INEQUALITY, RACIAL DISPARITY, AND STRATIFICATION ECONOMICS by Darrick

INEQUALITY, RACIAL DISPARITY, AND STRATIFICATION ECONOMICS by Darrick

INEQUALITY, RACIAL DISPARITY, AND STRATIFICATION ECONOMICS by Darrick Hamilton Associate Professor of Economics and Urban Policy Director, Doctoral Program in Public and Urban Policy THE IMPORTANCE OF WEALTH Wealth indicates economic opportunity, security & overall wellbeing Wealth provides for a human capabilities approach to economic development Primary source is intergenerational Structural not behavioral The economic indicator in which whites & communities of color are most disparate 2 Calculated by Matt Bruenig Demos 3 MEDIAN LIQUID ASSET VALUE: ASSETS EASILY CONVERTED TO CASH (SIPP 2011)

Median Liquid Wealth Holdings, 2011 SIPP 4 RHETORIC We are post-racial America has largely transcended the racial divide A shift from social responsibility for the conditions of black America Blacks are enjoined to; get over it stop playing the victim role stop making excuses take personal responsibility Study hard, graduate from college and get a good job 5 6 Studying hard is not enough 7 8 9

THE GREAT RECESSION AND THE RACIAL WEALTH GAP (SIPP DATA) 10 LESSONS LEARNED IN THE AFTERMATH OF THE GREAT RECESSION 1 Black & Latino families have little liquid assets to take risk, or deal with financial emergency or shocks 2 Communities of color suffered the most The racial wealth gap was extreme before the recession, and worsened after 3 Asians suffered the largest absolute loss in home values and wealth Most likely to reside in states that benefited from the housing boom & suffered most from the housing bust 11 AN INCOMPLETE NARRATIVE Asset markets are local e.g. the geographic maldistributive effects of the housing crisis The wealth position of many communities of color

remains unknown Aggregate categories like Asian mask the asset position for certain groups like those immigrating from Southeast Asia Indigenous groups are often hidden altogether in nebulous catchall category of other 12 Goals: 1. provide implicit control of asset and debt pricing and products 2. analyze the wealth of groups hidden in broadly defined non-white categories 3. examine asset and debt attributes particular to communities of color 4. Provide a template for a more permanent data collection infrastructure Limitations: (1) Statistical Power, (2) External validity and (3) Examines only Private Assets 13 SOME LESSONS ACROSS FIVE CITIES Variation within broadly defined ethnic categories Income inequality pales in comparison to wealth inequality An ethnic groups relative asset position may vary across

city Homeownership varied across city and may not be the only driver of wealth Substantial asset variation across and within cities with Blacks and Mexicans persistently at the bottom Race is still a defining attribute of ones life chances Wealth is the single best indicator of economic opportunity, security & human capability The primary source of wealth is inheritance, in-vivo transfers -- seed money to purchase an appreciating asset Education, hard work, income, and active savings do little to address the racial wealth gap The racial wealth gap is structural not behavioral 15 WHAT CAN WE DO? BABY BONDS SOCIAL SECURITY OVER THE LIFE COURSE Federal Trust accounts endowed based on family wealth position at birth Estimated to cost about 2% of current federal expenditures per annum Brake the cruel link between race, inheritance & economic

advantage or disadvantage Provide opportunity for upward mobility & economic security for all Americans regardless of the family in which they are born 16 FROM DARK TO LIGHT: THE ROLE OF SKIN SHADE IN DETERMINING LIFE OUTCOMES MEDIA IMAGES OF BEAUTY PRESENTED BY MARGRET HUNTER, COLORISM CONFERENCE, DUKE UNIVERSITY, MARCH 2008 SAMMY SOSA ON MAY 13, 2009 IN NEW YORK AND ON NOVEMBER 4, 2009 IN LAS VEGAS AFTER USING A COSMETIC FACIAL CREAM HEADLINE FROM APRIL 11, 2011 ARTICLE IN THEGRIO FROM AP ARTICLE BY DAVID MCFADDEN SKIN BLEACHING A GROWING PROBLEM IN JAMAICA SLUMS In this photo taken Feb. 15, 2011, a woman applies skin lightening cream to her legs as she sits on a curb in downtown Kingston, Jamaica. (AP Photo/Caterina Werner

FROM DARK TO LIGHT SKIN COLOR AND WAGES AMONG AFRICAN AMERICANS JOURNAL OF HUMAN RESOURCES, 2007 by Arthur Goldsmith, Darrick Hamilton, and William Darity, Jr. LABOR MARKET FINDINGS In the wake of the Civil Rights Movement skin shade differentials (colorism) remain an important determinant of socioeconomic status The conventional One-Drop view ignores the heterogeneity of the labor market experiences of black sub-groups. The U.S. context may not be so different from Latin America The penalties attached to skin shade are not strictly imposed internally The significance ascribed to cultural factors in explaining racial differences should be re-examined in light of the evidence of skin shade differences SHEDDING LIGHT ON MARRIAGE THE INFLUENCE OF SKIN SHADE ON MARRIAGE FOR BLACK FEMALES JOURNAL OF ECONOMIC BEHAVIOR AND

ORGANIZATION, 2009 by Darrick Hamilton, Arthur Goldsmith, and William Darity, Jr. MARRIAGE MARKET FINDINGS An intra-racial marriage gap based on skin shade exists among young black females Young light skinned black females are about 14 percent more likely to marry than their darker skinned counterpart The relationship between skin shade and marriage appears to be exacerbated by the paucity of marriageable black males Mixed race black females have access to a larger pool and better quality of males The pool of marriageable black males is further diminished by better quality black males marrying outside their race Marriage promotion policies directed at the female demand (attitudes and desires) for marriage may have the unintended consequence of generating more colorism in the black community DATA Multi City Study of Urban Inequality (MCSUI) 1992 National Survey of Black Americans (NSBA) 1979 Likert Scale Interviewer Coded Skin Shade

Measures Surveys have a rich array of economic, demographic, workplace, neighborhood and family background information SUMMARY STATISTICS Whites earned $1.52, $2.71, $4.22 more per hour than light, medium and dark skinned black males Whites had better human capital, family background and neighborhood characteristics Although light skinned blacks had better human capital, they had similar family background, and neighborhood characteristics as their darker counterparts Result is similar to Hughes and Hertel (1990) findings Data Source: MCSUI Dependent Variable: ln hrwage rate Variables Black Model 1 (n=968) Model 2

(n=948) Model 3 (n=948) Model 4 (n=921) Model 5 (n=921) -0.262*** (0.033) -0.155*** (0.028) -0.100*** (0.030) -0.121*** (0.029) -0.072** (0.031)

Rainbow Model Variables Model 1 (n=968) Model 2 (n=948) Model 3 (n=948) Model 4 (n=921) Model 5 (n=921) Light Black -0.134* (0.075) -0.076 (0.060)

-0.031 (0.061) -0.036 (0.060) -0.001 (0.060) Medium Black -0.246*** (0.044) -0.166*** (0.037) -0.112*** (0.039) -0.153*** (0.037) -0.106*** (0.038)

Dark Black -0.305*** (0.041) -0.168*** (0.036) -0.110*** (0.037) -0.116*** (0.036) -0.063* (0.038) H0: Light Blk Dark Blk = 0 4.58** 2.02 1.52 1.55

1.02 H0: Light Blk Med Blk = 0 1.91 1.94 1.59 3.32* 2.85* H0: Med Blk Dark Blk = 0 1.91 0.00 0.00 0.75 1.02

Human Capital & Demographics yes yes yes yes Work Place Characteristics yes yes yes yes Family and Neighborhood Occupation yes

yes yes yes Data Source: National Survey of Black Americans Dependent Variable: ln hrwage rate Rainbow Model Variables Model 1 (n=347) Model 2 (n=331) Model 3 (n=331) Model 4 (n=331) Model 5 (n=331) Light Black

0.183** (0.077) 0.107* (0.061) 0.126** (0.062) 0.107* (0.059) 0.133* (0.060) Dark Black 0.011 (0.050) 0.0123 (0.0340 0.030 (0.042)

-0.003 (0.038) -0.013 (0.040) 4.91** 2.37 2.23 3.50* 3.85* Human Capital & Demographics yes yes yes yes

Work Place Characteristics yes yes yes yes H0: Light Blk Dark Blk = 0 Controls for: Family and Neighborhood Occupation yes yes yes yes Data Source: MCSUI Dependent Variable: ln wage rate

Pre-Market Factors Only Variables Black Model 1 (n=968) Model 2 (n=968) Model 3 (n=968) Model 4 (n=968) Model 5 (n=968) -0.259*** (0.033) -0.224*** (0.031)

-0.255*** (0.031) -0.222*** (0.031) -0.158*** (0.032) Rainbow Model Variables Model 1 (n=968) Model 2 (n=968) Model 3 (n=968) Model 4 (n=968) Model 5 (n=968)

Light Black -0.104 (0.071) -0.090 (0.068) -0.097 (0.071) -0.086 (0.069) -0.020 (0.069) Medium Black -0.233*** (0.042) -0.214*** (0.041)

-0.229*** (0.042) -0.212*** (0.041) -0.150*** (0.041) Dark Black -0.318*** (0.039) -0.266*** (0.039) -0.313*** (0.039) -0.264*** (0.039) -0.200*** (0.040)

H0: Light Blk Dark Blk = 0 8.07*** 5.75** 8.30*** 5.94*** 6.16** H0: Light Blk Med Blk = 0 2.83* 2.75* 3.00* 2.87* 3.17* H0: Med Blk Dark Blk = 0

3.05* 1.21 2.99* 1.23 1.12 yes yes yes yes yes yes yes yes

yes Age Completion of HS by Age 19 Poor HS performance Family Background yes yes yes Median Regression: Includes the Unemployed Data Source: MCSUI Dependent Variable: ln wage rate Variables Model 1 (n=1170) Model 2 (n=1170)

Model 4 (n=1170) Black -0.336*** (0.058) -0.205*** (0.055) -0.157*** (0.046) Rainbow Model Variables Model 1 (n=1170) Model 2 (n=1170) Model 4 (n=1170)

Light Black -0.285** (0.131) -0.064 (0.105) -0.115 (0.010) Medium Black -0.270*** (0.077) -0.188*** (0.064) -0.190*** (0.064) Dark Black -0.403*** (0.071)

-0.240*** (0.061) -0.183*** (0.061) H0: Light Blk Dark Blk = 0 0.73 2.49 0.42 H0: Light Blk Med Blk = 0 0.01 1.20 0.51 H0: Med Blk Dark Blk = 0 2.28

0.51 0.01 yes yes Human Capital & Demographics Work Place Characteristics Family and Neighborhood Occupation yes Pre-Market Factors Only Median Regression Data Source: MCSUI Dependent Variable: ln wage rate Variables Model 1 (n=1171)

Model 2 (n=1171) Model 3 (n=1171) Model 4 (n=1171) Model 5 (n=1171) Black -0.371*** (0.057) -0.272*** (0.045) -0.384*** (0.051) -0.267*** (0.046)

-0.226*** (0.059) Rainbow Model Variables Model 1 (n=1171) Model 2 (n=1171) Model 3 (n=1171) Model 4 (n=1171) Model 5 (n=1171) -0.064 (0.126) -0.065

(0.116) -0.119 (0.119) -0.060 (0.108) -0.057 (0.109) Medium Black -0.319*** (0.074) -0.274*** (0.068) -0.351*** (0.070) -0.265*** (0.064) -0.222***

(0.066) Dark Black -0.454*** (0.069) -0.339*** (0.064) -0.445*** (0.065) -0.345*** (0.060) -0.322*** (0.063) H0: Light Blk Dark Blk = 0 8.60*** 4.96** 7.20***

6.26** 5.34** H0: Light Blk Med Blk = 0 3.55* 2.78* 3.29* 3.13* 2.00 H0: Med Blk Dark Blk = 0 2.47 0.68 1.67 1.17

1.86 yes yes yes yes yes yes yes yes yes Light Black Age Completion of HS by Age 19 Poor HS performance

Family Background yes yes yes LABOR MARKET FINDINGS In the wake of the Civil Rights Movement skin shade differentials (colorism) remain an important determinant of socioeconomic status The conventional One-Drop view ignores the heterogeneity of the labor market experiences of black sub-groups. The U.S. context may not be so different from Latin America The penalties attached to skin shade are not strictly imposed internally The significance ascribed to cultural factors in explaining racial differences should be re-examined in light of the evidence of skin shade differences SHEDDING LIGHT ON MARRIAGE THE INFLUENCE OF SKIN SHADE ON MARRIAGE FOR BLACK FEMALES JOURNAL OF ECONOMIC BEHAVIOR AND ORGANIZATION, 2009 by

Darrick Hamilton, Arthur Goldsmith, and William Darity, Jr. MARRIAGE: STYLIZED FACTS Racial Marriage Gap: Middle of the 20th Century 1950 percentage of never married 24-29 year olds (Mason,2006) Black women 16% White women 14% Racial Marriage Gap: End of the 20th Century 1996-98 percentage of never married 24-29 year olds (Mason, 2006) Black women 61% White women 34% WHY IS THE MARRIAGE RATE FOR BLACK FEMALES SO LOW? Sex Ratios: 1991-98 for unmarried civilian individuals aged 23-27 (Mason, 2006) 71 black males : 100 black females 119 white males : 100 white females 45 black males working full-time : 100 black females 90 white males working full-time : 100 white females Inter-Racial Marriage In 1990, 97% of married black females aged of 25-34 were

married to black males (Farley, 1996) Too Few Marriageable Black Males (Wilson and Neckerman, 1986; Ellwood and Crane, 1990; Darity and Myers, 1995); Low educational attainment, high drug use, high mortality, and high incidence of incarceration THE DECLINE IN MARRIAGEABLE MALES: EROSION OF RELATIVE LABOR MARKET PROGRESS From 1940 to mid to late 1970s, dramatic relative labor market progress for black males (smith and Welch, 1989) Stagnation thereafter, so What went wrong? (Bound and Freeman, 1992) (T)he environment for black achievement worsened Shift in federal policy away from legislation and enforcement against discrimination THE DECLINE IN MARRIAGEABLE MALES: INCARCERATION Seven fold increase in prison population from 19702007 (The Sentencing Project based on BJS statistics) 1970: 200,000 federal and state prisoners 2007: 1.5 million federal and state prisoners (2.3 million if local jails are included)

Disproportionate share of young black male (aged 2529) are incarcerated and greater likelihood of black males serving time in prison (Sentencing Project) 10.5 Percent young black males 1.7 percent young white males 32 percent lifetime likelihood of black male serving time Six percent lifetime likelihood of white male serving time MARRIAGE PROMOTION POLICIES AND THE LINK BETWEEN SKIN SHADE AND MARRIAGE Healthy Marriage Initiative (demand side policies) A hallmark of the George W. Bush Administration $200 million spent through the Federal Administration for Children and Families to promote marriage, especially targeted towards teenagers 2006 welfare legislation includes a $500 million appropriation over the next 5 years to promote marriage An alternative approach is to enlarge the supply of marriageable black males (supply side policies)

Improving educational opportunities Reducing labor market discrimination and barriers to employment Creating environments that promote personal and healthy development Initiating policies aimed at reducing the incarceration rate (particularly for non-violent criminals) MARRIAGE MARKET FINDINGS An intra-racial marriage gap based on skin shade exists among young black females Young light skinned black females are about 14 percent more likely to marry than their darker skinned counterpart The relationship between skin shade and marriage appears to be exacerbated by the paucity of marriageable black males Mixed race black females have access to a larger pool and better quality of males The pool of marriageable black males is further diminished by better quality black males marrying outside their race Marriage promotion policies directed at the female demand (attitudes and desires) for marriage may have the unintended What my work suggests about the conventional wisdom? 1.

2. 3. 4. In the wake of the Civil Rights Movement, skin shade still remains relevant in labor markets, and marriage markets The characterization that the U.S. is strictly stratified by genotype and Latin America by phenotype is inaccurate The claim that skin shade penalties are strictly imposed intra-racially is not consistent with our evidence of labor market discrimination The significance ascribed to cultural factors in explaining racial differences should be re-examined in light of the evidence of skin shade differences 42 43

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