1. 1 Cognitive Skills: Determinants and Labour Market Outcomes W. Craig Riddell University of British Columbia WISE conference Xiamen, China December 12-13, 2008

2. 2 Introduction Most research on human capital uses indirect measures like education and work experience These are inputs into the production of human capital, not outcomes -- skills, competencies and knowledge Relatively little is known about the relationship between direct measures of skills and labour market outcomes Advances in data collection allow us to explore these linkages

3. 3 Presentation draws on research using Canadian surveys IALS 1994 and IALSS 2003 Data combine methods of educational testing with household survey techniques In IALSS 2003, skills were assessed in four domains: –Prose literacy, Document literacy, Numeracy, Problem-solving Key feature: these are skills used in daily activities, not ability measures Provide measures of the skills of the adult population

4. 4 eg: Document literacy: skills required to find and use information contained in text formats such as job applications, payroll forms, bus schedules, maps, graphs Numeracy questions changed considerably between 1994 and 2003 Problem solving only assessed in 2003 survey Prose and document literacy: about 45% of questions were identical in two surveys Scores in remaining questions were rescaled to match differences observed in identical questions We treat prose and document scores as being comparable in the two years

5. 5 Sample sizes: IALS 94 5660 IALSS 03 23038 Sample restrictions: Drop immigrants, aboriginals, students, those with missing information on education Resulting sample sizes: IALS 94 3964 IALSS 03 14666 Earnings sample drops self-employed, unemployed, non- participants, wage outliers Correlation among test scores: –Prose and Document.96 –Document and Numeracy.92 –Document and Problem solving.92 Factor analysis: one principal component placing essentially equal weight on all four scores

6. 6 Determinants of skills I Simple heuristic model Individuals start life with ability and parental resources These plus formal schooling influence cognitive skills before legal school leaving age After that age students decide whether to remain in school; more skilled individuals face lower costs After HS, continuation depends on individual choice and admission decision; both depend on cognitive skills Thus cognitive skills and schooling are jointly determined

7. 7 Determinants of skills II Table 3 reports log skills regressions Small gender difference Essentially no relationship between skills and age in cross-section (e.g. impact of 1 year at age 30 = - 0.1%) G&R (2003) also found no relationship between literacy skills and age or experience with IALS 94 data Strong relationship with formal schooling, but concave Suggests skills acquired via parenting and schooling, ‘locked in’ thereafter

8. 8 Possible omitted variables bias Literacy skills and education may be influenced by ability Ideal control would be IQ type measure at a young age Columns 2 and 3 add proxies for unobserved ability Parental education only matters if parents dropouts (OLS2) Other parental influences (occupation, immigrant, working) minor Ease of learning mathematics in high school (OLS 3) Modest decline in coefficient on schooling (< 10%) OLS and IV estimates (columns 4 & 5): IV > OLS IV is province in which respondent attended HS interacted with age Regressions include controls for current province of residence

9. 9 Table 3: Log of Document Literacy Regressions VariableOLS 1OLS 2OLS 3OLS 4IV Female-0.014**-0.012**-0.0083*-0.012**-0.016** (0.0046)(0.0045) (0.0044)(0.0051) Years of Schooling0.058***0.054***0.053***0.024***0.052*** (0.0053)(0.0049)(0.0048)(0.0008)(0.013) Schooling Squared-0.0011*** -- (0.0002) -- Age0.0035***0.0064***0.0066***(0.0068)***-0.0003 (0.0008) (0.0036) Age Squared-0.0076***-0.0099***-0.011*** -0.0029 (0.0009) (0.0039) Mother’s Education Less than High School--0.037***-0.038***-0.028***0.0007 -(0.0059)(0.0058) (0.016) Some Post Secondary--0.0077-0.0084-0.0046-0.020* -(0.0067)(0.0066)(0.0065)(0.011) BA or More-0.00940.00750.0076-0.0050 -(0.0102)(0.0098)(0.010)(0.016) None Reported--0.067***-0.069***-0.048***-0.0047 -(0.0123)(0.0122)(0.011)(0.024)

10. 10 Table 3: Log of Document Literacy Regressions continued VariableOLS 1OLS 2OLS 3OLS 4IV Father’s Education Less than High School--0.032***-0.030***-0.029***-0.008 -(0.0067) (0.012) Some Post Secondary-0.00550.0060.0062-0.008 -(0.0073)(0.0071)(0.0073)(0.012) BA or More-0.01260.0154*0.011-0.025 -(0.0083)(0.0082) (0.020) None Reported--0.056***-0.055***-0.065***-0.021 -(0.012) (0.011)(0.023) Immigrant Mother-0.00560.0053-0.0009-0.013 -(0.0077)(0.0076)(0.0077)(0.011) Immigrant Father-0.015**0.016**0.0054-0.0084 -(0.0071)(0.007)(0.0071)(0.011) Good Math Grades-0.027*** - -(0.0055) - Teachers Too Fast--0.026*** - -(0.0057) - Constant5.08***5.09*** 5.29***5.04*** (0.04)(0.038) (0.020)(0.12) Observations146661452713868 R-squared0.490.510.520.470.31

11. 11 Age and cohort effects I Flat skills - age profile in cross-section may reflect age and cohort effects e.g. 35 year old in 2003 may differ from 25 year old in 2003 both because she is older and comes from an earlier cohort Use IALS94 and IALSS03 to create synthetic cohorts e.g. observe skills of 26-35 year olds in 1994 and 35-44 year olds in 2003 Each is a random sample of 1959-1968 birth cohort Also examine cohorts by education To ensure education doesn’t change, focus on individuals over age 25

12. 12 Age and cohort effects II Cohorts defined for age ranges: –26 to 35 in 1994(35 to 44 in 2003) –36 to 45 in 1994(45 to 54 in 2003) –46 to 55 in 1994(55 to 64 in 2003) –56 to 65 in 1994 (65 to 74 in 2003) –65+ in 1994(74+ in 2003)

13. 13 Age and cohort effects III First compare the same age groups in 1994 and 2003, shows cohort effects holding age constant e.g. 26-35 year olds in both years: 10th percentile increases from 223 to 248; 90th percentile declines from 363 to 354; median increases from 299 to 306 Breakdowns by education group: –dropouts: 10th percentile increases from 135 to 172 –college & university grad: 90th percentile drops by 10% Within cohort movements: –26 to 35 year olds in 1994: 10th percentile rises form 223 to 232, 90th declines from 363 to 344

14. 14 Figure 2: Document Literacy, Age 26-35

15. 15 Figure 3: Document Literacy, Age 36-45

16. 16 Figure 4: Document Literacy, Age 46-55

17. 17 Figure 5: Document Literacy, Age 56-65

18. 18 Figure 6: Document Literacy, less than high school

19. 19 Figure 7: Document Literacy, high school

20. 20 Figure 8: Document Literacy, post-secondary non-university

21. 21 Figure 9: Document Literacy, university

22. 22 Figure 10: Document Literacy, age 26 to 35 in 1994

23. 23 Figure 11: Document Literacy, age 36 to 45 in 1994

24. 24 Figure 12: Document Literacy, age 46 to 55 in 1994

25. 25 Figure 13: Document Literacy, age 56 to 65 in 1994

26. 26 Figure 14: Document Literacy, less than high school, age 26 to 45 in 1994

27. 27 Figure 15: Document Literacy, high school, age 26 to 45 in 1994

28. 28 Figure 16: Document Literacy, university, age 26 to 45 in 1994

29. 29 Table 4: Pooled Regressions Including Cohort Effects VariableOLS10th QuantileMedian90th Quantile Years School0.083***0.13**0.086***0.043*** (0.0067)(0.0052)(0.0031)(0.0028) School Squared-0.002***-0.0033***-0.0021***-0.0009*** (0.0002) (0.0001) Female-0.0087-0.0064-0.016-0.0062* (0.0058)(0.0083)(0.0049)(0.0036) Age Group 36-45-0.024*-0.0074-0.018-0.050*** (0.013)(0.018)(0.011)(0.0083) 46-55-0.050**0.0034-0.048***-0.12*** (0.016)(0.025)(0.015)(0.011) 56-65-0.09***-0.084**-0.099***-0.17*** (0.021)(0.031)(0.019)(0.014) 65+-0.19***-0.18*** -0.30*** (0.033)(0.039)(0.026)(0.016) Cohort 20.018*-0.0130.018*0.055*** (0.010)(0.018)(0.0097)(0.0078) Cohort 30.032**0.0310.042**0.082*** (0.016)(0.025)(0.016)(0.012) Cohort 40.053**0.052*0.055**0.13*** (0.029)(0.032)(0.023)(0.014) Cohort 5-0.010-0.053-0.0040.11*** (0.033)(0.039)(0.025)(0.015) Constant4.97***4.39***4.98***5.48*** (0.054)(0.047)90.025)(0.021) Observations14734

30. 30 Table 5: Pooled Quantile Regressions with Cohort Effects, By Education Group HighSchoolor LessUniversity Variable10th QuantileMedian90th Quantile10th QuantileMedian90th Quantile Female0.064***-0.011-0.0096**-0.021**-0.00460.0060 (0.013)(0.0049)(0.0047)(0.010)(0.0069)(0.0084) Age Group 36-450.025-0.037***-0.027**-.064**0.0075-0.090* (0.021)(0.01)(0.012)(0.027)(0.012)(0.050) 46-55 0.019-0.11***-.074*-0.021-0.12*** (0.035)(0.011)(0.015)(0.042)(0.015)(0.022) 56-65-0.032-0.11***-0.12***-0.18***-0.092***-0.15*** (0.068)(0.016)(0.024)(0.045)(0.017)(0.026) 65+-0.017-0.28***-0.24***-0.31***-0.16***-0.15*** (0.12)(0.024)(0.027)(0.093)(0.032)(0.038) Cohort 20.00530.038***0.061***0.012-0.0250.0071 (0.013)(0.010)(0.0094)(0.024)(0.017)(0.009) Cohort 3-0.0530.0150.046**0.0430.0150.018 (0.053)(0.017)(0.016)(0.032)(0.021)(0.018) Cohort 4-0.23***0.0190.044*0.0850.0380.011 (0.068)(0.021)(0.025)(0.085)(0.039)(0.023) Cohort 5-0.34***-0.0270.042**0.120.0071-0.12*** (0.11)(0.027)(0.024)(0.11)(0.041)(0.035) Constant5.45***5.76***5.87***5.73***5.80***5.99*** (0.027)(0.011)(0.014)(0.032)(0.017)(0.015) Observations9209 2163

31. 31 Implications Cognitive skills decline with age Less evidence of decline at bottom of skill distribution, but strong negative age effects at the 90th percentile Literacy declines across successive cohorts, particularly at top of distribution Absence of relationship between cognitive skills and age in cross-section results from offsetting age and cohort effects

32. 32 Skills and earnings: interpretive framework Simple framework builds on Green and Riddell (2003) who use the 1994 IALS to examine the role of cognitive skills in Canadian earnings patterns. Hedonic model in which earnings are determined by the skills an individual possesses and the prices of those skills.

33. 33 Abstracting from other influences on earnings, individual earnings are a function of the skills an individual possesses and puts into use: E i = f (G 1i, G 2i, G 3i ) + e i (1) where E i are earnings for individual i, G ki is the amount of skill k that person i sells in the market, and e i is a disturbance term that is independent of the skills.

34. 34 Typically, we do not observe the skills but we do observe some of the inputs that generate them. They enter via skill production functions: G ki = h k (edn i, exp i, v ki ) (2) where k indexes the skill type, edn corresponds to education, exp is years of work experience and v k is an ability specific to the production of the kth skill.

35. 35 If we do not observe the G ki 's directly, we can obtain an estimating equation by substituting equation 2) into 1). This yields a reduced form specification for earnings as a function of education and experience. The coefficient on a covariate such as education reflects the combination of how that input contributes to production of each skill and how those skills contribute to earnings.

36. 36 What can be learned when only some skills are observed? If we observe the cognitive skills G 1, we get a quasi- reduced form earnings regression that includes G 1 (the vector of cognitive skills), experience and education. The quasi-reduced form parameters on edn and exp now reflect the impact of education and experience on the production of skills other than the observed cognitive skills.

37. 37 Table 7: Earnings Regressions OLS 1OLS 2IV 1IV 2 Female-0.41***-0.40*** (0.024) Years of School0.087***0.069***0.070***0.050*** (0.0042)(0.0047)(0.013)(0.017) Experience0.067*** 0.068*** (0.0035) (0.0041)(0.004) Experience Squared-0.0011*** -0.0012*** (0.0001) Average Skill Score-0.0026***-0.0032*** -(0.0003)-(0.0007) Constant4.80***4.28***5.04***4.34*** (0.077)(0.10)(0.16)(0.10) Observations7768 R-squared0.380.40.380.39

38. 38 Cognitive skills and earnings Introducing cognitive skills reduces schooling coefficient by 20% (OLS) to 28% (IV). Earnings increase with age and experience. Since cognitive skills decline with age, non-cognitive skills must increase at a rapid rate Impact of cognitive skills on earnings is substantial. A 25 point increase in skills (half a standard deviation) is associated with an earnings increase equivalent to 1 extra year of schooling

39. Quantile regressions Table 8 shows estimates at 10 th, 25 th, 50 th, 75 th, 90 th quantiles Returns to schooling and experience decline across quantiles Coefficient on skills approximately constant across quantiles Implies cognitive skills do not interact with other (unobs) skills or attributes in generating earnings 39

40. 40 Conclusions Conventional estimates of returns to education and experience confound two effects: –Skill production effect –Market valuation of skills effect Cognitive skills increase strongly with schooling Parental education has an impact, but not as large as might be expected Literacy & numeracy skills decline with age after leaving school, particularly at the top of the distribution Cognitive skills have been declining across cohorts, particularly at the top of the distribution

41. Conclusions Absence of relationship between age and skills in cross section appears due to offsetting age and cohort effects Cognitive skills account for about 20% of the returns to schooling (IV estimates suggest more) Literacy and numeracy skills have substantial effects on earnings 41