Ec423: Labour Economics

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					 Discrimination:
Empirical Evidence
       Lent Term
        Lecture 7
   Dr. Radha Iyengar
Last Time
   Models of discrimination
       Employer Preferences/Becker Model
            Discrimination by average firm does not mean
             discrimination by marginal firm
            Competition will eventually eliminate discrimination


       Statistical Discrimination
            Groups with different means may get compensation
             based on group-average productivity not individual
             productivity
            Can occur even with the same mean but different
             variance in signals
Testing for Discrimination - 1
   Regression studies.
       interpret the meaning of a race or gender coefficient in an
        OLS model, typically a wage model.
       use a Oaxaca- Blinder decomposition to interpret the
        magnitude of findings.


   Learning models
       Apply structural models to make inferences about what
        employers believe initially and how they update beliefs as
        productivity is revealed over time.
       The key tool here is to use information known to the
        econometrician at market entry (such as test scores) but not
        known by employers except through its revealed effect on
        productivity or its correlation with other observables.
Testing for Discrimination - 2
   Quasi-experiments where race/gender is
    alternatively revealed or concealed.

   Experimental Studies
       Audit studies: attempt to ‘randomize’ race to evaluate if
        minorities are treated differently in job application,
        housing search, vehicle purchases.
       Lab experiments. In a non-market setting,
        experimenters look for evidence of disparate treatment
        of members of race or gender groups.
Earnings Equation
   Define
       Yi ≡ the outcome of the process, such as income
        earnings, or wage for the ith person.
       Xi ≡ a vector of productivity characteristics of
        the ith person that are independent of Y and of
        the particular form of economic discrimination
        under study (exogenous)
       Zi ≡ 1[in the majority group]
       ei ≡ random error term


 Our Standard Earnings Equation is then:
 Y = X'B + AZ + e
Estimating Discrimination
   If A > 0 then there is evidence of discrimination
       null is A = 0 with one-side alternative
       we’re not considering “reverse discrimination” where A < 0

   Define discrimination to be:
          ˆ                 ˆ
    D  (Y | X , Z  1)  (Y | X , Z  0),           ˆ
                                              where Y  E (Y | X )

   Obvious Problem is, what to do with differences in
    X’s
       May be that investment in a given level of X has different
        returns due to statistical discrimination: Remember the
        TWM results from GED paper
       Expectation of discrimination underinvestment (this is a
        point Heckman makes in his review of the education lit)
The Regression Approach
   Originally done by Oaxaca and repeated in
    many settings

   Key is to establish FACTS
       Not entirely clear what the causal mechanism
        underlying observed differences can be
       Useful for defining classes of models that could
        explain these differences
Race and Gender Earnings Differences
   Black and Hispanic men, as well as white
    women, earn about 2/3 that of white men.
       Black and Hispanic women earn even less than
        minority mean—only slightly over ½ of what
        white males earn.
       Wage trends reveal that women, particularly
        white women, have experienced an increase in
        their earnings relative to mean.
       After declining in the 60s, wage gaps have
        widended between race/ethnic groups.
Race and Gender Hours Differences
   Among women: white women’s wages have risen
    steadily since 1980.
       Black women’s wages almost reached parity with white
        women in the 1970s but diverged after that.
       Hispanic women are doing relatively worse, although
        that may be due to immigration and changing
        composition.
       Annual earning show an even large differential than
        hourly wages, suggesting that weeks and hours worked
        are lower among minorities and females. These
        differences are less among full-time/full-year workers,
        but are still substantial.
       Women are more likely to work part-time.
Race and Gender Employment Differences
   White men are more likely to ever be employed
    and to be employed at any point in time.
       Unemployment among white women has been as low or
        lower than among white men since the early 80s.
       Blacks have about twice the unemployment rate of
        whites and this unemployment is more cyclical.
       Female labor force participation rates have been
        converging. Women, especially white women, have
        been entering the labor force rates. They have reached
        parity with black women.


   These are more particular to US labor market
Thinking about Black-White Gap
   How much of the B/W earnings gap is
    explained by differences in skills that are
    formed prior to market entry?
       This is a great question because so much of
        literature is focused on market discrimination
       Ideal test: Look at identically skilled teens
        before market entry and then again later in
        life: What is the initial earnings gap and does it
        grow over time.
       Ideall, if there are no differences in tastes or
        costs of skill investment, one could attribute
        observed differences to discrimination.
Testing for observed race differences
   Can’t assume differences are all due to
    discrimination but can try to control for
    unobserved innate productivity
       Use NLSY. Sample 15 to 23 years old, who
        took AFQT prior to age 18. Regress age effects
        out of AFQT.
       The basic result. ‘Pre-market’ skill appears to
        explain a large part of racial earnings gap for
        currently employed workers.
Basic Results
   Military has done validation studies using objective
    performance measures. Finds no evidence that test
    systematically under-predicts performance of blacks.

   Given this, do blacks underinvest in skills due to lower
    return?
       In Table 2 and 3, no evidence of differences in return to AFQT.
        But this is an endogenous comparison — conditional on having
        attained a given level of skill.
       Return may have been lower in prior eras, and this could affect
        beliefs of black parents, who choose how much to invest in
        kids’ skills.
       How would you convincingly test this? What does this imply
        that we should see for IV estimates of returns to school for
        blacks versus whites?
Selection into Labor Market
   Low scoring blacks noticeably less likely to
    participate in labor market.

   How do we deal with this? Two approaches:
       Median regressions. If nonparticipants have
        wage offers less than median for group with
        similar observables (race, test score) and at
        least half participate, then the median is
        identified
       Less of the gap is explained once we condition
        on participation.
Selection into Labor Market
 Notice that:
  E(w) = LFPR *E( w | participate)
  + (1−LFPR)*E( w | don’t participate)
 Comparing two groups




   We can rewrite this as:
Adjusting for selection
   Defining a selection factor

   Can adjust the observed earnings ratio by
    a selection factor.
       OLS wage gap: −0.072
       Median gap: −0.134
Adjusting for Selection - 2
   We need to define a few other parameters
       Making a symmetry assumption: kb = kw = 0.1
       LFPRb = 0.91 and LFPRw = 0.94

   Using this we can get:




   Making an assumption on potential earnings of
    non-employed,
   we get back the observed ratio.
       this calculation does not account for AFQT scores
       if score gap is much larger among non-employed, this
        suddenly does not seem so conservative.
Bottom line on Selection
   Appears that selection into pre-labor
    market characteristics has large
    explanatory affect
       We need to worry more about the determinant
        of the X’s
       This motivates much of the discussion on
        gender differences in preferences
       May be even more serious as incarceration
        rates and social welfare participation affect
        minorities
       Important interactions with signaling models
Effects of Selection into LFPR
   Butler and Heckman (1977) : expansions in the
    generosity of transfer programs over the decade
    of the 1960s  reduced LFPR for low-skilled
    workers
       African-American men were more likely to be lower-
        skilled, observed relative wages would increase.
       a preoccupation with the wages of workers would cause
        social scientists to overstate the success of Title VII
        Legislation, or spuriously conclude that discrimination
        against blacks had declined.
       May not be that the Civil Rights Act (CRA) raising the
        relative demand for black labor
       selective withdrawal hypothesis could also rationalize
        the data
Source: Chandra (1998)
Explaining trends in Wages
   Increase in the returns to skill that would occur in
    the 1980s
       a factor which would cause withdrawal to the extent that
        reservation wages were relatively fixed over this period.

   Massive growth in the US prison population as a
    result of the “war on drugs” and the related
    Sentencing Reform Act of 1984
       Mandatory and longer sentencing guidelines for drug-
        related convictions.

   Together could generate convergence in observed
    wages since they disproportionately affect low-
    skilled blacks.
    What do we know about selection?
   Nonparticipation matters: across the entire skill
    distribution, prime-age black men have withdrawn
    from the labor-force at rates that exceed those for
    comparably skilled whites.
       By 1990, 30 percent of blacks versus 6.1 percent for whites
        were not employed during a random reference week in the
        year
       Wages are not observed for 20 percent of prime age black
        men with annual rates at 40 percent for certain black skill
        groups.
       Sample-selection criteria based on weeks worked or hours
        worked also generate convergence in observed wages by
        disproportionately excluding low-skill blacks and thereby
        exacerbating the bias induced by ignoring nonparticipants.
Returning to the Oaxaca Decomposition
   Differences in wages can be thought of as due to
       differences between whites and blacks in observable skill
        (“between skill differences”)
        differences in unobserved skill as well as the possible contribution
        of discrimination (“within skill differences”).

   For those respondents with wages, we may write the observed
    racial (log) wage gap
    ΓObs = E(wbt|z = 1) − E(wwt|z = 1) in year t

   This is a non-parametric version of the familiar Blinder-Oaxaca
    decomposition.
       no problem with overlapping supports since the skill cells are
        constructed separately by race but using the same X’s
       unlike the conventional decomposition where “counterfactual”
        wages of blacks are typically estimated by extrapolating into a
        region of no support, the nonparametric method does not suffer
        from this limitation.
Decomposing Observed Differences
   We can thus decompose observed
    outcomes as:
Adding the Time factor…
   Looking over time and defining selection
    correction weights, we can then define
The effect of Data on Inference
   In the US, a large amount of the inference is based on the
    Current Population Survey, this is similar to the inference
    based on the labor-force surveys
       Most labor force surveys, including the CPS, have the advantage
        of producing a fairly consistent yearly time-series which cover a
        large section of the population
       for example, the CPS is consistent from 1964 onward

   The CPS also come with limitations
       The CPS for example does not contain information on the
        institutionalized population. This omission overstates the
        convergence over time because it ignores the role of increasing
        criminal activity
       the Census provides a more accurate count of the Not in Labor
        Force (NILF) group than does the CPS.
       In 1990, ignoring the non-employed will be shown to understate
        the racial wage gap by 11-16 percentage points; of this, 4-6
        percentage points is the effect of incarceration which would be
        omitted by the CPS.
Imputing Wages for Nonworkers
   Wages for nonworkers are imputed
    assuming that nonworkers are drawn from
    points on the conditional wage offer
    distribution that lie below that of the
    median respondent.
       This method does not rely on the presence of
        arbitrary exclusion restriction to identify the
        counterfactual distribution of wages for
        nonworkers.
       Can construct a non-parametric identification of
        the standard sample-selection model and a
        non-parametric method to decompose the
        mechanisms of convergence is provided.
Selection-Corrected Estimates
   Selectivity-corrected estimates of the
    racial wage gap indicate large differences
       Ignoring nonparticipation in segregated states
        causes estimates of convergence in the 1960s
        to be understated by as much as 15 percent as
        a result of excluding a number of nonworking
        blacks in 1960 from the analysis.
       In contrast to the convergence in the observed
        series from 1970-90, selectivity corrected
        estimates indicate complete stagnation over
        this period with a divergence of 5 percent
        between 1980 and 1990.
Differences in Selection Corrections
Supply-Side Effects
   The recent withdrawal of black men across the skill
    distribution in recent years is a supply-side effect
       by 1990 blacks in the lowest quartile of the offer wage
        distribution had non-participation rates that were 20
        percent higher than whites in the same quintile,
        differences in offer wages explain 40 percent of the
        overall difference in participation.
       Over the 1960-90 period, differences in offer wages
        explain a declining portion of the racial gap in
        employment,
       In 1990, wage elasticities of nonemployment imply that a
        10% increase in offer wages would increase weekly
        participation by 3.5 percent for blacks (about 50 percent
        higher than comparable whites).
Can discrimination explain differences?
   May be blacks are selecting out of labor market
    because of low expected wages or because of
    employers tastes

   Bertrand & Mullinathan: Apply for jobs by
    sending resume by mail or fax.
       Manipulate perceptions of race by using distinctively
        ethnic names.
       Otherwise, hold constant resume characteristics.
       Test: Are ‘callback’ rates lower for distinctively black-
        named applicants?
Does implied race really matter?
 In most cases, names receive equal
  treatment but that’s because in most cases,
  applicants are not called back.
 Black names appear to benefit less from
  resume enhancements (such as honors,
  more experience) than do whites.
       Authors view this as evidence against statistical
        discrimination — should work in opposite direction
        they believe.
       Discrimination based on zip-code characteristics
        appears quite important, and does not
        systematically differ between white and non-white
        names.
Response Papers on Names
   Response by Levitt and Fryer: detailed
    birth records data that non-white names
    not substantially correlated with life
    outcomes once one conditions finely on
    mother/birth characteristics.
       B-M response: Not a surprise; it’s not the
        name, and that’s name is taken as proxy for
        race when race is shielded.
Can this be eliminated by the market?
   Economists typically study the economics of
    discrimination but not the process by which
    discrimination is remedied (through, for example,
    shifts from segregation to integration)

   Goff, McCormick and Tollison (AER, 2002) plan is to
    model desegregation as innovation in order to
    answer: What type of firm takes advantage of an
    innovation early and why?

   Use the case of professional sports
       Easy to measure outcomes
       Observable quality of both firms (e.g. teams) and individual
        players in baseball and basketball
Theories of Integration - 1
   Worst-first: The worst firms have the most to
    gain from the recruitment of a larger pool of
    talent that includes minorities.
       The problem with this is that the poor performance
        of these teams may be due to poor management,
        which may also render them unaware of potential
        profit gains from desegregation.
        This hypothesis is based on competitive rivalry
       Using league standing as a measure of
        performance, this theory predicts that “the farther
        a team is back in the league standings, the more
        likely it is to be a leader in integration”

Theories of Integration - 2
   Best-first: If these teams perform better
    because of better management, then they
    may better understand the profit
    opportunities offered by integration.
       This hypothesis is based on managerial
        capability.
       Using league standing again as a measure of
        performance, “teams with the best winning
        records and better management are more
        likely to be leaders in the integration process.”
Estimation: Baseball
   BLACKit = at + b0 + b1(Games Back)it-1, +
    b2(Median Income)it +b3 (% Nonwhite)it + eit ,
   Where:
       BLACKit = number of black players on team i in
        year t
       Games Backit-1 = number of games out of first
        place by team i in year t-1;
       Median Incomeit = median family income (in
        1950 dollars) for team i in year t; and
       Percentage Nonwhiteit = percentage of
        population that is non-white in year t for team i.
Results Baseball
   We should see b1 > 0 if the worst teams
    integrated first and b1 < 0 if the best
    teams integrated first.

   The negative coefficients are consistent
    with the best-first hypothesis (managerial
    ability).
       These finding also suggests that competition in
        the NL, led by the Brooklyn Dodgers, forced
        other NL teams to integrate rapidly or lose,
       absent Branch Rickey in the AL, the overall
        pace of integration was slower.
Table 2: Estimates of Major           League Baseball Integration, 1947-1971
Variable              Coefficient
Intercept             7.49 (7.96) *
Games Black           -0.03 (4.64)*
Median Family         -0.16 (-1.32)
Income
Percentage Non-white -0.01 (-0.32)
R2                    0.58
F-statistic           21.10
Year Effects
        1947      -5.88*              1955   -3.06*          1963    -1.19*
                  (-7.74)                    (-4.50)                 (-2.03)
        1948      -6.08*              1956   -2.08*          1964    -1.23*
                  (-8.12)                    (-4.19)                 (-2.11)
        1949      -5.79*              1957   -2.66*          1965    -0.68
                  (-7.85)                    (-4.02)                 (-1.18)
        1950      -5.67*              1958   -2.19*          1966    -0.05
                  (-7.78)                    (-3.38)                 (-0.04)
        1951      -5.09*              1959   -2.05*          1967    -0.24
                  (-7.10)                    (-3.20)                 (-0.40)
        1952      -4.85*              1960   -2.34*          1968    0.26
                  (-6.09)                    (-3.79)                 (0.45)
        1953      -4.70*              1961   -1.90*          1969    0.84
                  (-6.73)                    (-3.00)                 (1.47)
        1954      -3.58*              1962   -1.69*          1970    0.59
                  (-5.18)                    (-2.80)                 (1.08)
Other results
   They find similar results in college basket
    ball, where good managers are the first
    adopters of black players
       Can think of this as “new technology”
       Best managers adopt new productivity
        enhancing technology faster
       Then should be the case that black players in
        professional baseball and college basketball,
        around the time of integration, were better
        than their white counter parts
Shifting Gears a little: What about trends
in Gender Differences?
   The gender earnings ratio began to increase in
    the late 1970s or early 1980s.
   Convergence has been substantial: between 1978
    and 1999 the weekly earnings of women full-
    time workers increased from 61 percent to 76.5
    percent of men's earning and flattened out by the
    mid-1990s
   Could be that :
       Women are encountering less discrimination than
        previous ones
       an upward progression over time in the gender ratio
        within given cohorts
Human Capital Explanations
   Usually analyzed within the human capital
    model (Mincer and Polachek, 1974)
       The idea: division of labor by gender in the
        family, women tend to accumulate less labor
        market experience than men.
       women anticipate shorter and more
        discontinuous work lives, they have lower
        incentives to invest in market-oriented formal
        education and on-the-job training
       choose occupations for which on-the-job training
        is less important, gender differences in
        occupations would also be expected
Non-human Capital Explanations
   Specific Capital
       Shorter experience/tenure at firms
       employers may be reluctant to hire women for such jobs
        because the firm bears some of the costs of such firm-
        specific training and fears not getting a full return on that
        investment.

   Labor market discrimination may also affect
    women's wages and occupations.
       "statistical discrimination," differences in the treatment of
        men and women arise from average differences between
        the two groups in the expected value of productivity
       discriminatory exclusion of women from "male" jobs can
        result in an excess supply of labor in "female"
        occupations, depressing wages there for otherwise equally
        productive workers
Testing for Discrimination
   Difficult to observe employer tastes
       Can we separate human capital differences from some
        form of discrimination
       Set aside, for the moment, pre-market discrimination


   Clever natural experiments by Goldin & Rouse
    using orchestra
       Symphony orchestras used to be all male in the past and
        have slowly hired female musicians in the post-war period.
       Many orchestras introduced screens during auditions in the
        1970s, so that the judges wouldn’t be able to see the
        gender of a performer.
       data on auditions for about four decades for 8 major US
        symphony orchestras.
Estimating Differences after Blind Auditions
    Simple difference in success rates in screened and
     not-screened auditions
        On average, women do worse on blind rounds
        this could be due to changing composition of female pool.
        Possible that only the very best women competed when
         the game was lopsided.

    Limited to musicians (male and female) who
     auditioned both blind and non-blind suggest that
     women did relatively better in blind rounds (diff-
     females minus diff-males)
        All of these models exclude orchestra fixed effects
         because there is no ‘within’ variation in orchestra blind
         policies.
        Modeling changes for the 3 orchestras that switch policies,
         can include musician and orchestra fixed effects
Glass Ceilings
   One particular form of discrimination might be
    glass ceilings
       women not being promoted to higher level jobs.
       If this is due to discrimination, then we would expect the
        women who do get promoted to be particularly able.

   Wolfers (2006) looks at the stock returns to
    female headed S&P 1500 firms
       very slightly negative returns for female headed firms
        (though not many observations)
       This result implies either there is no discrimination, or if
        there is discrimination, the market completely
        understands how much better the women are and
        values the firms correspondingly
       May be incorporated in stock prices and didn’t study the
        announcement of hiring a female CEO instead
Can we explain differences with preferences?
    Hard to do in a non-experimental setting
        Rarely observe controlled enough conditions to
         isolate preference related response
        Typically want to have more stylized setting


    What might generated differences in
     outcomes?
        Preference for competition
        Negotiations
Difference in Response to Competition
   Gneezy, Niederle, and Rustichini (2003): lab experiment.
    Students at and Israeli engineering school were asked to solve
    mazes on a computer.
   The experimental sessions were run with six students at a time
    working in a room. They were paid according to how many
    mazes they solved using different payment schemes:
       An individual piece rate: 2 shekels per maze solved
       A piece rate with randomization: Among a group of six subjects,
        only one was selected randomly to be paid at the end, and that
        individual received 12 shekels
       A single sex tournament: Only the participant in the group solving
        the most mazes was paid 12 shekels for every maze solved, and
        groups consisted of six men or six women
       A mixed tournament: Only the participant in the group solving the
        most mazes was paid 12 shekels for every maze solved, and
        groups consisted of three men and three women
Gender Differences in Competition
   Performance was the same for piece rates and
    piece rates with randomization.
       Men solved significantly more mazes in either of the
        tournaments, and their performance was similar in the
        single sex and mixed tournaments.
       Men solve slightly more mazes than women.
       Otherwise women’s performance resembled that of
        men’s, except in the mixed
       In the tournament were women performed only as well as
        in the piece rate scheme.


   Results suggests that women compete less in
    mixed gender environments.
       This may be rational because they expect the slightly
        better men to win anyway or due to stereotypes
Testing “Choking” behavior
   Not quite the same but related question: Do women
    perform differently (worse) than men in competitive
    environments?

   Paserman analyzes data from tennis Grand Slam
    tournaments, played by professional tennis players for
    rather high stakes.
       He analyzes how the performance of men and women in
        singles matches varies with the stakes within a tournament
       To do this, he assigns each point a significance based on the
        how winning the point affects the probability of winning the
        entire match
       uses the classifcation of each points into winners, forced
        errors, and unforced errors,
       and focuses on unforced errors as evidence of choking.
“Choking” Evidence
   Results:
       Simple comparison of outcome of a play and the
        importance of a point: Not much of a relationship for
        men, but women make more unforced errors in more
        important points.
       More formally, including match fixed effects: men actually
        make fewer unforced errors as the stakes get higher,
        women more.
       Women become more conservative on both measures,
        men hit faster first serves, slower second serves, and also
        have longer rallies.
       Higher risk aversion of women could possibly explain
        these results
   Again, stylized setting, hard to interpret
    magnitudes or generalizability
Women and Negotiations
   The Theory: It’s not preferences over
    competition/work environment/etc. it’s
    failure to ask for money that generates
    differences in observed outcomes

   Babcock and Laschever (2003) suggest
    that women expect lower salaries and are
    less likely to ask for higher salaries in the
    workplace. They present some evidence
    that this results in lower pay for women.
Women and Negotiations - 2
   Some evidence:
       Survey of starting salaries of Carnegie Mellon
        Students. Women earn 8% less. 7% of women
        negotiated but 57% of men negotiated. Those who
        negotiated were able to raise their starting salaries
        by about 7%.
       Lab experiment where participants were told they
        would be paid between 3 and 10 dollars at the end.
        Everybody was actually offered only 3 dollars at the
        end. 9 times as many men as women asked to be
        paid more.
       Internet survey of individuals negotiation behavior
        in real life. Women negotiate less often than men.
Bottom Line
   There is some “discrimination”. In recent data, this is
    probably more important with respect to race/ethnicity than
    with respect to gender, although there was probably a lot of
    gender discrimination in the past.
   It is less of a settled issue what the causes for
    discrimination are, i.e. whether it is statistical or taste
    based. However, evidence for rational statistical
    discrimination is weak
   Particularly for women, other things seem to matter a lot.
    Different preferences, potentially different skill sets,
    different opportunity costs all seem to play a role

				
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