1 Cognitive Skills: Determinants and Labour Market Outcomes W. Craig Riddell University of British Columbia RCEA Labour Workshop Rimini, Italy August, 2009

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 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 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 Sample sizes: IALS IALSS NB sample: drop immigrants, aboriginals, students, those with missing information on education Resulting sample sizes: IALS IALSS Earnings sample drops self-employed, unemployed, non- participants, wage outliers Sample size IALSS 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

Determinants of skills Motivating questions: Role of parental inputs, formal schooling, age and work experience in developing and maintaining cognitive skills Evidence of “use it or lose it” feature? Why no improvement in average literacy skills between mid-1990s and mid-2000s?

7 Determinants of skills 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

8 Skill determinants 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 Barrett (2009) finds similar result for Australia Strong relationship with formal schooling, but concave Suggests skills acquired via parenting and schooling, ‘locked in’ thereafter

9 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

10 Table 3: Log of Document Literacy Regressions VariableOLS 1OLS 2OLS 3OLS 4IV Female-0.014**-0.012** *-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 *** -- (0.0002) -- Age0.0035***0.0064***0.0066***(0.0068)*** (0.0008) (0.0036) Age Squared *** ***-0.011*** (0.0009) (0.0039) Mother’s Education Less than High School ***-0.038***-0.028*** (0.0059)(0.0058) (0.016) Some Post Secondary * -(0.0067)(0.0066)(0.0065)(0.011) BA or More (0.0102)(0.0098)(0.010)(0.016) None Reported ***-0.069***-0.048*** (0.0123)(0.0122)(0.011)(0.024)

11 Table 3: Log of Document Literacy Regressions continued VariableOLS 1OLS 2OLS 3OLS 4IV Father’s Education Less than High School ***-0.030***-0.029*** (0.0067) (0.012) Some Post Secondary (0.0073)(0.0071)(0.0073)(0.012) BA or More * (0.0083)(0.0082) (0.020) None Reported ***-0.055***-0.065*** (0.012) (0.011)(0.023) Immigrant Mother (0.0077)(0.0076)(0.0077)(0.011) Immigrant Father-0.015**0.016** (0.0071)(0.007)(0.0071)(0.011) Good Math Grades-0.027*** - -(0.0055) - Teachers Too Fast *** - -(0.0057) - Constant5.08***5.09*** 5.29***5.04*** (0.04)(0.038) (0.020)(0.12) Observations R-squared

12 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 year olds in 1994 and year olds in 2003 Each is a random sample of birth cohort Also examine cohorts by education To ensure education doesn’t change, focus on individuals over age 25

13 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)

14 Age and cohort effects III First compare the same age groups in 1994 and 2003, shows cohort effects holding age constant year olds in both years (Fig 2): –10th percentile increases from 223 to 248; –90th percentile declines from 363 to 354; –little change in median (increases from 299 to 306) year olds in both years (Fig 3): similar except improvement at bottom smaller, decrease at top larger Fig 4 (46-55 age group): higher skills across distribution

15 Figure 2: Document Literacy, Age 26-35

16 Figure 3: Document Literacy, Age 36-45

17 Figure 4: Document Literacy, Age 46-55

18 Figure 5: Document Literacy, Age 56-65

Cohort effects by education Breakdowns by education group: –HS dropouts (Fig 6): 10th percentile increases from 145 to 175 –HS grads (Fig 7): worsening at top, smaller improvement at bottom than dropouts –College & University grads (Fig 8 & 9): worsening at top evident e.g. Univ grads 90th percentile drops from 390 to 360 –Summary: improvements at bottom most evident in HS dropouts, worsening at top most evident among PSE grads

20 Figure 6: Document Literacy, less than high school

21 Figure 7: Document Literacy, high school

22 Figure 8: Document Literacy, post-secondary non-university

23 Figure 9: Document Literacy, university

Age effects Within cohort movements: –26 to 35 year olds in 1994 (Fig 10): worsening at top of distribution, modest improvement at bottom –10th percentile rises from 223 to 232, 90th declines from 363 to 344 –36 to 45 year olds in 1994 (Fig 11): similar pattern –46 to 55 year olds in 1994 (Fig 12): modest improvements at both top and bottom

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

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

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

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

Age effects by education Figs for cohorts aged in 1994 HS dropouts: deterioration of skills at top, improvement below 10th percentile HS grads: deterioration at top and above 20th percentile, about the same at bottom Univ grads: deterioration at top, similar below median Summary: decline in higher level skills for all education groups over time

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

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

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

Pooled regressions All specifications control for parental education and immigrant status Flat age profile in cross-section reflects declining literacy with age and greater skills among earlier cohorts Schooling effects under-estimated in cross- section Impact of schooling declines across quantiles Literacy declines more with age at top of distribution Cohort effects evident throughout but stronger at top of distribution

34 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*** *** *** *** (0.0002) (0.0001) Female * (0.0058)(0.0083)(0.0049)(0.0036) Age Group * *** (0.013)(0.018)(0.011)(0.0083) ** ***-0.12*** (0.016)(0.025)(0.015)(0.011) ***-0.084**-0.099***-0.17*** (0.021)(0.031)(0.019)(0.014) ***-0.18*** -0.30*** (0.033)(0.039)(0.026)(0.016) Cohort 2 (36-45 in 1994)0.018* *0.055*** (0.010)(0.018)(0.0097)(0.0078) Cohort 3 (46-55 in 1994)0.032** **0.082*** (0.016)(0.025)(0.016)(0.012) Cohort 4 (56-65 in 1994)0.053**0.052*0.055**0.13*** (0.029)(0.032)(0.023)(0.014) Cohort 5 (>65 in 1994) *** (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

Pooled regressions by education For each education group, top end of literacy distribution declines more with age than bottom For HS or less group, no aging effect at lowest quantiles, i.e. little loss of basic skills with age For univ grads, strong declines with age across distribution

36 Table 5: Pooled Quantile Regressions with Cohort Effects, By Education Group HighSchoolor LessUniversity Variable10th QuantileMedian90th Quantile10th QuantileMedian90th Quantile Female0.064*** **-0.021** (0.013)(0.0049)(0.0047)(0.010)(0.0069)(0.0084) Age Group ***-0.027**-.064** * (0.021)(0.01)(0.012)(0.027)(0.012)(0.050) ***-.074* *** (0.035)(0.011)(0.015)(0.042)(0.015)(0.022) ***-0.12***-0.18***-0.092***-0.15*** (0.068)(0.016)(0.024)(0.045)(0.017)(0.026) ***-0.24***-0.31***-0.16***-0.15*** (0.12)(0.024)(0.027)(0.093)(0.032)(0.038) Cohort 2 (36-45 in 1994) ***0.061*** (0.013)(0.010)(0.0094)(0.024)(0.017)(0.009) Cohort 3 (46-55 in 1994) ** (0.053)(0.017)(0.016)(0.032)(0.021)(0.018) Cohort 4 (56-65 in 1994)-0.23*** * (0.068)(0.021)(0.025)(0.085)(0.039)(0.023) Cohort 5 (>65 in 1994)-0.34*** ** *** (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) Observations

37 Main Findings Prose and document literacy skills decline with age after formal schooling Less evidence of decline at bottom of skill distribution, but strong negative age effects at the top Literacy has declined across successive cohorts, particularly at top of distribution Absence of relationship between literacy skills and age in cross-section appears to result from offsetting age and cohort effects

Main findings Schooling is an important determinant of literacy, and this is more evident when we estimate using cohorts Correlation of differences in literacy across cohorts with differences in education results in under-estimation of schooling impacts in cross- section Parental education and immigrant status have significant effects on education but little direct impact on literacy

39 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.

40 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.

41 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.

42 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.

43 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.

44 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 *** *** (0.0001) Average Skill Score *** *** -(0.0003)-(0.0007) Constant4.80***4.28***5.04***4.34*** (0.077)(0.10)(0.16)(0.10) Observations7768 R-squared

45 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

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 46

48 Conclusions Conventional estimates of returns to education and experience confound two effects: –Skill production effect –Market valuation of skills effect Cognitive skills account for about 20% of the returns to schooling (IV estimates suggest more) Literacy and numeracy skills have substantial effects on earnings