Human Capital and Inequality in Canada

Kelly Foley

University of Saskatchewan

David A. Green

Vancouver School of Economics and IFS

February 24, 2014

Inequality in Canada Driving Forces, Outcomes and Policy Human Capital

Introduction

1 Describe recent Canadian trends in education and inequality

Using LFS 1997-2013 and Census 1981-2001

2 What impact does education have on the wage structure?

Technology, skills and occupational-tasks

3 What is the potential impact of policy?

Inequality in Canada Driving Forces, Outcomes and Policy Human Capital

.1.1

5.2

.25

.3.3

5.4

1980 1985 1990 1995 2000 2005 2010year

Women Ages 2534

.1.1

5.2

.25

.3.3

5.4

1980 1985 1990 1995 2000 2005 2010year

Men Ages 2534

Education Shares

Census: Less than H.S. Census: H.S. Census: College Census: BA Census: Graduate

LFS: Less than H.S. LFS: H.S. LFS: College LFS: BA LFS: Graduate

.2

0.2

.4.6

1980 1985 1990 1995 2000 2005 2010 2015

Employed Women Ages 2534

.1

0.1

.2.3

.41980 1985 1990 1995 2000 2005 2010 2015

Employed Men Ages 2534

Education Premia (Relative to High School Graduates)

LFS: Less than H.S. LFS: College LFS: BA LFS: Grad degree

LFS: Less than H.S. Census: College Census: BA Census: Grad degree

Table : Ln Weekly Wage Gaps Men Ages 25-34 (LFS Estimates)

2013Canada East Ont/PQ West

Mean Ln Wage, HS graduates 6.433*** 6.285*** 6.353*** 6.549***(0.010) (0.028) (0.015) (0.014)

Wage gaps:

Less than HS -0.106*** -0.041 -0.123*** -0.030(0.021) (0.064) (0.031) (0.033)

College 0.145*** 0.310*** 0.153*** 0.169***(0.012) (0.036) (0.019) (0.019)

Bachelor Degree 0.176*** 0.261*** 0.250*** 0.078***(0.014) (0.042) (0.022) (0.021)

Graduate Degree 0.241*** 0.393*** 0.311*** 0.151***(0.020) (0.059) (0.028) (0.035)

0.1

.2.3

.4

1980 1985 1990 1995 2000year

Employed Men Ages 2534 (Census)

0.1

.2.3

.4

1980 1985 1990 1995 2000year

Employed Women Ages 2534 (Census)0

.1.2

.3.4

1997 2002 2007 2012year

Employed Men Ages 2534 (LFS)

0.1

.2.3

.4

1997 2002 2007 2012year

Employed Women Ages 2534 (LFS)

Between and Within Group Variance Ln Weekly Wages

Total Variance Within Var Between Var

Link between Human Capital and Inequality:Skill-biased technical change

Skill-biased technical change

e.g. Katz & Murphy, 1992; Autor, Katz, & Krueger, 1998; Card &Lemieux, 2001

Computer revolution increases productivity of and demand forskilled workers

Race between education and technology

Implies that increasing education will reduce inequality

Does not fit Canadian data in the recent periods well

Returns to education and supply of education rising at the sametime

Inequality in Canada Driving Forces, Outcomes and Policy Human Capital

Link between Human Capital and Inequality:Technological change and demand for occupational tasks

Technology adoption occurs in response to supply of skilledworkers:

In Beaudry & Green (2005) firms switch from oldtechnology/unskilled workers to new techologies/skilled workers

Technology changes demand for particular tasks:

e.g. Acemoglu & Autor 2011

Falling demand for routine-task occupations leads to polarization

Consistent with the pre-2000 period (Green & Sand 2014), butnot the post-2000 period

Inequality in Canada Driving Forces, Outcomes and Policy Human Capital

Link between Human Capital and Inequality:Demand reversal and resource booms

Resource booms in key regions

Relative wages fall among university educated in the resource-richregions

Low-skilled workers are relatively scarce in the booming areas

Demand reversal

Demand for high skilled workers falls as investment stage iscompleted (Beaudry, Green and Sand 2013)

Highly educated workers compete for jobs lower on theoccupational structure

Fractions of university graduates working in highest payingoccupations declines between 2000-2008

Inequality in Canada Driving Forces, Outcomes and Policy Human Capital

Impact of Human Capital Policy: Childhood-targeted policy

Targeted early education:

Long-run positive eects on crime, education and earnings (Garces et al 2002,Baker 2011)

Persistent eects are associated with non-cognitive rather than cognitive skills(Heckman et al. 2010)

Universal child care

Some negative behavioural impacts (Havnes & Mogstad 2010, Baker 2011)

Positive eects concentrated among disadvantaged children (Havnes &Mogstad 2010, Baker 2011)

Increasing income among low-income families

Improves cognitive scores pro-social behaviour and educational attainment(Milligan & Stabile 2011)

Inequality in Canada Driving Forces, Outcomes and Policy Human Capital

Childhood-targeted policy: Implications for inequality

Eect of increasing the propensity to attend university amonglow-income children depends on whether:

Additional graduates end up in service and clerical jobsCompete down median wages and increase inequality

Additional graduates are high ability and compete for higher payingoccupations

Compete down high wages and reduce inequality

Eect of increasing non-cognitive skills among the lessadvantaged:

Allow less advantaged people to access middle-paying occupationsReduce the gap between median and low earnings

Increase competition for middle paying jobs, lowering wagesIncrease the gap between median and high earnings

Inequality in Canada Driving Forces, Outcomes and Policy Human Capital

Impact of Human Capital Policy:Policies related to University education

Public funding/subsidized tuition:

Children from families with higher incomes more likely to attend university(Belley, Frennette, and Lochner, forthcoming), which suggests they benefitrelatively more

Grants and loans

Compared to the U.S., Canadian aid programs are relatively generous tomiddle-income families (Belley, Frennette, and Lochner, forthcoming)

Eliminating aid would substantially increase family income-PSE attendancegaps

Savings-based policies (RESP)

Most heavily used by higher income families (Milligan 2005)

Only 15 % of eligible children used Canada Learning Bonds (Essaji and Neil2012)

Inequality in Canada Driving Forces, Outcomes and Policy Human Capital

Policies related to University education: Implications forinequality

Eect of reducing tuition or increasing aid depends on whetherfamily-income gradient results from credit constraints

If the important barriers not are financial,

New attendees likely to be drawn from middle and higher income families withlower average ability

Will increase competition for middle paying jobs

Push lower skilled individuals down the occupational ladder

If other policies can successfully target lower income children,

Tap into a new pool of talent

Will increase competition for higher paying jobs

Decrease gap between median and higher incomes.

Inequality in Canada Driving Forces, Outcomes and Policy Human Capital

Conclusion

Expanding educational spending, particularly at the universitylevel, can not be relied upon as a singular antidote to inequality

Empirical relationship between education and inequality is notstrong

When demand for high-skilled workers is stable or falling:

Important distinction between whether policies increase competitionfor middle-paying versus higher-paying occupations

Inequality in Canada Driving Forces, Outcomes and Policy Human Capital