Quantitative Economics of Education

Transcription

1 MSc Economics Quantitative Economics of Education Winter 2010/2011 Instructor Prof. Süssmuth Time Location SR 7 Day Wed IEW Institute for Empirical Research in Economics #1< 1

2 Outline: Part I Introduction and motivation I. Investment in Human Capital 1. Investment decision and life-cycle model: Theory 2. Investment decision and life-cycle model: Empirics 3. Views on education besides classical HC Theory I 4. Views on education besides classical HC Theory II Midterm Exam QEE Prof. Bernd Süssmuth IEW Winter 2010/2011 #2< 2

3 I.2 Investment decision and life-cycle model: Empirics or how to validate implications from HC-Theory models? The classic approach: OLS specification of HC income function hypotheses (Mincer, 1974) QEE Prof. Bernd Süssmuth IEW Winter 2010/2011 #3< 3

4 Mincer Equation Estimates for 3 different countries obviously Source: Johnes (1993) QEE Prof. Bernd Süssmuth IEW Winter 2010/2011 #4< 4

5 Mincer Equation original AEPs from Mincer s seminal book: Source: Mincer (1974) QEE Prof. Bernd Süssmuth IEW Winter 2010/2011 #5< 5

6 Mincer Equation How to calculate the upper turning point of the income function? Differentiate with respect to years worked:. In the Johnes example above it lies between 10.5 and 22.5 years, that is, the peak is reached clearly before retirement age. QEE Prof. Bernd Süssmuth IEW Winter 2010/2011 #6< 6

7 Mincer Equation Ad hoc, 2 practical remarks - If X is not available (but S is), help yourself with X = age S 6 - It s all conditional estimates, example: university degrees estimating returns makes only sense in case of completing. = so called Heckman-Critique (James J. Heckman, Nobel 2000) QEE Prof. Bernd Süssmuth IEW Winter 2010/2011 #7< 7

8 I.2.1 Age-earnings profiles and Mincer-Regressions 2 principal methods to calculate rates of return on education (a) Algebraic method (b) Mincer-Regression Algebraic method Starting point: internal rate of return (IRR) QEE Prof. Bernd Süssmuth IEW Winter 2010/2011 #8< 8

9 Algebraic method Objective: Calculation of ROI for educational investment of level x Period of education starts now and takes S years (working life ends in T years) x = 1 e.g., primary education (completed typ. compulsory school) x = 2 e.g., secondary education (completed secondary school) K-12 x = 3 e.g., tertiary education (university degree) QEE Prof. Bernd Süssmuth IEW Winter 2010/2011 #9< 9

10 Algebraic method Definition The IRR is that value r that fulfills the following equation where Y x = income for an x-level of education C = costs of education t = time index (year) QEE Prof. Bernd Süssmuth IEW Winter 2010/2011 #10< 10

11 Algebraic method Special case cost of education = foregone earnings (exclusively!) QEE Prof. Bernd Süssmuth IEW Winter 2010/2011 #11< 11

12 Algebraic method Note: We can sharply discriminate private from social ROEs. private ROEs exclusively consider individual costs of education social ROEs imply costs for the society as a whole (financing education through taxes) typically, social ROE < private ROE QEE Prof. Bernd Süssmuth IEW Winter 2010/2011 #12< 12

13 Algebraic method Central drawbacks of this method Data intense Information on AEPs for all levels of education = required Query-based AEPs for individuals and small groups = often erratic Smoothing of such AEPs = (frequently) required Typical (private) ROE based on algebraic calculation: 10% Quantitatively returns on physical capital QEE Prof. Bernd Süssmuth IEW Winter 2010/2011 #13< 13

14 Algebraic method A rare example of the use of the algebraic methods = the works by Giora Hanoch (Jo Human Resources, 1967) Her seminal paper is entitled An Economic Analysis of Earnings and Schooling 2-tier approach 1. Construct AEPs for specific groups of the population 2. Compute IRR on the base of these AEPs QEE Prof. Bernd Süssmuth IEW Winter 2010/2011 #14< 14

15 Algebraic method Earnings by age and years of schooling (Hanoch, 1967) Whites / North Whites / South QEE Prof. Bernd Süssmuth IEW Winter 2010/2011 #15< 15

16 Mincer-Regressions A simple way to develop a testable Mincerian function w/ Y t C t r t = annual income for t years of education = total cost of investment in t-th year of education = return Assumption: QEE Prof. Bernd Süssmuth IEW Winter 2010/2011 #16< 16

17 Mincer-Regressions Back-tracing to period 0 (1 st non-compulsory year of schooling), gives. Applying natural logs (ln) and the standard approximation:, we get. QEE Prof. Bernd Süssmuth IEW Winter 2010/2011 #17< 17

18 Mincer-Regressions For an identical return over all years of education, this implies. Considering the impact of experience on income, we finally end up w/ the legendary Mincerian specification QEE Prof. Bernd Süssmuth IEW Winter 2010/2011 #18< 18

19 Mincer-Regressions Interpretation of (semi-log- or log-lin-specification) Increasing S by 1 yr, increases ln(y) by. NB, therefore, if ln(y) increases by this = an increase of Y by %. QEE Prof. Bernd Süssmuth IEW Winter 2010/2011 #19< 19

20 Mincer-Regressions A slightly more sophisticated version of a Mincerian income function w/ S 1, S 2, and S 3 denoting dummy (0/1) variables for which S 1 = 1 for a level of education of at least first grade; S 2 = 1 for a level of education of at least a high school grade; S 3 = 1 for a level of education of at least a college degree. QEE Prof. Bernd Süssmuth IEW Winter 2010/2011 #20< 20

21 Mincer-Regressions Example An individual who completed high school but not college is characterized by S 1 = 1, S 2 = 1, and S 3 = 0. Let N x = # yrsauf of education to attain level of education x the private ROE on primary education then is given by r 1 = b/n 1 secondary education then is given by r 2 = c/n 2 college education then is r 3 = d/n 3. QEE Prof. Bernd Süssmuth IEW Winter 2010/2011 #21< 21

22 Mincer-Regressions Advantages: Mincerian vs. algebraic method less data-intense Completely based on individual data (instead of grouped/aggregated data) Disadvantages Primarily suited to compute private ROEs Completely based on individual data (instead of grouped/aggregated data) QEE Prof. Bernd Süssmuth IEW Winter 2010/2011 #22< 22

23 Mincer-Regressions We should keep 3 caveats in mind 1. Education = (partially) a consumption good - fun-factor, bandwagon (business admin fad), political motivation - e.g., Angrist and Krueger (JASA, 1992): * run for universities during Vietnam War * 9% increase in # students enrolled for * Vietnam Era Draft Lottery : Prob(study) 5% higher for small no s QEE Prof. Bernd Süssmuth IEW Winter 2010/2011 #23< 23

24 Mincer-Regressions and also 2. incomplete labor markets wages may not reflect productivity 3. Signalling and Screening Mincerian approach Returns to Investment in Education in global perspective Psacharopoulos and Patrinos (EE, 2004) QEE Prof. Bernd Süssmuth IEW Winter 2010/2011 #24< 24

25 Mincer Regressions 3 major styilized facts (international perspective) ROEs w/ increasing level of education ROEs w/ increasing level of development private ROE > social ROE QEE Prof. Bernd Süssmuth IEW Winter 2010/2011 #25< 25

26 Mincer Regressions QEE Prof. Bernd Süssmuth IEW Winter 2010/2011 #26< 26

27 Mincer Regressions Overall women benefit more from education (in terms of returns), but QEE Prof. Bernd Süssmuth IEW Winter 2010/2011 #27< 27

28 Mincer Regressions Major problems of global comparative studies Sample selection and stratification 1. Developing countries: bias towards cities, major companies, etc. 2. Civil servants, soldiers, etc. sample etc. (wages market determined) Methodology questionable really same estimation strategy for all countries? QEE Prof. Bernd Süssmuth IEW Winter 2010/2011 #28< 28

29 Mincer Regressions Some further critical aspects Control variables = problematic in Mincer Regressions (e.g., occupation) Functional form: linear, quadratic, in parts linear, log? Sheepskin effects ( milestones after 12 and 16 yrs)? Selection models (labor market activity = endogenous decision) QEE Prof. Bernd Süssmuth IEW Winter 2010/2011 #29< 29

30 Mincer Regressions Some sample findings (German Labor Force Survey 2004) Experience-Income profiles (university, male) QEE Prof. Bernd Süssmuth IEW Winter 2010/2011 #30< 30

31 Mincer Regressions Some sample findings (German Labor Force Survey 2004) Experience-Income profiles (university, female) QEE Prof. Bernd Süssmuth IEW Winter 2010/2011 #31< 31

32 Mincer Regressions Some sample findings (German Labor Force Survey 2004) High school graduates w/o further vocational training (control group) QEE Prof. Bernd Süssmuth IEW Winter 2010/2011 #32< 32

33 Mincer Regressions Some sample findings (German Labor Force Survey 2004) High school graduates w/o further vocational training (control group) QEE Prof. Bernd Süssmuth IEW Winter 2010/2011 #33< 33

34 Mincer Regressions Some sample findings (German Labor Force Survey 2004) High school graduates w/o further vocational training (control group) QEE Prof. Bernd Süssmuth IEW Winter 2010/2011 #34< 34

35 Mincer Regressions Some sample findings (German Labor Force Survey 2004) High school graduates w/o further vocational training (control group) QEE Prof. Bernd Süssmuth IEW Winter 2010/2011 #35< 35