Understanding ‘Sheepskin Effects’ in the Returns to Education: The Role of Cognitive Skills W. Craig Riddell University of British Columbia CLSRN Workshop University of Toronto November 18-19, 2008 1
Objective • To investigate the role of cognitive skills in “sheepskin effects” in impacts of education • Sheepskin effects refer to outcomes associated with the completion of a diploma or degree, controlling for years of schooling • Credential effects can be interpreted as the value of program completion -- the difference in outcomes between those with a diploma or degree and non-completers with the same years of schooling • Study uses measures of prose literacy, document literacy, numeracy and problem solving 2
Why sheepskin effects may matter I • Completion of educational programs is associated with noteworthy changes in outcomes • Earnings: discrete jumps in earnings associated with credentials • Crime: substantial drop in criminal activity associated with HS graduation (Figure 1) • Re-employment success: discrete jumps in re-employment probabilities at 12 and 16 years of schooling (Figures 2 and 3) 3
Figure 1. Regression-Adjusted Probability of Incarceration, by Years of Schooling 4
Figure 2. Regression-Adjusted Probability of Re-employment Conditional on Being Unemployed for More than 12 Weeks in the Previous Year by Years of Schooling Data source: 1980 Census merged with compulsory schooling laws and child labor laws Number of observations: 204,853 Note: Regression-adjusted probability of re-employment is obtained by conditioning on state of birth, state of residence, gender, race, and cohort of birth (1916-1925, 1936-1945, etc.). The graphs display the coefficient estimates on the complete set of schooling dummies. The intercept applies to the base category – white males who were born in California between 1936 and 1945, had eight years of schooling or less, lived in California at the time of the survey. 5
Figure 3. Regression-Adjusted Probability of Re-employment Conditional on Being Unemployed in the Previous Year Data source: Canadian Census (1981-2001) Number of observations: 458,641 Note: Regression-adjusted probabilities of re-employment were obtained by conditioning on survey year, province/territory, CMA (Toronto, Montreal, Vancouver, or other CMA), nine age groups (age 20- 24, 25-29, and so on), gender, marital status, census family size, and language. The graphs display the coefficient estimates on the complete set of schooling dummies. The intercept applies to the base category -- males surveyed in 1981 who were 35 to 39 years of age, had eight years of schooling or less, were married, lived in a CMA other than Toronto, Montreal, or Vancouver, lived in Ontario, and only spoke English at the time of the survey. 6
Why sheepskin effects may matter II • Help to understand economic returns to education • Most common specification (human capital earnings function) is linear in years of schooling • According to this model each year of school raises earnings by the same proportion • Alternative popular specification uses highest level of education • This amounts to a step function in which only credentials matter • Sheepskin specification nests both years of schooling and program completion within a more general model (see Figure 4) 7
Figure 4. Graphic representations of Five Models of the Economic Returns to Education Linear/Credentialist Credentialist Linear Ln(Earnings) Ln(Earnings) Ln(Earnings) 0 Elementary School 8 High School 12 College 16 Grad 18 0 Elementary School 8 High School 12 College 16 Grad 18 0 Elementary School 8 High School 12 College 16 Grad 18 Educational Attainment Educational Attainment Educational Attainment Source: Goodman, Jerry D. “The Economic Returns of Education: An assessment of Alternative Models” Social Science Quarterly 60 (September 1979) pp. 269-283 8
Why sheepskin effects may matter III • Relevant to how we measure human capital over time or across jurisdictions/countries • Most international comparisons are credential-based • Years of schooling may be more comparable measure of educational inputs • Census and household surveys have been moving away from years of schooling 9
Why sheepskin effects may matter IV • May help distinguishing among alternative theories of education • Layard and Psacharopoulos (1974) dismiss signaling theory on basis of absence of sheepskin effects in their data • However, evidence not decisive • Sheepskin effects consistent with human capital theory if package of complementary educational inputs matter • Also consistent with both signaling/screening and human capital views is graduates are more skilled than non-graduates 10
Why sheepskin effects may matter V • Education policy often focuses on program completion • Many initiatives to encourage high school completion • Recent concern that many university enrollees do not graduate • If only years of schooling matter, program completion irrelevant 11
Previous literature • Layard and Psacharopoulos (JPE 1974) crude comparison of dropouts and grads • Hungerford and Solon (REStat 1987) linear spline with nodes at S = 8, 12, 16, 17, 18 � Find SE at S = 16 (largest) and S = 12. � Don’t observe program completion • Jaeger and Page (REStat 1996) match respondents to old and new CPS � Find years of study an imperfect measure of program completion � Estimated sheepskin effects much larger when program completion observed � EG HS grad: 11% vs 3% with HS (1987) specification � EG College grad: 31% vs 12% with HS (1987) specification • Park (1999) reports similar results • Ferrer and Riddell (CJE 2002) use Canadian census data � Estimated sheepskin effects for Canadian born: – HS grad 4%-6% – PS without HS 6% – PS with HS 3%-5% – Univ BA 21% – Univ MA 7%-10% – Univ Professional 30% 12
Data: IALSS 2003 • Several key advantages • Direct measures of cognitive skills • Information on years of completed schooling and highest level of education • Separate question on high school graduation • Large sample 13
Cognitive skills measures • Four skills assessed: prose literacy, document literacy, numeracy, problem solving • Tests assess ability to apply skills in everyday settings • Results presented for average of four skills 14
Sample restrictions • Full sample: focus on Canadian born, non-aboriginal population • Drop those whose main activity is “student” • Worker sample: drop self-employed, unemployed, non-participants, wage outliers • Sample sizes: Main sample 14,637 Worker sample 7,766 15
Educational attainment Six main categories: � Less than HS � HS graduate � Non-university post-secondary without high school completion � Non-university post-secondary with high school completion � University bachelor’s degree � University postgraduate and professional degree 16
Table 1 Summary Statistics for the Full Sample Males Females Both Genders Age 44 45.8 44.9 Experience 25.1 26.9 26 Years of Schooling 12.9 13 12.9 Educational Attainment % Less than High School 26.5 23.1 24.7 % High School 30.5 33.7 32.1 % Non Univ PS w/o HS 3.1 1.9 2.5 % Non Univ PS with HS 21.9 24 23 % University BA 12.7 13.6 13.2 % Univ Postgrad 5.3 3.7 4.5 Mother ’ s education % Less than High School 41.2 48.3 44.8 % High School 29 23.4 26.1 % Some Post Secondary 12.7 15.2 14 % BA or higher 8.2 6.8 7.5 % None reported 8.9 6.3 7.6 Father ’ s education % Less than High School 47 50.6 48.8 % High School 20.3 18.9 19.6 % Some Post Secondary 12 11.2 11.6 % BA or higher 11.7 10.5 11.1 % None reported 9 8.8 8.9 Immigrant Parents % Immigrant mother 16.4 15.6 16 % Immigrant father 19.4 17.9 18.6 Math in high school % Good math grades 70.8 66 68.3 % Teachers went too fast 21.4 30.8 26.2 Prose Literacy 278.1 282.9 280.6 Document Literacy 280.6 275.8 278.2 Numeracy 277.4 261.2 269.2 Problem Solving 273.8 273 273.4 Average Skill Score 277.5 273.2 275.3 17 Number of Observations 6693 7944 14637
Are graduates more skilled? • Investigate whether sheepskin effects in generation of skills • Figures 1 and 2 show plots of skills versus years of schooling • No obvious discrete jumps at S = 12 or S = 16 18
Figure 1 Cognitive skills by years of schooling 19
Figure 2 Average skill score by average years of schooling 20
Skills generation (log skills regressions: Table 2) • Small gender difference • Essentially no relationship between skills and age in cross-section • Strong relationship with education, but diminishing returns • Impact of one extra year of S: @ S = 12 3.2% @ S = 16 2.1% • Moderately large sheepskin effects (column 3) • Column 4 adds controls for parental education and parental immigrant status 21
Recommend
More recommend
