explained differences: the human capital model - … 321 - spring... · explained di erences: the...

Explained Differences: The Human Capital Model

Spring 2010

Rosburg (ISU) Human Capital Model Spring 2010 1 / 57

Supply-Side Explanations

Differences in occupations and earnings can be from either demand-side(employer) or supply-side (employee) factors.

Supply-side factors:

Different tastes of potential/current employees

Different qualifications of potential/current employees (i.e. education,experience, training, etc.)

Such differences could cause certain groups (race, sex, etc.) to earn lessand concentrate in different occupations


Employee differences

Differences in tastes and qualifications may come from two sources:

Voluntary choices of employees

Societal Discrimination

Definition: Multitude of societal influences that cause specific groups(ex. women) to make decisions that adversely influence their status inthe labor market

Different than labor market discrimination


Human Capital

Human Capital (HK) - resources invested in an individual today in orderto increase his or her future productivity and earnings

Alternative definition: stock of marketable skill and knowledgeembodied in a worker that may be rented on the labor market

Stock: not used up at once but offers stream of services

Marketable: includes only the skills that are important in the labormarket

Embodied, rented: employer cannot access stock without paying theholder


Human Capital II

Examples: formal education, on-the-job training, job search methods,geographic migration, health

Similarities to physical capital (K):

Depreciation

Investment

Stock

Differences from physical capital (K):

Influenced more by non-pecuniary costs (ex: distaste for school)

More difficult to finance investment in human capital – no collateralto offer if loan not repaid


Education Differences

Gender Differences in HK - Education

Educational attainment differences are not large in US

Recall Goldin et al.’s (2006) article on reversal of college gender gap

Women less likely to drop out of high school

Gender differences in educational attainment by race

Whites: men and women nearly equalAsians: men have edgeBlacks and Hispanics: women have edge

Declining gender difference in educational attainment among youngercohorts



Gender Differences in HK - Education II

Differences in high school courses taken:

There has been considerable progress in reducing differences in HScourses

Higher proportion of men take AP exams in science and calculus andscore higher on average than women

Differences in college fields of specialization:

Differences in fields of specialization more substantial than differencesin high school courses

Index of segregation by college major:

1964: 51.41990: 29.4



Differences in HK - Education III

Blacks and Hispanics have lower educational attainment relative to whites

Difference smaller for blacks

Note: Even though men and women have similar educational attainment,gender differences in amount and type of education may impact earningsand occupational attainment


Educational Investment Model

Educational Investment Model (EIM)

Basics:

Individual makes decision whether to invest in formal education or not

Considers both pecuniary and non-pecuniary benefits and costs

Two types of pecuniary costs:

Direct costs - expenditures such as tuition, fees and booksIndirect costs - earnings foregone during time in school [opportunitycost]

Compare expected experience-earnings profiles associated witheach type of schooling

Definition: Annual earnings at each level of labor market experience


Educational Investment Model

Educational Investment Model (EIM) II

Evaluates incremental costs and benefits

Obtain additional education if net benefits (benefits - costs) areabove a given threshold

Threshold may vary by individualForgone interest if money had been invested in a bank insteadDelayed gratificationDecision can not be determined graphically

For simplicity, all graphs will exclude non-pecuniary costs or benefitsunless specified

On average, private rate of return to college education ranges from 5to 15 percent


Educational Investment Model Education

Experience-earnings profile by education level

College profile: ABCRHigh School profile: OEDR’


Educational Investment Model Education

Two views of education

1 Productivity Enhancing

Provides a variety of skills and knowledge that are potentially useful inthe job marketTeaches desirable behaviors (punctuality, following instructions,dependability, etc.)

2 Screening device or signal

Education may be used to distinguish more productive applicants fromless productive applicants during hiring processIf employers believe education signals different productivity for peopleof different groups, unfavored group members need higher education toget same job (statistical discrimination)

Difficult to resolve but the reason doesn’t matter from individual’sperspective in EIM


Educational Investment Model Expected Work Life

EIM - Expected Work Life

Women may anticipate shorter, more disrupted work lives than men

Lowers incentive to make HK investments which:

1 Require sustained, high-level commitment to payoff2 Depreciate rapidly during interrupted periods

As young women anticipate longer and more continuous work lives, it willbe more profitable to increase investment in formal education



Discontinuous Worker

Return to college education without interruption: DRR’Return to college education with interruption: grey areasWork-force interruption reduces return to investment



EIM - Discontinuous Worker

Discontinuity of expected labor force participation may help explaingender differences in fields of specialization

Returning women have depreciation of skills AND face advancementof field during absence

May avoid fields with rapid technological progress

Note: Gender differences in college major is strongly related to thegender wage gap among college graduates

Expectations may affect which area of specialization a person chooseswhich leads to wage differences


Educational Investment Model Other Factors

Other factors - Educational Investment

Societal discrimination may cause differences in educationalattainment and fields of specialization

Need to consider non-pecuniary costs and benefits

Feedback effects: faced with discrimination in labor market, whichlowers returns to HK investment, individuals facing discrimination willhave less incentive to invest.

Socialization: process by which the influence of family, friends,teachers and media shapes an individual’s attitude and behavior



Socialization

Television and movies

Taught or influenced at early age to aspire and train forgender-appropriate jobs

Potential disapproval of family, teachers or friends is a non-pecuniarycost → lowers net value of investment

Example: Barbie and Ken

1 in 100,000 young women will have shape of Barbie1 in 50 young men will have shape of Ken

Some stores have separate toy aisles for ‘girls’ and ‘boys’ toys

Merchants argue they are responding to children’s preferencesPerpetuate traditional gender roles by limiting children’s visions



Gender-Appropriate Traits and Consequences

Female-dominated fields may be socialized to emphasize “feminine” traits:

subordinate, nurturing, emotional

Male-dominated fields may be stereotyped as requiring “masculine” traits:

dominance, competitiveness, rationality

Women may avoid male-dominated fields due to:

Non-pecuniary costs of acting in an “unfeminine” way

Perception that they are unequipped to act in an “unfeminine” way



Example - Women and Math Stereotype

Stereotype: Women have inferior math skills

Psychology study tested impact of stereotype on performance

Women may worry that difficulty during the test or poor performancecould be judged as proof of stereotype – extra pressure or stress

Participants were university men and women with equivalent mathbackgrounds and interests



Women and Math Stereotype Study - Results

First group: Gave participants either easy or difficult math test with onlyinformation about difficulty level

Women performed worse than men on the difficult test only

Second group: Gave the difficult test to all participants. Half of theparticipants were told the test had shown gender differences in past andother half were told it was a gender fair test.

Told gender fair → women performed equally well

Told gender differences → women scored lower

More evidence of “blanking-out” or “choking”



Women and Math Stereotype Study - Results II

Conclusion: If math skills can be judged negatively, even when controllingfor math interest and background, women’s performance is influenced bygender stereotypes when combined with stress and anxiety.

Similar results found for:

African-Americans and Latinos in intellectual situations

Elderly in memory testing

White men in sports



Discrimination by Educational Institutions

Overt discrimination against women (and other minorities) in collegeadmission and professional school in not-so-distant past

Occurred at highly respected institutions (ex: Princeton, Yale)

Allowed access to classes and certain facilities but minorities weresometimes limited to separate colleges

‘Opening of doors’ does not mean it has been opened as wide

Minorities held to higher standardsOvert or informal quotasDifferent course requirements



Subtle Barriers to Women

Overt barriers are beginning to decline but subtle barriers remain a problem

Male dominance in field may discourage young women from attempting toenter

Lack of role models (lack mentor-protege system)

Lack successful strategies or plan for combining work and family roles

Forced pioneers – more difficult than well-established path

Exclusion from informal networks (i.e. study groups and discussions)



Subtle Barriers to Women II

Informal contact between teachers and students and among studentsprovide:

Support

Encouragement

Access to information

Lack of informal contact raises non-pecuniary cost

May result in feedback effects (lower incentive to invest)

Problem: Quantitative evidence difficult to gather


Educational Investment Model On-the-Job Training

On-the-Job Training

Similar to investment in education, human capital theory suggests thatweaker attachment to labor force of women who follow “traditional”gender roles lowers their incentive to acquire on-the-job training

Two types of on-the-job training:

1 General training: increases productivity in all firms

2 Firm-specific training: increases productivity only at firm whichprovides training

Note: Usually a mixture of both but will assume separate for simplicity



General Training

Employer will not pay any costs since collects no return - employeecan abandon job at any point and use skills from general training atnew job

Employee bears all costs and reaps all return

During training, employee transfers attention from daily production totraining

Decline in output is opportunity cost to firm of trainingEmployee will initially accept wage below what could get elsewhere dueto expected future benefits from training



Experience-Earnings Profile for General Training

No training: UU’

General training: GG’



Firm-Specific Training

Benefits from training are firm-specific – if employee leaves, trainingwill not be beneficial at new job

Employee will not bear all costs since ability to reap returns dependson continued employment at firm

Employer will not bear all costs since employee would have noincentive to remain at firm unless you provide them with a financialincentive

Share costs and returns

Employer has incentive to retain workerEmployee develops a relatively permanent attachment



Experience-Earnings profile for Firm-Specific Training

Employee return to firm-specific training: GG’

Employer return to firm-specific training: SS’

Benefits and costs noted in diagram



Experience and Earnings

HK theory: earnings increase with experience since work productivity isaugmented by on-the-job training

Critics are not sure if this causes higher earnings

Rise in earnings may be result of widespread use of seniorityarrangements



Experience and Earnings - Alternative Argument

Alternative explanation: upward-sloping earnings profiles rewardsexperience which raises productivity through the motivation to gainbenefits of tenure (retirement, higher earnings)

Higher productivity NOT due to training

Productivity does NOT rise with experience

Focus is on tenure

Empirical evidence mixed

Again, the reason doesn’t matter from the individual’s perspective



Gender Differences in Training Investment

Gross return to on-the-job training depends on number of years overwhich the return is earned

Workforce interruption lowers earning profiles due to depreciation ofskills

Next graph illustrates consequences on the incentive to invest infirm-specific training of shorter and more discontinuous labor forceparticipation [i.e. women following ‘traditional’ path]

Women in traditional gender roles find it less profitable to make largeinvestments in training than career-oriented men or women



Discontinuous Worker and Firm-Specific Training



Discontinuous Worker and Firm-Specific Training II

If exit, may not be able to get old job back

Firm-specific skills useless (return to profile without training)

Returns to investment wiped out

Unless guaranteed reemployment, employee faces risk of losing returns

HK theory suggests that workers who anticipate workforce interruptions oflong or uncertain duration will avoid jobs where firm-specific training isimportant (ex: ‘traditional’ wife)



Expected Work Tenure and Training

Empirical evidence supports prediction that women receive less on-the-jobtraining

As labor force attachment and career-orientation increase → profitabilityof investment increases

Most important factor for firm-specific training is attachment to firm

Yet, differences in training are not fully explained by factors emphasized inthe HK model → discrimination has a role



Occupations and Earnings - Gender DifferencesBased on the HK model, women fulfilling ‘traditional’ roles are expectedto:

Select occupations requiring less investment in education andon-the-job training

Employers may also be reluctant to hire women in these jobs(statistical discrimination)

Seek jobs where depreciation of earnings for time spent out of laborforce is minimal

Choose occupations with higher earnings now rather than in future

Avoid jobs where pay is highly dependent on experienceMay cause occupational segregation

Search less for best available job



Gender Differences in Occupations and Training

Earnings profiles in male jobs: MM’Earnings profiles in female jobs: FF’



Additional Supply-side Factors

Traditional gender roles (i.e. women as secondary earner)

Housework time may reduce available effort for work

Women are more likely to quit for family-related reasons

Non-market responsibilities

Availability of maternity leave mitigates this effect

Priority of husband’s career:

“Tied mover” - forced to relocate when not advantageous“Tied stayer” - forced to forgo good opportunities elsewhere



Assessment of Human Capital Model

Most studies find HK important in explaining the gender pay gap

Other important factors: tenure, work interruptions, and timing of pastwork experience

Young women who expected to be working at 35 have experience-wageprofiles that start lower but have a steeper slope compared to women whodo not plan to be working at 35.

Expected to not work at 35: took higher paying jobs with lowerexpected wage growth

HK theory as the explanation for gender differences in occupations hasmixed results



Evidence for the HK Model

A substantial portion of the pay difference is accounted for bydifferent skill requirements between male and female jobs

Married women have lower penalties for reentering the labor force infemale-dominated rather than male-dominated jobs

Women with limited expected future labor force participation selectoccupations with lower job skill requirements



Evidence against HK model

In the diagram for women’s and men’s occupations and training,there was a crossover point in the experience-earnings profile → nocrossover point in the data

Returns to education are not higher for women in male-dominatedjobs versus female-dominated jobs

Earnings of women in female-dominated jobs do not depreciate lessthan in male-dominated jobs

Women with discontinuous work history were no more likely to be infemale-dominated occupations than women continuously employed

Overall: Labor force commitment and participation ingender-dominated jobs are not as closely associated as the HKexplanation for segregation suggests


Empirical Evidence

EMPIRICAL EVIDENCE


Empirical Evidence

2003 Distribution of Educational Attainment (25-64)1

Larger differences in investment in education across races thanbetween males and females

1Source: Blau, Ferber, Winkler. 2006. The Economics of Women, Men and Work.5th Edition.


Empirical Evidence

Ratio of College Graduate Rates at 35 by Birth Year2

Increase during Great DepressionOther significant events: GI Bill, WWII, Korea, Vietnam

2Source: Goldin, Katz and Kuziemko. 2006. “Homecoming of American Women.”Journal of Economic Perspectives.


Empirical Evidence

Education by Race3

High school gap convergingCollege gap seems to be widening

3Source: Lang. 2007. Poverty and Discrimination. Princeton University Press.Rosburg (ISU) Human Capital Model Spring 2010 45 / 57

Empirical Evidence

Male Mean Earnings by Education in 2003

Limited to full-time, year-round MALE workers

Lifetime difference between college and HS earnings: $593,000


Empirical Evidence

Female Mean Earnings by Education in 2003

Limited to full-time, year-round FEMALE workers

Lifetime difference between college and HS earnings: $415,000


Empirical Evidence

Earnings by education

Given the high lifetime benefits of college ($593,000 for men and $415,000for women), does this imply that college pays for itself on average?

Recall: minimum returns to invest (threshold) varies by individual

Will depend on how much they discount future earnings


Present Value

PRESENT VALUE


Present Value

Present Value of HK Investment

Investment in human capital provides benefits for many years (stock)

Make decision today based on expected payoffs in the future → need tocalculate present value

Present value: today’s value of a future payment or series of futurepayments discounted to reflect the time value of money


Present Value

Present Value of HK

Since investment in HK typically provides benefits for many years and thevalue typically changes annually, we cannot use a standard form forpresent value.

Present value of the full set of benefits is calculated by dividing each year’sbenefit by (1 + r)T where r is the individual’s time preference (i.e. discountrate) and T is the number of years in the future the benefit will be received

Let:

Ht = Earnings from High School Education in year t

Et = Earnings from College Education in year t

Ct = College tuition and costs in year t


Present Value

Present Value of HK II

PVE = Present Value of Earnings with a College Degree

PVE =E5

(1 + r)5+

E6

(1 + r)6+

E7

(1 + r)7+ . . . +

ET

(1 + r)T

PVC = Present Value of College Costs (including opportunity costs)

PVC =H1 + C1

(1 + r)+

H2 + C2

(1 + r)2+

H3 + C3

(1 + r)3+

H4 + C4

(1 + r)4+

H5

(1 + r)5. . .+

HT

(1 + r)T


Present Value

Net Present Value

Net present value (NPV) is the difference between the present value ofbenefits (salary) less the present value of costs (direct and indirect)

NPV = −H1 + C1

(1 + r)− H2 + C2

(1 + r)2− H3 + C3

(1 + r)3− H4 + C4

(1 + r)4+

E5 − H5

(1 + r)5+ . . . +

ET − HT

(1 + r)T

Incentive to invest:

Increasing in: Et , T

Decreasing in: Ht , Ct , r


Present Value

Net Present Value

Investment Decision:

NPV > 0→ Go to college

NPV < 0→ Don’t go to college

NPV = 0→ Indifferent

There exists some r̄ such that NPV = 0

r̄ is the individual’s internal rate of return


Present Value

Example 1: Returns Necessary for College to Breakeven

Case 1:

C = $5,000/year

H = $20,000/year for 44 years

r = 0.10→ PV College cost = $79,250

For breakeven [NPV = 0] need

PV of earnings with a college degree to equal $79,250 or, equivalently

E - H = $8,104 for 40 years (or E = $28,104)

Lifetime gains of a college degree relative to a high school degreeneeds to be: $324,000

Given earlier statistics, the mean returns for men and women would beenough to incentivize investment for both men and women on average


Present Value

Example 2: Returns Necessary for College to Breakeven

Case 2:

C = $15,000/year

H = $20,000/year for 44 years

r = 0.10→ PV College cost = $110,950

For breakeven (NPV = 0) need:

PV of earnings with a college degree to equal $110,950 or,equivalently

E - H = $11,346 for 40 years (or E = $31,346)

Lifetime gains need to be: $453,830

Given earlier statistics, mean returns for men would be enough toincentivize investment in college but not for women


Present Value

Readings for Next Section:

Newspaper Articles Handout

Case Evidence - Women

Blau and Kahn (2000)

Blau and Kahn (2006)


explained differences: the human capital model - … 321 - spring... · explained di erences: the...

Documents