Is there a signal from education? Evidence from Polish LFS data.

Wojciech Hardy*

*Big thanks to: Joanna Tyrowicz, Stanisław Cichocki and my colleagues at GRAPE

Presentation plan

1. The theory of signaling

2. Why even bother?

3. The empirical literature

4. My study

5. Conclusion and ideas for the future.

The theory of signaling

• Spence, 1973, Quarterly Journal of Economics, Volume 87, Issue 3:

• „To hire someone is frequently to purchase a lottery. (…) the employer cannot directly observe the marginal product prior to hiring. What he does observe is a plethora of personal data in the form of observable characteristics and attributes of the individual, and it is these that must ultimately determine his assessment of the lottery he is buying.”

You’re a non-discriminating owner of a banking company. Consider three situations – who would you hire?

The theory of signaling, Spence (1973)

A) • Female • Age 25 • Graduated in Psychology • No previous experience

B) • Female • Age 25 • Secondary school • No previous experience

A) • Male • Age 26 • Graduated in Economics in 2012 • 2 years of previous experience • Finished a music school in 2012

B) • Male • Age 26 • Graduated in Economics in 2012 • 2 years of previous experience

A) • Male • White • Age 26 • Graduated in Economics in 2012 • 2 years of previous experience

B) • Female • White • Age 26 • Graduated in Economics in 2012 • 2 years of previous experience

The theory of signaling, Spence (1973)

A) • Female • Age 25 • No previous experience • Graduated in Psychology

B) • Female • Age 25 • No previous experience • Secondary school

Situation 1

1. Signaling induces costs. These are related to the individual’s skills.

2. Individual A reports being able to „afford” finishing studies.

3. He signals being more productive.

Signal

Indices

The theory of signaling, Spence (1973)

A) • Male • Age 26 • Graduated in Economics in 2012 • 2 years of previous experience • Finished a music school in 2012

B) • Male • Age 26 • Graduated in Economics in 2012 • 2 years of previous experience

Situation 2

1. It is impossible to assess the productivity with a 100% certainty.

2. Individual A reports having done more in 2012.

3. He signals being more productive.

Signal

The theory of signaling, Spence (1973)

A) • Male • White • Age 26 • Graduated in Economics in 2012 • 2 years of previous experience

B) • Female • White • Age 26 • Graduated in Economics in 2012 • 2 years of previous experience

Situation 3

1. If I don’t discriminate, then A=B, right?

2. Over time, there’s a self-regenerating equilibrium, which changes

incentives based on the distribution of education.

3. The COSTS might vary based on indices if others discriminate.

4. Therefore the signal is there, based on the state of the market.

Signal ?

Where’s the signal?

Why even bother?

If a signal of education exists

1. There’s a bias on all estimates of education’s role in productivity, employment and entry wages.

2. Inequalities in education levels affect the efficiency of employer-worker matching.

Empirical literature

• Required skills versus necessary skills – self-report studies (e.g. Dolton & Vignoles, 2000; Chatterji et al., 2003).

– Findings regarding differences in signaling for men and women.

• Natural experiments – education reforms (e.g. Lang & Kropp, 1986; Chevalier et al., 2004).

• The self-regenerating cycle and the distribution of education (Kroch & Sjoblom, 1994; Rosenbaum, 2000).

– The Ranks.

The idea The education distribution can be used as an identification instrument for signal’s strength.

Besides education level, the following are important:

• The Rank (i.e. the place in the distribution of education)

• Equals (i.e. how much our education level sets us apart from others)

• Group size (i.e. how many similar people are there on the job market)

The data

• Polish Labour Force Survey.

• Self-reported flow information.

• Years: 2004-2012.

– (prior no information on fields of study).

• Representative of the whole population.

– Both an advantage and a necessity.

Education level patterns across years (24-26 year olds)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2004 2005 2006 2007 2008 2009 2010 2011 2012

Tertiary

High school

High school vocational

Vocational

Elementary

Overrepresentation of occupations after particular studies

Field of study

Occupation

Management Specialists Technicians Office

workers Sales and services

Farmers Artisans Machine operators

Low-skilled

Engineering & construction

+ + + +

Pedagogy + + + +

Humanities + + +

Science + + + +

Social sciences + + + +

General + + + +

Agriculture, etc. + + +

Services + + + +

Health & care + + +

Signal variables construction

• The groups: – voivodship, – age groups (but most concentrated below 30), – field of study, – gender, – year of the survey, – industry (for the wage equation).

• The signaling variables: – The Rank = Lower education in group / Whole group – The Equals = Equal education in group / Whole group – The Group Size = Whole group

The results

Prob(working) All

Engineering

&

construction

Pedagogy Humanities Science Social

science Agriculture Services

Health &

care

Age 0.002 0.004 0.008 0.009 0.006 0.001 0.004 0.003 0.010

Gender (1=Female) -0.054*** -0.160*** -0.013 0.003 -0.113*** 0.015 -0.107** -0.073*** -0.030

Highschool

vocational 0.018 0.087*** -0.178 0.048 0.068 0.100** -0.024 0.081** 0.006

Highschool 0.018 0.219*** 0.181 0.155 0.079 0.056 0.120**

Tertiary 0.091*** 0.213*** -0.071 0.291 0.221 0.180*** 0.096 0.151** 0.172***

Rank (% of lower

educ.) 0.006 -0.126 0.225 -0.163 -0.070 -0.031 -0.049 -0.160 -0.128

Equals (% of equal

educ.) 0.077*** 0.124** 0.085 0.037 0.131** 0.089** 0.211** 0.028 0.099

Group size 0.000*** -0.000 0.000 0.002 -0.001** -0.000 0.003 0.000 -0.000

N 32,292 6,485 1,305 1,312 2,461 6,458 970 3,616 1,206

Probit mfx by fields of study

** p<0.05; *** p<0.01

Highschool

vocational 0.020 0.025 -0.283 0.040 0.051 0.080 0.014 0.037 0.016

Highschool 0.013 0.118** 0.161 0.108 0.042 0.006 0.035

Tertiary 0.084*** 0.089*** -0.008 0.208 0.161 0.140*** 0.021 0.047 0.129***

No signal controls:

** p<0.05; *** p<0.01

dummies for years and fields of study (where appropriate) included but not reported

Additional: Wage regression

Wage regression Last-year

students

Change

observed

Last-year

students +

Signal

Change

observed +

Signal

(Male) Change

observed +

Signal

(Female)

Change

observed +

Signal

Age 24.908*** 17.542*** 25.357*** 18.531*** 9.689* 28.918***

Gender (1=Female) -230.618*** -198.788*** -227.299*** -192.672***

Vocational 226.033*** 306.456*** 239.467*** 321.845*** 345.702*** 278.870***

High school vocational 269.447*** 329.715*** 284.125*** 343.861*** 394.376*** 254.220***

High school 280.745*** 302.565*** 293.666*** 313.863*** 342.596*** 248.866***

Tertiary 447.067*** 465.818*** 465.321*** 479.426*** 646.140*** 318.747***

Rank (% of lower

educ.) -10.141 19.797 -22.415 51.972*

Equals (% of equal

educ.) -76.465*** -133.679*** -190.577*** -49.548

Group size -0.001*** -0.001*** 0.001 -0.001***

Constant 1.213 -17.133 40.273 34.378 193.791 -346.045***

R2 0.24 0.26 0.24 0.26 0.27 0.25

N 7,575 3,467 7,575 3,467 1,520 1,947

* p<0.1; ** p<0.05; *** p<0.01

dummies for years and fields of study included but not reported

Conclusions

• No observable signal in education, when determining employment. – Possible bias due to unobservable real job-search.

– Possible bias due to overeducation.

– Large heterogeneity between fields of studies.

• Some evidence of signal in education, when determining first wages.

• Differences between genders (reinforcing the findings of Chatterji et al., 2003)

Ideas for the future

• Combining the LFS dataset with Household Survey and calculating the Heckman selection equation.

• Comparison with a longer period of data (at the expense of the ’field of study’ variable).

• Cross-country analysis.

• Deeper analysis of the signal differences between genders.

THANK YOU!

(all comments welcome)