The Long Term Evolution of Female Human Capital · The Long Term Evolution of Female Human Capital Audra Bowlus and Chris Robinson University of Western Ontario Presentation at Craig

The Long Term Evolution of Female HumanCapital

Audra Bowlus and Chris RobinsonUniversity of Western Ontario

Presentation at Craig Riddell’s FestschriftUBC, September 2016

Audra Bowlus and Chris Robinson University of Western Ontario The Long Term Evolution of Female Human Capital

Introduction and Motivation I

Labor market attachment of females has increaseddramatically over the last half century (Blundell andMacurdy (1999), section 3, Goldin (2006), Blau and Khan(2013).)

Large and very interesting literature trying to explain why -not the topic of this paper

Focus of this paper is on an important implication ofhuman capital theory, given this greater labor marketattachment: an increased incentive for females to invest inhuman capital


Introduction and Motivation II

One manifestation of increased incentive to invest:convergence in share of successive female birth cohortsthat are college graduates to that of males

Only a partial indication of increased human capital

Standard “composition adjustment” methods showincrease in efficiency units of female human capital throughchanges in education and experience “cell” composition

Cannot capture within cell increases for females: animplication of human capital theory given increased labormarket attachment

Consequence: under-estimate of the contribution of femalehuman capital to postwar growth


Introduction and Motivation III

Human capital is not directly observed: problem ofidentification

Alternative approaches and data sources:

(1) Wage-based approach uses data on changes in wagesto infer changes in quantities of human capital - classicBen-Porath

(2) Standard “composition adjustment” uses data onchanges in education and experience

(3) Job-skills-based approach uses data on changes inoccupations

Paper uses and compares estimates from (1) and (3); bydefinition (2) cannot capture the within cell increases forfemales the paper seeks to explore


Outline of the Presentation: Wage-based Approach

Description of Wage-based approach and identification -(Ben-Porath style with cohort effects) - extension of Bowlusand Robinson (AER 2012)

Discussion of three sources of cohort effects: (1) selectionon ability in education choice; (2) technological change inhuman capital production functions; and (3) expectedattachment to the labor force

Cohort data on education and participation relevant for (1)and (3) and some discussion of (2)Estimates of life-cycle human capital (efficiency unitssupplied) profiles - (wage-based approach) - andcomparison of cohort patterns for males and females

Complications due to (time path of) discrimination incomparing males and females


Outline of the Presentation: Job-Skills-basedApproach

Description of Job-Skills-based Approach

Problems of identification of life-cycle and cohort effectswith the Job-Skills-based Approach

Construction of a single dimension measure to comparewith the single dimension measure of the wage-basedapproach

Estimates of life-cycle (single dimension measure) profiles- (job-skills-based approach) - and comparison of cohortpatterns for males and females


Outline of the Presentation: Summary and Next Steps

Wage-based approach yields:

pattern of life-cycle human capital profiles for females thatshift up for the cohorts with higher labor market attachmentfor all education groups

contrast with pattern for males where there is much lesschange in labor market attachment across cohorts

Job-skills-based approach yields mostly similar qualitativepattern in the contrast between females and males, thoughdifferent magnitudes

Next steps: (1) refinement of both methods; (2) use ofpanel data including LISA; (3) estimate of under-estimationof “true” aggregate labor input of females


Wage-Based Approach: Framework

Heterogeneous human capital - type determined byeducation group - separate prices

Each education group has only one type - quantities canbe aggregated within type (efficiency units) - similar to“canonical model”

Ben-Porath style optimal life-cycle accumulation problemwith a first stage choosing education group (type)(Heckman, Lochner and Taber (1997)); solution followsfrom comparison of marginal cost of production of humancapital and marginal benefit

Bowlus and Robinson (2012) framework with cohort effectsextended to allow for variation in labor market attachment

Identification of human capital quantities (efficiency units)via implementation of a“flat-spot” method based on theBowlus and Robinson (2012)


Wage-Based Approach: Three Sources of CohortEffects

Two sources of cohort effects in the original Bowlus andRobinson (2012) framework:

(1) heterogeneity in ability and correlation of ability andeducation level (effects through the production functionsand marginal cost)

(2) technological change, broadly interpreted, in theproduction of the different types of human capital (again,effects through the production functions and marginal cost)

Third source of cohort effects particularly important for thispaper:

increased incentive for females to invest in human capitalfollowing the large secular increase in their participationrates (effect through marginal benefit)


Wage-Based Approach: Data Patterns Relevant forCohort Effects

Data relevant for cohort effect (3) through increasedmarginal benefit for cohorts with greater labor marketattachment are life-cycle participation patterns by cohort

Data relevant for cohort effect (1) through heterogeneity inability and correlation of ability and education level arecompleted education levels by cohort

More difficult for cohort effect (2) through technologicalchange in human capital production (currently inferred inBowlus and Robinson (2012) and Agopsowicz, Bowlus andRobinson (2016))

Possibility of some direct data from other sources, e.g.Green and Riddell (2013), Barrett and Riddell (2016)


Female Life-Cycle Participation by Birth Cohort


Life-Cycle Participation by Cohort: Females Summary

Participation rates are higher for more educated groups

More recent cohorts for all education groups show higherparticipation levels at all ages

Interesting changes in the life-cycle pattern of participation:

For early cohorts the peak in participation rates does nothappen until relatively late in the life-cycle

Most dramatic difference in the shifts in profiles across thecohorts is that participation at the earlier part of thelife-cycle increases

For all but the dropouts, by the 1958 cohort the rate is at itspeak and relatively flat shortly after the end of the formaleducation period

Potential, therefore, for large cohort effects for females inall education groups


Male Life-Cycle Participation by Birth Cohort


Participation Rates by Age: Summary for Males

By comparison with females:Males show only quite minor changes across cohorts

Tendency for participation to fall slightly compared to thestrong increase for females

College graduates for all birth cohorts show flatparticipation rate at high level from their mid to late 20suntil their mid-50s and still show participation rates of 80%or more until age 60

Some college males show same pattern but begin a slowdecline earlier and start to fall below 80% by their late 50s

High school graduates similar to some college except formore variation by cohort and dropouts show most cohortvariation with lower participation for the most recentcohorts and generally lower participation at each age


Increasing Educational Attainment for Females


Lower Education Groups: Comparison of Males andFemales


Higher Education Groups: Comparison of Males andFemales


Increasing Educational Attainment for Females

Completed education level shares of four educationgroups: dropouts, high school graduates, some collegeand college graduates for successive birth cohorts offemales from 1931 to 1967 obtained from usingobservations on the same age group (point in thelife-cycle), 31-35, for each cohort to control for life-cycleeffects in reporting

The two lowest education levels show declining shares,while the two highest education levels show increasingshares

Overall, pattern of completed education levels for femalesrelative to males shows the features expected from theincreased incentive for females to invest in human capitalimplied by their increased labor force participation


Wage-Based Approach: Maintained Assumption forMale-Female Human Capital Comparisons

There is no specifically male or female human capital - forexample, female and male college graduates have thesame “type” of college graduate human capital

Same assumption made in constructing college graduatehuman capital in standard implementation of the canonicalmodel using composition adjustment - males and femalesmay have different amounts of the college graduate typehuman capital, but they do not have different types

Same assumption in much of discrimination literature


Wage-Based Approach: Possible Distinction betweenMarket and Home Human Capital

Goldin (2006) discusses shift in the college majors for women:

“Whereas in 1970 a standard dissimilarity index for college majorsbetween men and women exceeded 0.5, it fell to about 0.3 in 1985”

“Women’s majors shifted from those that were “consumption” related tothose that were “investment” related.”

There may be two types of human capital: one useful largely in marketproduction (Goldin’s “investment” major); and another more useful inhome production (Goldin’s “consumption” major)

Shift in production at the university level as seen in major choicetowards more market oriented human capital would show up astechnological improvement in producing (market oriented) humancapital for females relative to males

This “technological improvement” would be tied to the pattern ofparticipation increase and major shift

Amend assumption to: there is no specifically male or female marketoriented human capital


Wage-Based Approach: Identification of Male andFemale Human capital

The wage-based analysis in this paper uses the price series derived inBowlus and Robinson (2012) from data on full time and full year malesto back out the quantity of human capital profiles by cohort for bothmales and females

In a standard competitive market males and females would face thesame price for any given type of human capital - price series derived inBowlus and Robinson (2012) based on data for males can be used tocompute efficiency units of human capital for females

Males used because their participation pattern for each cohort andacross cohorts, and their (college graduates) education pattern acrosscohorts are important elements of the implementation of the “flat-spot”method

Patterns for females make similar implementation of the “flat-spot”method on female data infeasible

Complications from discrimination


Wage-Based Approach: Complications fromDiscrimination I

Large literature that studies discrimination against females - can takemany forms

If it results in a different (lower) price for females for same type ofhuman capital, male based price series would be an over-estimate ofthe price series females faced resulting in an under-estimate of thefemale human capital when the female wage is divided by thisover-estimated price

Using male price series not a problem if level of discrimination constant- changes in cohort pattern of efficiency units for females could still beidentified and contrasted with changes in the cohort pattern for males

Primary concern is that there may have been a secular decrease indiscrimination resulting in secular decline in amount of under-estimationof female human capital, imparting an upward bias on estimatedchange in female human capital


Wage-Based Approach: Complications fromDiscrimination II

Possible that the main effect of discrimination is not to put a largewedge on the price but rather to prevent females acquiring the levels ofhuman capital acquired by males by reducing access to training andpromotion opportunities or other barriers

Also possible that a large part of the disappearance of the gender wagegap, at least on labor market entry for female college graduates is dueto the shift in college major to produce the “investment” rather than“consumption” type human capital referred to by Goldin (2006) and thatthis shift could be in part a response to reduced barriers fromdiscrimination

In this case use of the price series based on males would still permit theestimation of changes in the actual amount of efficiency units suppliedby females in the different cohorts even in the presence of decliningdiscrimination


Wage-Based Approach: Data

MCPS for the period 1964-2009, same as Bowlus andRobinson (2012)

Provides source of annual data for large age rangecovering wide range of pre- and post-war cohorts

Issues of time varying top coding and allocation treatedsame way as Bowlus and Robinson (2012)

Allows for selection of full time and full year


Wage-Based Approach: Estimated Profiles andCohort Differences

The comparisons presented are for estimates based onthe evidence for full time and full year males and females

They are not designed to measure relative changes forfemales via changes in education or labor supply per se

They are designed to examine whether a “compositionadjustment” approach to measuring female human capitalis likely to under count the growth in female human capitalfollowing a participation increase

That is, we are looking for evidence of relative changes forfemales compared to males even within age and educationgroup and full-time and full year workers


Wage Based Human Capital Profiles for Female


Wage Based Human Capital Profiles for Males


Wage-Based Approach: Cohort Pattern Differences inMale and Female Profiles

The figures show a clear contrast for males and females:

Apart from the dropout group, which for much of the periodcan be expected to have a negative selection effect basedon the change in the fraction of the cohort that are dropouts,there is clear evidence of an upward shift in the profiles forfemales, consistent with the increased participation

In contrast, male dropouts, high school graduates, and to alesser extent the some college group show a decline in therecent cohorts

For male college graduates there is an increase for the post1949 cohorts, interpreted in terms of cohort effects fromselection and technological change in the human capitalproduction function in Bowlus and Robinson (2012)

For female college graduates, however, there are clearincreases across all three cohorts


Wage-Based Approach: Summary Estimates ofCohort Differences

Bowlus and Robinson (2012) presented a simple measure of cohortdifferences in the profiles for male college graduates by regressing thelog efficiency units from the previous plots for a particular age range(30-45) on a quadratic in age and cohort dummies

The age range restriction means that all ages are represented for allcohorts used

We extend this to males and females for all education groups and usethe individual level data:

the estimates of the cohort dummies are insensitive to theinclusion of the age quadratic

the age quadratic is only necessary to pick up the life-cycle shape

The cohort dummies are plotted separately for low and high educationgroups


Wage-based Cohort Patterns: Lower EducationGroups


Wage-based Cohort Patterns: Higher EducationGroups


Wage-Based Approach: Male-Female ComparisonResults

Clear evidence from wage-based approach of differentcohort pattern for females consistent with increased labormarket attachment

Females human capital increases generally ineducation/experience cell, including lower educationgroups, unlike males

Magnitudes are large: the 1961 cohort full time and fullyear college graduate is supplying on average over 20%more than her counterpart from the 1946 birth cohort

Suggests conventional labor input measures result insignificant under-estimate of the contribution of females topost-war growth


Job-Skills-based Approach: Description

Infers the amount of human capital from occupation data instead offrom wage data thus avoiding the problems associated with decliningdiscrimination

Growing literature uses measures of job skills to study human capitaland wage patterns

We assume skills can’t be unbundled (Heckman and Scheinkman(1987), Firpo, Fortin and Lemieux (2013) - same skill types are held byall education groups but in different amounts and ratios

Same objective function as in Ben-Porath framework - some optimalpath for the bundle - but multi-dimensionality of bundle breaks simplelink between quantity and wages

In order to compare human capital from a multi-dimensional bundle ofskills to the single dimension measure from the wage-based approachwe “scale” the bundles along a single dimension


Data for the Job-Skills-based Approach

Same as for the wage-based analysis - MCPS - except drop years ofthe data set before the start of three digit 1970 occupation coding (1970coding began in 1971)

Emphasis on consistency over time in the data source for occupations -four different census occupation coding periods in the data covering1970, 1980, 1990 and 2000 census coding

Strong similarity between 1980 and 1990 codes - more majordifferences between 1970 and 1980 and between 1990 and 2000

IPUMS project constructed a set of three digit occupation codes,based on census 1990 coding, that aims for consistency across1970 through 2000 coding

For consistency number of codes is reduced

Final number of codes is less than the close to 500 in thelater period original coding schemes, but still provides for alot of variation with close to 400 codes


Job-Skills-based Approach: Definition of the Bundles

Literature typically takes two steps to assign job skills to workers:

(1) job skill ratings are taken from sources such as the DOT orONET and average skill ratings for a low dimension vector of skills(skill portfolio) for each occupation are constructed

(2) skill portfolios are assigned to individuals in the main workersdata sets on the basis of the worker’s 3-digit occupation code(Poletaev and Robinson (2008), Yamaguchi (2012), Gathman andSchonberg (2010), Bowlus, Mori and Robinson (2016).)

Could make a grid of the separate skills from a low dimension skillportfolio obtained from (1) and treat these as the bundles

Instead we treat each of the approximately 380 IPUMS consistentcodes as a unique bundles of skills and bypass (1) so that scaling thebundles is the same as scaling the IPUMS occupations

In practice, discretizing each skill in a three vector of skills and allowingfor even a modest number of discrete categories for each skill soonresults in a grid where there are as many points as unique occupations


Job-Skills-based Approach: Construction of SingleDimension Measure

Analogous to composition adjustment approach to scaling differentage/education cells to compute single total for a given type of humancapital in the canonical model

Standard implementation computes efficiency units for a single skillheld by different age and education cells by “pricing” the cells using thewages of the cell averaged over the entire period

Could “price” bundles to scale them and produce quantities on a singledimension in same way

Preferred approach modifies this - does scaling using efficiency units formales from the wage-based approach

Argument: male efficiency units are correctly estimated and cantherefore provide a direct baseline quantity scaling for the bundles


Job-Skills-based Approach: Identification

Wage-based method can capture both life-cycle andcohort effects on quantities of human capital - simplydivide the observed wages across the life-cycle or acrosscohorts by the appropriate price

For job-skills-based approach observed data across thelife-cycle or across cohorts are occupations

If there was a very fine grid of occupations in which allworkers had identical skills within occupation we couldcapture changes in skills across the life-cycle and acrosscohorts by observing changes in these occupations

Moreover, the price wedge between males and females forthe same type of human capital could be estimated fromwage differences within this fine grid of occupations

Unfortunately, in practice the occupation codes andallocation of workers to these codes are very far from thisideal situation


Job-Skills-based Approach: Single Occupation CareerProblem Example

Suppose individuals start in an occupation corresponding to their initialbundle and then make investments that simply scale up the bundle

The ideal fine occupation grid would represent sequences on jobladders such that each higher level bundle of any type corresponded toa different occupation - with enough occupation codes the growth inskills over the life-cycle or cohort changes could be inferred fromoccupation data

Actual occupation coding is such that many “careers” in which thebundle does evolve through human capital investment are representedby a single occupation code rather than a sequence of codes on a jobladder (many professions such as lawyer, doctor, and professor)

neither growth in skills over the life-cycle, nor differences in thehuman capital across cohorts of lawyers, can be identified fromthe occupation data


Job-Skills-based Approach: Summary Estimates ofCohort Differences

The same regressions are run as for the wage basedmethod with the log of efficiency units as the dependentvariable and cohort dummies

However, the log of efficiency units is now measured by thesingle dimension measure from the job-skills-basedapproach: each individual in the data set is assigned avalue of efficiency units based on their 3 digit IPUMSconsistent 1990 code

The results are again insensitive to the inclusion of an agequadraticThe cohort dummies are again plotted separately for lowand high education groups


Skill-based Cohort Patterns: Lower Education Groups


Skill-based Cohort Patterns: Higher Education Groups


Job-Skills-based Approach: Male-Female ComparisonResults for Low Education Groups

Compared to the wage-based results, for the low incomegroups the male-female difference is still apparent, thoughthere are some significant differences:

The earlier cohorts for females again show improvementwhile for males their human capital declines

However the later cohorts decline for both males andfemales, albeit the rate of decline is slower for females

Job-skills-based approach can only capture cohort effectsto the extent that cohorts change occupation patterns -cannot capture within occupation changes:

magnitudes are, therefore, likely to be different

pattern may be affected by differential changes in theamount of occupational change across cohorts (withineducation) for males and females


Job-Skills-based Approach: Male-Female ComparisonResults for High Education Groups

For the high education groups the results are qualitativelyquite similar to the results from the wage-based approach,though again the magnitudes are different:

Male college graduates show a very similar pattern to thewage-based estimates and the females show a similarcontinuous growth over cohorts

Male some college group also show a similar pattern to thewage based estimates and their decline in the pre-warcohorts again contrast with the increase for females

Unlike the wage-based estimates, however, the somecollege group for females shows no growth after the 1949cohort


Outline of the Presentation: Summary and Next Steps

The relative patterns for males and females are similar in boththe wage-based and the job-skills-based approach

For the higher education groups there is consistent evidencefrom both approaches of increased human capital in the morerecent female cohorts

Suggests that there is enough change in occupation patterns,especially for female college graduates, for the job-skills-basedapproach to capture at least some cohort effects

wage-based approach suggests that there is also substantialwithin occupation increases in the human capital for females, butthe potential bias induced by declining discrimination makes itdifficult to assess the true magnitude

Next steps: (1) refinement of both methods; (2) use of paneldata including LISA; (3) estimate of under-estimation of “true”aggregate labor input of females


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The Long Term Evolution of Female Human Capital · The Long Term Evolution of Female Human Capital Audra Bowlus and Chris Robinson University of Western Ontario Presentation at Craig