effect Tobit model)
Placebo
test
Effort is strongly correlated with own absolute income
Effort increases with the rank in the income distribution
Experiment: a rise in rank of 1 position increases effort as much as
an income increase of 9.7%. Rank/income elasticity=0.49
ISSP: a 20% rank increase is worth $ 606 per month on
average. Rank/income elasticity=1.6
Average reference group income has a significant influence only in
the experiment
=> Comparisons are more ordinal than cardinal
Effort and Comparisons over time
Hypothesis: past exposure to higher incomes may reduce the utility
associated with current income and decrease the current level of effort
Not easy to test with field data because of the difficulty to ensure that
ceteris paribus holds over long time-periods between waves.
Experimental data ideally suited to test models of habituation: same
environment over time
Test: we estimate the influence of the running minimum and running
maximum incomes and ranks on the current level of effort
Inspired by the peak-end transformation in psychology
(Redelmeier and Kahneman 96)
Past income matters! (Random-effect Tobit on experimental data only)
4. Conclusion
Both the experimental evidence and the ISSP data analysis show the
importance of income comparisons on observable behavior
Effort at work depends both on own income and on what others earn
Income rank is a better predictor than average reference group income
Income profile over time matters in itself; higher influence of
relative demotions than promotions. Past best rank matters more than
past best absolute income => Implications on mergers
1) Interpretation: Status seeking (Frank 85) drives effort behavior
Alternative interpretations:
-> Inequality aversion (Fehr and Schmidt 99, Bolton and Ockenfels 00)?
(but why a stronger role for rank? Why an influence of the past?)
-> Search for the fair wage
(but why not more rejections over time? Why not care about worse
wages in the past?)
In general, effort likely depends on how well the workers think that
they are treated.
Krueger, A., and Mas, A. (2004). "Strikes, Scabs and Tread
Separations: Labor Strife and the Production of Defective
Bridgestone/Firestone Tires". Journal of Political Economy, 112,
253-289.
The Decatur tyre plant had a long and contentious strike in the mid1990s.
Replacement workers were used during the strike, and then union
workers rehired after the strike had ended.
Take a D-i-D approach:
Compare Decatur to the other Bridgestone plants pre- and post- the
dispute period.
Outcome variables: complaints from customers, and fatal accidents.
They find that just over 50% of fatal accidents were liked to these
tyres due to excess defects associated with the labour dispute.
Effort and Loss-Aversion
Abeler, J., Falk, A., Goette, L. And Huffman, D., "Reference
points and effort provision". American Economic Review,
April 2011.
Experimental approach.
Subjects work on a tedious task: counting the number of zeros in
tables that consisted of 150 randomly ordered zeros and ones.
Two stages
During the first stage, subjects had four minutes to count as many
tables as possible. They received a piece rate of 10 cents per
correct answer for sure.
• Count zeros in tables shown on
the screen
– Boring and pointless task
– Very low intrinsic motivation
In the second (and main) stage, the task was again to count zeros, but
there were two differences compared to the first stage.
First, they could now decide themselves how much and for how long
they wanted to work. At most, they could work for 60 minutes.
How much subjects chose to work is the main outcome variable in
the analysis of effort.
The second difference was that subjects did not get their accumulated
piece rate earnings from the main stage for sure. Before they
started counting in the main stage, they had to choose one of two
closed envelopes. They knew that one of the envelopes contained a
card saying “Acquired earnings” and that the other envelope
contained a card saying “3 Euros.” But they did not know which
card was in which envelope.
Uncertainty is resolved only after they have stopped working
There were two main treatments. The only difference between these
treatments was:
• the amount of the fixed payment: 3 Euros or 7 Euros. Treatments
were assigned randomly to subjects.
• If the fixed payment is f, the piece rate is w and effort is e:
Optimal effort e* is independent of the fixed payment, f.
This makes sense, and underlines an important economic truth: for a
variable (price, others’ actions, whatever) to affect my behaviour,
it must affect the net marginal utility (= marginal utility –
marginal cost) from my actions. A deadweight effect on utility is
like a sunk cost and won’t change behaviour.
The findings are that those in the 7 Euro fixed payment treatment
work significantly longer before stopping.
How can this be explained?
Is this just tracing out a labour supply curve? Have we just shown
dH/dw > 0?
No, because you receive f (with probability of 0.5) whether you work
for 30 seconds or the full hour.
Many subjects stop when accumulated earnings equal the fixed
payment (continuous updating of no. of tables correctly
evaluated).
• HI vs. LO (N=120)
Stopping at 3 euros
• LO: 15.0 %
• HI: 1.7 %
• U-test: p=0.009
Stopping at 7 euros
• LO: 3.3 %
• HI: 16.7 %
• U-test: p=0.015
Stopping at f modal
choice in both
treatments
The authors argue that f affects H via the marginal utility of piece
rate earnings (=we, which are received with probability of 0.5).
Often, people compare their outcome to some reference point, as in
loss aversion (Kahneman & Tversky 1979)
Examples:
 Paying an unexpectedly high price for a good
 Not getting an expected wage increase
 Being rejected after a "revise & resubmit" vs. being rejected
directly
Specifically, f acts as a benchmark, and earning less than f (from
the piece rate payment) is perceived as a loss. Individuals are
loss-averse and thus act to reduce the chance that this happens.
Greater effort is therefore associated with higher
expectations or benchmarks. In this experiment there is
a probability that you will receive the benchmark, but
we could imagine this in general being sociallydetermined.
Higher expectations will lead to greater effort. But does
that mean that worker utility is lower? In this
experiment it is unclear whether worker well-being is
lower as f rises (as they may well receive this fixed
payment). More generally, if there is no utility value
from the benchmark, greater expectations should
correspond to lower utility.
Conclusion
Job Quality fell from the 1980s/1990s to the early 2000s. Job content
might have been the reason. Interpretations.
1) Wages went up (but that doesn’t reduce utility)
2) Unemployment increased (but endogenous, and false)
3) The cost of monitoring fell
4) Easier to sack shirkers
5) Consequences of shirking now more serious (more tournaments)
6) Declining unionism
7) A possible psychological role for effort (but this doesn’t
work..things that make me work hard should also make me
happy)
So What?
Why do we care about job quality?
Because it is a measure of VE, the value of a job. And we worry
about this for social welfare reasons.
But also because it might help us to understand labour-market
behaviours.
The value of a job is relative to unemployment or inactivity.
As VE – VU falls, employment becomes less attractive. This can
happen because job quality falls, or because unemployment
becomes less unpleasant (for example, the social-norm effect).
BHPS Results from Clark (2003)
GSOEP Results from Clark et al. (2010)