1. Rational Expectations and the Puzzling No-Effect of the Minimum WAge
Sara Pinoli
Bocconi University
December 12th, 2008

2. EMPLOYMENT EFFECT OF MW
Introduction Model Empirical Analysis Conclusion
EMPLOYMENT EFFECT OF MW
Competitive labor market  disemployment effect
Controversial empirical evidence  puzzle
characteristics
of labor market
monopsony
efficiency wage
endogenous search effort
characteristics
of MW policy
expected
unexpected

3. Introduction Model Empirical Analysis Conclusion
CONTRIBUTION
Changes in the minimum wage are often PREDICTABLE
 rational agents discount future variations in the minimum wage
 expected changes in the minimum wage have different observed employment effect than unexpected changes

4. Introduction Model Empirical Analysis Conclusion
MAIN FINDINGS
Model:
agents adjust their behavior in advance
expected ΔMW has lower observed effect than unexpected ΔMW
 harder to identify
Empirical Evidence (Spain 2004:3):
unexpected ΔMW has higher positive effect on job separations;
unexpected ΔMW has negative effect on ex-post Δu
expected ΔMW has negative effect on ex-ante Δu

5. OUTLINE
Model
Empirical Analysis
Conclusion

6. Introduction Model Empirical Analysis Conclusion
MODEL: FRAMEWORK
LOW WAGE WORKERS’ LABOR MARKET:
matching frictions: m(v,u)
heterogeneous stochastic matches:
productivity
productivity shocks at rate λ
firing cost F
minimum wage increases with probability
state 0:
state 1:
state 2:

7. Introduction Model Empirical Analysis Conclusion
FLOWS DYNAMICS

8. UNEMPLOYMENT DYNAMICS
Introduction Model Empirical Analysis Conclusion
UNEMPLOYMENT DYNAMICS

9. Introduction Model Empirical Analysis Conclusion
MODEL: SUMMARY
Expectations affect the TIMING of the MW effects
Ex-post effects expected MW vs. unexpected MW
lower disemployment
lower increase in job destruction
lower decrease in job creation
 harder to find empirical evidence of disemployment effect of expected changes in the minimum wage

10. EMPIRICAL ANALYSIS: SPAIN
Introduction Model Empirical Analysis Conclusion
EMPIRICAL ANALYSIS: SPAIN
Why Spain?
Expected vs. unexpected Δ+MW: Aznar vs. Zapatero 
Data
EPA : rotating quarterly survey on population living in Spain.
150,000 individuals aged 16+
6 consecutive quarters
working status, personal characteristics

11. ECONOMETRIC SPECIFICATION
Introduction Model Empirical Analysis Conclusion
ECONOMETRIC SPECIFICATION
Probit model
employment prob.
job destruction rate
job finding rate
Difference in difference
Young (16-24) vs. adult (25-54),

12. Introduction Model Empirical Analysis Conclusion
ROBUSTNESS
Expected Δ+MW
CK(95): only substantial ΔMW can be used to estimate the employment effect  ZMW
Treatment and control groups
not all the young workers are paid MW 
young female vs. adult female
young female low edu vs. adult female low edu
Timing
expectations affect the timing of employment effects 

13. RESULTS: EMPLOYMENT PROBABILITY
Introduction Model Empirical Analysis Conclusion
RESULTS: EMPLOYMENT PROBABILITY
Y vs. A
YF vs. AF
YFL vs. AFL
young*UMW1
0.003
0.002
0.001
-0.000
(0.001)***
(0.001)**
(0.001)
(0.003)
young*ZMW2
-0.002
-0.003
-0.010
-0.012
(0.002)
(0.005)*
(0.005)**
young*UMW_pre3
-0.001
young*UMW_post4
-0.004
(0.003)***
young*ZMW_pre3
-0.006
(0.004)*
young*ZMW_post4
(0.004)***
Pseudo-R2
0.237
0.147
0.033
Observations
952728
199349
Standard errors in parenthesis
Controls: gender, education, attended courses the last month, time (quarterly) effect, linear trend, region of residence

14. RESULTS: FLOWS OUT OF EMPLOYMENT
Introduction Model Empirical Analysis Conclusion
RESULTS: FLOWS OUT OF EMPLOYMENT
Y vs. A
YF vs. AF
YFL vs. AFL
young*UMW1
0.002
0.004
0.008
(0.000)***
(0.001)***
(0.003)***
young*ZMW2
0.001
(0.000)
(0.001)
(0.005)
young
0.018
0.017
0.006
female
0.020
temporary
0.085
0.106
0.145
part-time
0.013
0.011
-0.001
(0.003)
Pseudo-R2
0.161
0.156
0.162
Observations
956432
396856
48789
Standard errors in parenthesis
Controls: gender, education, attended courses the last month, time (quarterly) effect, linear trend, region of residence, contract type, working day, industry, occupation, sector

15. RESULTS: FLOWS INTO EMPLOYMENT
Introduction Model Empirical Analysis Conclusion
RESULTS: FLOWS INTO EMPLOYMENT
Y vs. A
YF vs. AF
YFL vs. AFL
young*UMW1
0.002
0.001
0.009
(0.002)
(0.005)*
young*ZMW2
0.004
0.005
0.012
(0.003)*
(0.003)
(0.008)
young
0.040
0.047
0.034
(0.003)***
(0.009)***
female
-0.066
(0.002)***
active search
0.021
0.016
(0.006)***
Pseudo-R2
0.104
0.110
0.107
Observations
186191
112978
21081
Standard errors in parenthesis
Controls: gender, education, attended courses the last month, time (quarterly) effect, linear trend, region of residence, first job, search method

16. Introduction Model Empirical Analysis Conclusion
changes in MW are often predictable;
expected changes in MW are partly anticipated;
ex-post unemployment effect is lower in case of expected changes in MW;
 better to focus on unexpected ΔMW
MW increases separations but may increase participation  non significant employment effect

17. Introduction Model Empirical Analysis Conclusion

18. Introduction Model Empirical Analysis Conclusion
Wage Structure 2002
wages MW
edu