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Cognitive Skills: Determinants and Labour

Market Outcomes

W. Craig RiddellUniversity of British Columbia

WISE conferenceXiamen, China

December 12-13, 2008

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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

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• 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

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• 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

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• 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

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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

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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

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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

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Table 3: Log of Document Literacy Regressions Variable OLS 1 OLS 2 OLS 3 OLS 4 IV

Female -0.014** -0.012** -0.0083* -0.012** -0.016**

(0.0046) (0.0045) (0.0045) (0.0044) (0.0051)

Years of Schooling 0.058*** 0.054*** 0.053*** 0.024*** 0.052***

(0.0053) (0.0049) (0.0048) (0.0008) (0.013)

Schooling Squared -0.0011*** -0.0011*** -0.0011*** - -

(0.0002) (0.0002) (0.0002) - -

Age 0.0035*** 0.0064*** 0.0066*** (0.0068)*** -0.0003

(0.0008) (0.0008) (0.0008) (0.0008) (0.0036)

Age Squared -0.0076*** -0.0099*** -0.011*** -0.011*** -0.0029

(0.0009) (0.0009) (0.0009) (0.0009) (0.0039)

Mother’s Education

Less than High School - -0.037*** -0.038*** -0.028*** 0.0007

- (0.0059) (0.0058) (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 - 0.0094 0.0075 0.0076 -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)

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Table 3: Log of Document Literacy Regressions continued

Variable OLS 1 OLS 2 OLS 3 OLS 4 IV

Father’s Education

Less than High School - -0.032*** -0.030*** -0.029*** -0.008

- (0.0067) (0.0067) (0.0067) (0.012)

Some Post Secondary - 0.0055 0.006 0.0062 -0.008

- (0.0073) (0.0071) (0.0073) (0.012)

BA or More - 0.0126 0.0154* 0.011 -0.025

- (0.0083) (0.0082) (0.0082) (0.020)

None Reported - -0.056*** -0.055*** -0.065*** -0.021

- (0.012) (0.012) (0.011) (0.023)

Immigrant Mother - 0.0056 0.0053 -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) -

Constant 5.08*** 5.09*** 5.09*** 5.29*** 5.04***

(0.04) (0.038) (0.038) (0.020) (0.12)

Observations 14666 14527 13868 13868

R-squared 0.49 0.51 0.52 0.47 0.31

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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

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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)

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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

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Figure 2: Document Literacy, Age 26-35

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Figure 3: Document Literacy, Age 36-45

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Figure 4: Document Literacy, Age 46-55

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Figure 5: Document Literacy, Age 56-65

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Figure 6: Document Literacy, less than high school

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Figure 7: Document Literacy, high school

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

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Figure 9: Document Literacy, university

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

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

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

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

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

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

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

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Table 4: Pooled Regressions Including Cohort EffectsVariable OLS 10th Quantile Median 90th Quantile

Years School 0.083*** 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) (0.0002) (0.0001) (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*** -0.18*** -0.30***

(0.033) (0.039) (0.026) (0.016)

Cohort 2 0.018* -0.013 0.018* 0.055***

(0.010) (0.018) (0.0097) (0.0078)

Cohort 3 0.032** 0.031 0.042** 0.082***

(0.016) (0.025) (0.016) (0.012)

Cohort 4 0.053** 0.052* 0.055** 0.13***

(0.029) (0.032) (0.023) (0.014)

Cohort 5 -0.010 -0.053 -0.004 0.11***

(0.033) (0.039) (0.025) (0.015)

Constant 4.97*** 4.39*** 4.98*** 5.48***

(0.054) (0.047) 90.025) (0.021)

Observations 14734 14734 14734 14734

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Table 5: Pooled Quantile Regressions with Cohort Effects, By Education GroupHigh School or Less University

Variable 10th Quantile Median 90th Quantile 10th Quantile Median 90th Quantile

Female 0.064*** -0.011 -0.0096** -0.021** -0.0046 0.0060

(0.013) (0.0049) (0.0047) (0.010) (0.0069) (0.0084)

Age Group

36-45 0.025 -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 2 0.0053 0.038*** 0.061*** 0.012 -0.025 0.0071

(0.013) (0.010) (0.0094) (0.024) (0.017) (0.009)

Cohort 3 -0.053 0.015 0.046** 0.043 0.015 0.018

(0.053) (0.017) (0.016) (0.032) (0.021) (0.018)

Cohort 4 -0.23*** 0.019 0.044* 0.085 0.038 0.011

(0.068) (0.021) (0.025) (0.085) (0.039) (0.023)

Cohort 5 -0.34*** -0.027 0.042** 0.12 0.0071 -0.12***

(0.11) (0.027) (0.024) (0.11) (0.041) (0.035)

Constant 5.45*** 5.76*** 5.87*** 5.73*** 5.80*** 5.99***

(0.027) (0.011) (0.014) (0.032) (0.017) (0.015)

Observations 9209 9209 9209 2163 2163 2163

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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

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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.

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• Abstracting from other influences on earnings,

individual earnings are a function of the skills an

individual possesses and puts into use:

• Ei = f (G1i , G2i , G3i ) + ei (1)

• where Ei are earnings for individual i, Gki is the

amount of skill k that person i sells in the market, and

ei is a disturbance term that is independent of the

skills.

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• Typically, we do not observe the skills but we do

observe some of the inputs that generate them. They

enter via skill production functions:

• Gki = hk (edni , expi , vki ) (2)

• where k indexes the skill type, edn corresponds to

education, exp is years of work experience and vk is

an ability specific to the production of the kth skill.

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• If we do not observe the Gki'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.

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What can be learned when only some skills are observed?

• If we observe the cognitive skills G1, we get a quasi-

reduced form earnings regression that includes G1

(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.

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Table 7: Earnings Regressions OLS 1 OLS 2 IV 1 IV 2

Female -0.41*** -0.40*** -0.40*** -0.40***

(0.024) (0.024) (0.024) (0.024)

Years of School 0.087*** 0.069*** 0.070*** 0.050***

(0.0042) (0.0047) (0.013) (0.017)

Experience 0.067*** 0.067*** 0.068*** 0.068***

(0.0035) (0.0035) (0.0041) (0.004)

Experience Squared -0.0011*** -0.0011*** -0.0012*** -0.0012***

(0.0001) (0.0001) (0.0001) (0.0001)

Average Skill Score - 0.0026*** - 0.0032***

- (0.0003) - (0.0007)

Constant 4.80*** 4.28*** 5.04*** 4.34***

(0.077) (0.10) (0.16) (0.10)

Observations 7768 7768 7768 7768

R-squared 0.38 0.4 0.38 0.39

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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 10th, 25th, 50th, 75th, 90th 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

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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

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

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