Job Quality and Effort

APE/ETE Masters Course

Andrew E. Clark (Paris School of Economics and IZA)http://www.parisschoolofeconomics.com/clark-andrew/

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 EffortWe 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 1992-2002. 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.

Pooled Panel

1993 -0.079* -0.165** (0.033) (0.042)

1994 -0.127** -0.241** (0.033) (0.054)

1995 -0.142** -0.256** (0.033) (0.069)

1996 -0.116** -0.231** (0.032) (0.085)

1997 -0.067* -0.195 (0.032) (0.103)

1998 -0.171** -0.278* (0.032) (0.121)

1999 -0.204** -0.355* (0.031) (0.139)

2000 -0.206** -0.333* (0.031) (0.157)

2001 -0.169** -0.307 (0.031) (0.178)

2002 -0.200** -0.362 (0.032) (0.197)

Male -0.208** (0.010)

Age -0.061** -0.009 (0.003) (0.021)

Age-Squared/100 0.079** 0.014 (0.004) (0.010)

High Education -0.208** (0.014)

Medium Education -0.153** (0.013)

Separated 0.005 0.121* (0.031) (0.047)

Divorced -0.040* 0.028 (0.017) (0.039)

Widowed 0.056 0.099 (0.040) (0.103)

Single -0.116** -0.094** (0.014) (0.033)

Overall Job Satisfaction

-0.400

-0.350

-0.300

-0.250

-0.200

-0.150

-0.100

-0.050

0.000

1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

Year

Reg

ress

ion

Coe

ffic

ien

t

Pooled Panel

Satisfaction: Pay

-0.250

-0.200

-0.150

-0.100

-0.050

0.000

0.050

0.100

0.150

0.200

1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

Year

Reg

ress

ion

Coe

ffic

ien

t

Pooled Panel

Satisfaction: Hours of Work

-0.200

-0.150

-0.100

-0.050

0.000

0.050

1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

Year

Reg

ress

ion

Coe

ffic

ien

t

Pooled Panel

Satisfaction: Job Security

-0.250

-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 1999 2000 2001 2002

Year

Reg

ress

ion

Coe

ffic

ien

t

Pooled Panel

Satisfaction: Work Itself

-0.500-0.450-0.400-0.350-0.300-0.250-0.200-0.150-0.100-0.0500.0000.050

1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

Year

Reg

ress

ion

Coe

ffic

ien

t

Pooled Panel

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 L

q

dq

d L = 1, which defines optimal probationary monitoring, q*.

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. Self-report1 No2 Yes3 No4 No5 No6 Yes7 No8 Yes9 No10 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 (within-site) 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 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).

Temporary Jobs and Work Effort.

Engellandt and Riphahn observe that P(Temp Perm) positively correlated with worker effort when Temporary.

Workers are assumed to prefer permanent to temporary jobs.

Perm TempAbsence 1.2 0.8UOT 20.6 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 3Little 6Some 23Lot 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 (inter- temporal; to oneself in the past)?

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

2. Empirical strategy

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 a contract

Stage 2: The employee accepts or rejectsIn case of rejection, both earn 0In case of an acceptance, choice of level of effort

Convex cost function

120,...,21,20w

0.1,0.2,...,1ie

Effort e 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Cost c(e) 0 1 2 4 6 8 10 12 15 18

Firm’s payoff: with v=120

Employee’s payoff: with ‘transportation costs’=20

Feedback to the employee: own payoff

iF v w e

20)( iEi ecw

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 jobs17 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

Employees interviewed

Country

No. % Mean Effort

USA 775 6.47 3.93 Canada 546 4.55 3.75 Portugal 843 7.03 3.71 Switzerland 1 727 14.41 3.65 Denmark 600 5.01 3.64 Great Britain 545 4.55 3.63 Japan 607 5.06 3.62 Hungary 626 5.22 3.60 Czech Republic 526 4.39 3.60 Norway 1 366 11.40 3.59 East Germany 261 2.18 3.59 West Germany 648 5.41 3.52 Sweden 793 6.62 3.42 Spain 387 3.23 3.35 Poland 564 4.71 3.26 Italy 475 3.96 2.96 France 698 5.82 2.85 Total 11 987 100.00 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

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

20-25 26-35 36-45 46-55 56-65 66-75 76-85 86-95 96-120

Wage

Mea

n ef

fort

Benchmark Treatment

Info Treatment

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)

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

4. Conclusion

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 mid-1990s.

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 socially-determined.

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)