Bruno Contini with Matteo MoriniUniversity of Torino and LABORatorio R. Revelli,

Centre for Employment Studies

JOB CHANGING BEHAVIOR: IS THERE A CASE FOR BOUNDED RATIONALITY ?

September 2007

“fail often, fail better…” S. Beckett

We observe the ex-post performance of job changers and establish the extent to which it suggests “fully rational” or “boundedly rational” behavior.

Two contrasting hypotheses:

Individuals choose to move or stay on the basis of subjectively perceived assessments of

future (long run) wage growth

risk of job loss

arguments of a well defined utility function (known to the agents)

ORboundedly rational decisions based on “satisficing” rules of thumb (or other “reasonable” criteria).

In mainstream microeconometric explorations the hypothesis of full rationality is a given and never subject to test.

This is what we might expect under full rationality…., and also under bounded rationality !

real wage growth in a 3-year window after the job change

Risk-on-the-job in a 3-year window after the job change

DATA - Data from WHIP (Work Histories Italian Panel), employer-employee longitudinal panel

random sample of all Italian employees of the private sector, observed at monthly frequency (at the time available from 1985 to 1998, now updated to 2003).

The sample-population ratio is 1:90.

Closed panel of male individuals working full-time in the private sector, age 30 -40 in 1986 (over 7000 individuals), observed from 1986 through 1996.

Gender, age and working hours restrictions respond to the necessity to minimize heterogeneity of behaviour unrelated to job changing activities (maternity and child care, retirement choices, etc.).

a pseudo-utility function as a plausible counterfactual

As a plausible counterfactual (fully rational individuals) we assume a Cobb-Douglas utility function (U) in two arguments: the observed (ex-post) real wage growth over the future 3-year window (W), and a proxy of risk-on-the-job (ROJ)

U = [(W)**n] / [ROJ**m]

Workers accept job offers on the basis of two criteria:

- if the wage offer is “sufficiently high” (i.e. higher than some unknown reservation wage);

- if the offered position is subjectively perceived to be “sufficiently stable” (i.e. with a low probability of being dismissed or forced to leave).

Both arguments imply a subjective judgement on the future evolution of earnings and on the quality of the job. Neither W, nor ROJ are known a priori.

The robustness of the hypothesis of bounded rationality may be tested also by letting n and m take different values.

BOUNDED RATIONALITY

Agents set targets – aspiration levels – à la Simon, on the basis of “local knowledge”, and choose “satisficing” options with limited information / computational ability, The agents’ happiness depends on the difference between output y and aspiration level y* (alias reference point, alias norm in Akerlof’s terminology).

Sophisticated versions of this model embody “loss aversion”: preferences are kinked at y*.

The aspiration level is evolutionarily updated over time on the basis of performance and learning mechanisms.

In our context, y* consists of a two-dimensional vector of long run wage growth and risk-on-the-job.

BOUNDED RATIONALITY WHERE ?

a lot of experimental and behavioral literature

America’s mortgage business with subrprime borrowers (the most recent example)

rational or behavioral investors ? (Guiso, Jappelli, Lusardi)

consumer behavior in the credit card industry (H. Shui and L.M. Ausubel, 2004)

purchase of large appliances (J. Hausman, 1979)

purchase of flood and earthquake insurance (H. Kunreuther et al. 1978)

reference based behavior of package deliverers (Fehr et al. 2007) = PROBABLY THE ONLY WELL-DESIGNED EXPERIMENT ON REAL ECONOMIC FACTS

case-based decision theory (Gilboa – Schmeidler) = GAME THEORETIC APPROACH

HOW ? WHY ?

distaste for psychological transaction costs

cognitive dissonance

salience (the bias of attaching undue weight to recent events)

social norms and gift advantages (Akerlof)

slow learning and adaptation (in domains where the planning horizon is long and the stakes are high)

MOVERS = individuals observed in different firms in 1986 and 1991. The three-year window is measured since the last relevant job switch. STAYERS = individuals observed in the same firm from 1986 to 1991 (careers possibly interrupted by short unemployment or temporary layoff spells in between).

In testing modes of behavior, comparisons must involve individuals who have expressed a choice (voluntary movers and stayers). How to recognize Involuntary movers ? individuals found on a job in 1991, but have been – as it were -“forced” to leave a preceding position as a consequence of collective layoffs and/or incumbent industry or firm crises:

at work in 1991 after having switched jobs in 1986-91;either (i) leaving firms that had closed and exited the market

before 1991; or (ii) had undergone drastic workforce reductions before 1991

(> 40% of 1986 workforce).

No way to single out voluntary vs. involuntary stayers. As will be seen, this makes our tests more robust !

Dataset: stayers, movers

Stayers:

7.063 Movers:

2.723

1.594205

Involuntary movers

Workers whose firm exits the market or

exhibits a workforce decrease above 40%

between 1986 and 1991

2723-1594 = 1129 Voluntary movers

Measuring real (long run) wage growth (W)

STAYERS w3 = average yearly nominal wage earned during the 3-year spell started in 1990:

w1 = average yearly nominal wage earned at the end of 1990.

MOVERSw3 =average yearly wage earned during the 3-year spell after the job switch

w1 = average yearly wage earned at the end of the period preceding the job switch.

Nominal wages are deflated by CPI (p).

W = real (long-run) wage growth W = w3/ p / w1/p

The post change performance of movers and stayers is recorded through a sliding three-year window

risk-on-the-job ROJ = [predicted individual likelihood of dismissal 1986-91] /

[firm employment trend 1994 / 1991]

Suppose that Mr. X’s predicted likelihood of (past) dismissal is 0.30. If Mr. X stays at his firm of origin and such firm increases employment by 50% in the next 1991-94 period. Mr. X’s risk-on-the-job is reduced to 0.30/(1+ 0.5) = 0.20. If he moves to a different firm that cuts employment by 20%, his risk-on-the-job increases to 0.375 = 0.30/(1-0.2).

(i) likelihood of dismissal prior to 1991 (NUMERATOR OF ROJ)

Likelihood of dismissal estimated on the open panel 1986-1991, including all full-time male workers aged 20-50. In 1986 the number of workers on payroll is 36,114; of these, only 15,394 are left by 1991.

Logit estimated separately for white and blue collars, against a set of covariates including age and age-square, wage, industry, location, firm size and firm employment trend, initial conditions and various interactions.

(ii) projected employment trend (DENOMINATOR OF ROJ) Firm employment histories are observed through 1996.

E(i, 1994) / E (i,1991) E(i) = total employment at i-th firm

Is a simple indicator of firm-specific trend.

The i-th movers’ ratio E(i,1994) / E(i,1991) is measured at the firm that made (i) a successful offer (around 1991).

Nearly two thirds of the observable firms reduce their workforce in the 1991-94 period that falls around the 1993 recession: this is in line with well known trends of the Italian labor market.

Blue collars

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

movers

stayers

White collars

0

0.5

1

1.5

2

2.5

movers

stayers

W = real wage growth

Long run wage growth W: large variability among movers, smaller among stayers.

Blue-collars = stayers do better in the low half of the distribution, movers in the upper half. White-collars = the movers’ performance dominates the stayers from P20 onwards.

ROJ : movers are more exposed to the risk of job loss than the stayers (a result for which we find no precedents in the literature). result somewhat blurred by composition effects…..

25% of the movers who switched jobs around 1991 end up in firms that exit the market before the end of 1994, while only 10 % of the stayers (who did not make the switch) are in the same position.

Blue collars

0

2

4

6

8

10

12

14

16

18

movers

stayers

White collars

0

2

4

6

8

10

12

14

16

18

20

movers

stayers

U = W / ROJ

BLUE COLLARS WHITE COLLARS

U = W / ROJ**3

0

500

1000

1500

2000

2500

pct1

0

pct2

0

pct3

0

pct4

0

pct5

0

pct6

0

pct7

0

pct8

0

pct9

0

movers

stayers

U = W**1 / ROJ **3 blue collars U = W**1 / ROJ**3 - white collars

0

500

1000

1500

2000

2500

3000

3500

4000

pct1

0

pct2

0

pct3

0

pct4

0

pct5

0

pct6

0

pct7

0

pct8

0

pct9

0

movers

stayers

BLUE COLLARS WHITE COLLARS

U = W**3 / ROJ

U = W**3 / ROJ**1 - blue collars

0

5

10

15

20

25

30

35

pct1

0

pct2

0

pct3

0

pct4

0

pct5

0

pct6

0

pct7

0

pct8

0

pct9

0

movers

stayers

U = W**3 / ROJ**1 - white collars

0

5

10

15

20

25

30

35

40

45

50

pct1

0

pct2

0

pct3

0

pct4

0

pct5

0

pct6

0

pct7

0

pct8

0

pct9

0

movers

stayers

BLUE COLLARS WHITE COLLARS

<aspiration levels / reference targets>

strict “local knowledge” implies that search for better alternatives is often confined into:

- same industry (11 manufacturing and service sectors)

- same firm size (3 size classes: small (<20), medium (20-200), large (>200)

- same skill level (blue and white-collars)

- same location (3 geographical areas: North, Centre, South)

In principle 196 CELLS (reduced to 42 to preserve at least 10 individuals per cell).

Reference targets = percentiles of two distributions of the ex-ante performance indicators (i.e. referring to the pre-job change window 1986-91), whose ex-post values measure performance

real wage growth W

likelihood of dismissal (ROJ)

We shall experiment with

Intermediate targets y*: [P 50 (W) & P50 (ROJ)]

High targets y** : [P66 (W) & P33 (ROJ)]

with no dynamic adjustment.

0,00

0,10

0,20

0,30

0,40

0,50

0,60

0,70

0,0 2,0 4,0 6,0 8,0 10,0 12,0 14,0

Serie1

Utility U is calculated at y* for each cell shows a large variability between cells.

The (slight) negative inclination in the scatter suggests that cells closer to the unconditional efficiency frontier have less variability than the ones removed from the frontier.

. Average euclidean distance D within cell around y*=(P50, P50)

D(y*) = distances in the ordinate; U(y*) in the abscissa

cells closer to the unconditional efficiency frontier in the <W-ROJ> space have less variability than those removed from the frontier

Scatter of the reference points y*(k) in the space <W – ROJ>

(cells k = 1,2, ,2,…..42) .

Very similar to the

unconditional scatter of all 7000 + observations

0,000

0,200

0,400

0,600

0,800

1,000

1,200

1,400

0,000 0,050 0,100 0,150 0,200 0,250 0,300 0,350

Serie1

The position of all cells is determined by industry differentials, skill differentials, firm size differentials. Cells in the N-W corner dominate those in the S-E corner.

Same as before: in the <W – ROJ > space the circles, centered at y* - are proportional to (P90 – P10) of the U-function in each cell. The efficiency frontier – taken as P90 of the U-function for all the observations – lies to the left of the graph and cuts across only a few of the N-W cells

1most observations are very distant

from efficiency frontier;

2 movers’ search for new opportunities

limited to own neighborhood…...

U=W / ROJ**3 U = W / ROJ U= W**3 / ROJ cell 2LB 3 3 5 2LW 15 18 27 2MB 4 4 4 2MW 21 21 26 2SB 0 0 8 3LB 7 7 5 3LW 10 14 25 3MB 7 11 13 3MW 13 16 26 3SB 2 2 9 3SW 18 18 18 4LB 6 13 38 4LW 7 13 33 4MB 12 18 21 4MW 20 23 29 4SB 0 0 4 4SW 13 0 13 6LW 29 29 29 6MB 4 4 8 6MW 16 16 24 6SB 0 2 6 6SW 10 10 21 7LB 9 0 0 7LW 0 13 13 7MB 0 0 0 7MW 0 0 0 7SB 0 0 9 8LB 50 50 0 8LW 30 26 33 8MB 15 15 23 8MW 56 64 64 8SW 40 40 40

% frequency of

voluntary movers

above efficiency

frontier (p90 of

different U definitions).

Cells with < 10

observations

removed

INTER CELL MOBILITY

Mobility across cells is limited:

- 40% of the movers do not change cell (specific human capital or limited search ? )

- 65% of the movers do not change industry (some moves take place across firms of different size)

EX-POST PERFORMANCE NOT DISTANT FROM REFERENCE POINTS

- see slide no. 23 = 80% of individuals within circles centered at y* = p50, P50;

- about 90% within circles centered at y* = P60, P40 (a more ambitious target);

- very few above the overall efficiency frontier (9-th decile of U distribution).

If ex-post performance is symmetrically distributed around the median reference target (a modest achievement rate), we expect 25% of individuals to be found above BOTH targets simultaneously, and 75% below at least one. With more ambitious target [(y* = P66 (W); P33(ROJ)] the expected achievement rate will be 1/9 = 11%.

FREQUENCY ABOVE / BELOW REFERENCE POINTS

cellfront below above below above1LB 100.00 83.33 16.671LW 83.33 16.67 83.33 16.671MB 100.00 100.001MW 100.00 100.002LB 92.31 7.69 87.18 12.822LW 84.85 15.15 72.73 27.272MB 84.62 15.38 84.62 15.382MW 84.21 15.79 73.68 26.322SB 91.67 8.33 75.00 25.003LB 90.91 9.09 85.45 14.553LW 74.60 25.40 68.25 31.753MB 90.63 9.38 84.38 15.633MW 82.86 17.14 72.86 27.143SB 95.74 4.26 87.23 12.773SW 63.64 36.36 63.64 36.364LB 87.50 12.50 75.00 25.004LW 80.00 20.00 80.00 20.004MB 83.82 16.18 72.06 27.944MW 77.14 22.86 71.43 28.574SB 75.56 24.44 62.22 37.784SW 37.50 62.50 37.50 62.506LW 42.86 57.14 42.86 57.146MB 75.00 25.00 70.83 29.176MW 84.00 16.00 80.00 20.006SB 88.46 11.54 82.69 17.316SW 72.41 27.59 65.52 34.487LB 100.00 90.91 9.097LW 50.00 50.00 50.00 50.007MB 100.00 92.31 7.697MW 100.00 100.007SB 100.00 95.65 4.358LB 100.00 100.008LW 66.67 33.33 66.67 33.338MB 92.31 7.69 84.62 15.388MW 76.00 24.00 68.00 32.008SW 100.00 60.00 40.00

front: 60-40 front: 50-50

Joint attainment (ABOVE) is larger than expected values under symmetry.

Higher with respect to y* than y** (more ambitious target).

In few cases (7 out of 42 ) the attainment of y* is less than 10%. Only in 3 cases do we find it above 50% with respect to y**.

The relatively high frequencies of joint attainment are prerogatives of the white-collars (last cell digit is W)..

In all cells, while the reference points y* are very different - some strongly dominate others - the % of individuals above / below y* is always quite constant.

Suggestion: people are satisfied by relative positions that are roughly similar across cells, i.e.

…..THE DEGREE OF INDIVIDUAL SATISFACTION IS A RELATIVE MATTER……..

All the above results strongly suggest bounded rationality of the agents.

Under full rationality the following are to be expected:

- High(er) inter-cell mobility, reflecting substantial search activity across different industries and firm sizes;

- Greater dispersion around reference points (reference points are irrelevant under full rationality);

- More clustering in the vicinity of the efficiency frontier.

Estimation of the wage / risk tradeoff

(i) OLS estimationA trade-off between real wage growth and risk-on-the-job is reasonable among fully

rational as well as boundedly rational agents. It is estimated – on (y – y*) differences - with the following linear model on all the (voluntary) movers:

(1) [(W(i,k) - W*(k)] = a + b [ROJ(i,k) - ROJ*(k)] + c X(i,k) + d I(k) + e INTER(i,k) + u(i)

X(i,k) numerical covariates; I(k) dummy of cell indicators: 2 skill groups, 9 industries, 4 geographical areas, 3 firm sizes; INTER(I,k): all relevant interactions. No endogeneity problems with ROJ as it is estimated from worker-specific covariates prior to 1991, and forward looking firm-specific elements.

Model (1) is similar – in deviations from w* - to the specification of <ln wage>derived from theoretical equilibrium conditions of standard job search theory:

ln w (i) = f (B, theta, nu, r ) + g (X(i)-controls) + residuals (i)

B = unemployment benefits, a proxy of the reservation wage, theta = labor market thickness (or arrival rate of a job offer); nu = bargaining strength, a shift factor; r = a discounting factor incorporating all future dynamics. “Theta” and our “risk-on-the-job” convey similar (but opposite) concepts of job stability / instability

Individual fixed effects removed

Individual fixed effects on wage growth are removed using the following strategy.

Let W(0,i) be the wage increase of the i-th individual in the time window 1986-81, and W(1,i) the same in the more recent !991-94 window.We use the specification used in the OLS version, with X numerical covariates and I dummy-indicators, and take differences:

W (0,i) = alfa(i) + beta * X(0,i) + gamma * I(0,i) + res(0,i) W (1,i) = alfa (i) + beta * X(1,i) + gamma * I(1,i) + res(1,i)

 delta W(i) = W(1,i) – W(0,i) = beta * [X(1,i) - X(0,i)] + gamma * [I(1,i) - I(0,i)] + [res(1,i) - res(0,i)]which allows to retrieve "beta" and "gamma" coefficients non contaminated by individual effects.

beta^^ and gamma^^ are the non-contaminated estimates.

We obtain non-contaminated predictors of W(1,i) as follows: W(1,i)^^ = beta^^ * X(1,i) + gamma^^ * I(1,i) + mean[W(0,i)] and re-estimate (1) with W(1,i)^^ in place of W(i)

(2) [(W(i,k)^^- w*(k)] = a + b [ROJ(i,k)- ROJ*(k)] + c X(i,k) + d I(k) + e INTER(i,k) + u(i,k)

.

ESTIMATION OF TRADE-OFF EQUATIONS (1) and (2): DEPENDENT VARIABLE (W – W*), WITH W* = [P50(W), P50(ROJ)]

(W - W*) and (ROJ – ROJ*) standardized before estimation

OLS estimates after elimination of fixed individual effects in W

White - 0.182 - 1.098 ****

(ROJ - ROJ*) - 0.027 0.078 **

(ROJ - ROJ*) x white - 0.119 * 0.053

Small firm 1991 0.070 - 0.073

Large firm 1991 0.010 1.920 ****

Moves 0.390 *** 0.094 *

Ineq86 - 0.479 ** - 0.660 ***

Ineq86 x white 0.400 ** 0.145

Industry dummies 1991 n.s. yes ***

Age & age**2 n.s. n.s.

------------------------------------------------------------------------------------------------------------

R**2 0.14 0.52

OLS estimation the trade-off between wage growth and risk-on-the-job is non-significant (and negatively sloped).

OLS with wage growth W replaced by predictor net of individual effects W*

a significantly positive (and “rational”) tradeoff between wage growth (deviations from W*) and risk-on-the-job (deviations from ROJ*).

Additional indications:- white-collars severely penalized vis-à-vis manual workers - industry dummies very significant- large firm cells perform better than small & medium size- proxy of initial conditions (ineq86) highly significant: low starters do better than high starters - individuals who have switched jobs more than once (<moves>) are better off than

the one-time movers (a “rational” choice if the first move was unsatisfactory)

High significance of cell indicators may be counter-intuitive, given that all observations, once expressed in deviations from relevant targets, are compressed towards the origin. But slide 20 - and XXX to be added - show that some cells strongly dominate others. White-collar cells are dominated; some industries and large firms outperform others.

Slope negative and below significance

Slope positive, well above significance.

Two distinct clusters: big firms above; small firms below.

[1] It could be argued that the result depends on our arbitrary parametrization of the utility function. If it were defined otherwise, say U = (G-w) n / ROJ m for other values of n and m,

the conclusions may not hold. The following table displays values of P50/P90 for m=1 and different values of n. Changes are modest and confirm our intuition, including the one

pointing at the higher variability of the movers’ performance vs. the stayers’. m = 1 n= 0.1n= 0.2n= 0.5n= 1n= 2n=

5Movers0.430.440.440.410.320.14Stayers0.610.600.600.580.540.40

A quasi-counterfactual analysis: movers vs. matching stayers

"how would the movers have performed had they decided not to move ?".

each mover is linked to his observable co-workers (“matching stayers”). The stayer co-workers include voluntary as well as involuntary agents, and represent a quasi-counterfactual.

The PREMIUM ratio, defined as PREMIUM= Performance (movers ) / Performance (median matching-stayers)

indicates the relative performance of each mover vis-à-vis his matching stayers. Performance is W, ROJ and U.

Among the white-workers, the median mover performs better than the median matching stayer: in about 60% of the cases we observe PREMIUM > 1. Among the blue- collars, instead, the comparative performance is split at the median (PREMIUM reaches 1 at P50).

A quasi-counterfactual:

PREMIUM = IND(movers) / IND(matching stayers)

Wage growth (W) = 40% of matching stayers better off than movers (BLUE); 50% of matching stayers > movers (WHITE)

Risk-on the-job (ROJ) = 60% of matching stayers better off than movers (BLUE and WHITE)

Premium ROJ

0,000,501,001,502,002,503,003,504,004,505,005,50

decilespr

em R

OJ

B

W

threshold

Premium W

0,00

0,50

1,00

1,50

2,00

deci les

B

W

threshold

PREMIUM UTILITY = W / ROJ

The answer to the question "how would the movers have performed had they decided not to move ? “ would be

"the MEDIAN mover performs a little worse than his matching stayers". (but conclusion strengthened by the fact that the comparison includes the

involuntary stayers)

60 % matching stayers have higher utility than movers (BLUE)

50% matching stayers have higher utility than movers (WHITE)

Premium U

0,00

0,50

1,00

1,50

2,00

2,50

deciles

B

W

threshold

PREMIUM (UTILITY) with varying parameters m and n

MATCHING STAYERS VS. MOVERS UNDER ALTERNATIVE PARAMETRIZATION OF UTILITY

only slight changes from previous slides

Premium U (n=3; m=1)

0,00

0,50

1,00

1,50

2,00

2,50

3,00

3,50

4,00

4,50

5,00

5,50

deciles

B

W

threshold

Premium U (n=1; m=3)

0,000,501,001,502,002,503,003,504,004,505,005,506,006,507,007,508,008,509,009,5010,0010,5011,0011,5012,0012,5013,00

deciles

prem

U

B

W

threshold