What’s Policy Got to Do with It? - IA de Investigación especializada en el MIT

What’s Policy Got to Do with It?
Carrera, Gender & Economic Inequality
in the United States

Jamila Michener & Margaret Teresa Brower

In the United States, economic inequality is both racialized and gendered, con
Black and Latina women consistently at the bottom of the economic hierarchy. Rel-
ative to men (across racial groups) and White women, Black and Latina women
often have less-desirable jobs, lower earnings, and higher poverty rates. In this es-
decir, we draw attention to the role of the state in structuring such inequality. Specif-
icamente, we examine how public policy is related to racial inequities in economic posi-
tions among women. Applying an intersectional lens to the contemporary landscape
of economic inequality, we probe the associations between public policies and eco-
nomic outcomes. We find that policies have unequal consequences across subgroups
of women, providing prima facie evidence that state-level decisions about how and
where to invest resources have differential implications based on women’s race and
etnicidad. We encourage scholars to use aspects of our approach as springboards for
better specifying and identifying the processes that account for heterogeneous policy
effects across racial subgroups of women.

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I n the United States, economic inequality is both racialized and gendered.1

This means that the intersecting categories of race and gender are system-
atically associated with wide disparities in economic outcomes. For exam-
por ejemplo, women across racial groups earn less income than men, but Black and Lati-
na women earn less than both White women and Black and Latino men.2 Similar
patterns occur across a variety of economic indicators. In terms of income, pov-
erty, and employment, Black and Latina women remain marginalized: ellos tienen
the lowest earnings, face the most intense occupational segregation, and have the
highest poverty rates.3

Sociologists, economists, and other social scientists have identified a host of
factors that explain the relative economic status of Black and Latina women. Racial
discriminación, constrained social networks, labor market inequities, and much
more underlie the processes that generate disparate material outcomes for Wom-
en of Color.4 Still, there is a lot we do not know about the mechanisms that stratify

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100

Black and Latina women. En particular, scholars have an inadequate understanding
of how public policy affects women’s economic positioning by gender and race.

En este ensayo, we investigate whether and how social and economic policies dif-
ferentially shape women’s economic positioning across racial and ethnic groups.
We begin by charting disparities between White women and Women of Color
across a range of key economic indicators including educational attainment, em-
ployment, wages, and poverty. Entonces, we assess statistical associations between
economic outcomes and state-level policies for White, Negro, and Latina wom-
en. We find substantial heterogeneity in the relationships between economic pol-
icies (such as minimum wage laws and disability insurance), social policies (semejante
as cash, alimento, and medical assistance), and the economic status of women across
racial and ethnic groups. Our empirical and theoretical approach is grounded in
the concept of intersectionality, a framework developed by Black feminist schol-
ars to capture how a multiplicity of intersecting social identities determine one’s
fuerza, life experiences, political interests, and more.5 By adopting an intersec-
tional approach, scholars can study heterogeneous groups with more nuance, re-
maining attentive to various junctions of different social positions and catego-
ries. Applying the lens of intersectionality to questions about economic inequal-
ity prompts us to investigate the ways that Women of Color–specifically Latina
and Black women–are affected by social and economic policies relative to their
White counterparts. Doing so reveals the complex role of the state in gendering
and racializing economic inequality.

N umerous factors shape race and gender inequalities in economic out-

comes, but we stress the role of policy, bringing the state more into
view.6 Concentrating on social and economic policies–primary levers
through which government determines and regulates access to resources–is im-
portant for three reasons.

Primero, policy is uniquely vital to producing and reducing inequality. The state wields
enormous power to differentially determine the fortunes of its denizens.7 The
New Deal of the 1930s offers especially pertinent lessons on how policy can cre-
ate, maintain, and exacerbate racialized and gendered economic inequality.8 One
of the centerpieces of the New Deal–Social Security/OAI (Old Age Insurance)–
included provisions that disqualified workers in the agricultural and domestic in-
dustries.9 These provisions meant that nine out of ten African American women
workers were automatically rendered ineligible.10 Social Security did not incor-
porate domestic workers until 1948 and agricultural laborers were left out until
1950.11 Despite its prominent status as “the closest thing to a race-blind social pro-
gram the United States has ever known,” Social Security was marked by inequi-
ty at its origins. This was particularly consequential for Black women, who lost

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149 (1) Winter 2020Jamila Michener & Margaret Teresa Brower

state-based financial resources for well over a decade during a time when others
were gaining them.12 Policy matters for inequality.

The second reason we center policy in our analytical approach is because it is
amenable to change. When the design or implementation of policy exacerbates in-
equality, Responsables políticos, advocates, and other engaged members of the political
community can work to modify and improve it. The ability of such actors to ad-
vance change hinges upon knowledge about how public policy affects economic
inequality. To extend the previous example with a more contemporary focus, So-
cial Security continues to have disproportionate effects on Americans by race and
etnicidad, with lower total benefit amounts for People of Color.13 This disparity is
no longer the result of occupational exclusion. En cambio, it stems from larger struc-
tural realities: Black and Latino Americans spend fewer years in the workforce,
make less income from work, and do not live as long as their White counterparts.14
Unless we are attentive to such policy inequities, we can neither conceptualize nor
configure policy to account for such disproportionalities.15

The third reason we emphasize policy is because it reflects and affects democracy.
Political institutions that are part and parcel of the democratic process produce
and enable economic inequality. Federalism, por ejemplo, exacerbates racialized
economic inequality through social policy. Históricamente, Aid for Dependent Chil-
niños (cash assistance) resulted in unbalanced welfare coverage by race and eth-
nicity, with Black Americans receiving significantly less than their White coun-
terparts.16 More contemporary cash assistance programs, such as Aid to Fami-
lies with Dependent Children (AFDC) and its successor Temporary Assistance for
Needy Families (TANF), have also been marked by the institution of federalism
in ways that reinforce economic disparities by geography, carrera, and ethnicity.17
Even in-kind benefits like health insurance proliferate such inequities through the
mechanism of federalism.18 These differential outcomes by state reveal the ways
policies are shaping Americans differently within a federated political structure.
By determining access to and experiences with government resources meant to
bolster economic security, the political institutions that contour the delivery of
public policy both reflect and affect democratic politics. Such processes of poli-
cy feedback–the term used to describe the recursive relationship between policy
and politics–have profound implications for democracy.19 Given the relationship
between policy and democracy, it is imperative to assess the connections between
public policy, economic inequality, carrera, y género.

W hen the economy goes through a process of restructuring, resulting

changes affect individuals differently based on their gender, class,
carrera, and ethnic positioning in the social hierarchy. Por ejemplo, el
industrial restructuring of the economy between the 1970s and 1990s had dispa-
rate effects on Americans by race and gender.20 Sociologist Irene Browne found

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Dédalo, la Revista de la Academia Estadounidense de las Artes & SciencesRace, Gender & Economic Inequality in the United States

that processes of reindustrialization during this period disproportionately affect-
ed young Black women who experienced high increases in unemployment as a re-
sult of the expansion of retail trade industries.21 Young White women were not
similarly affected. Although the 1980s are often depicted as an era that reduced
economic inequalities for women, Black women actually experienced greater eco-
nomic inequality, decreased earnings, and increased unemployment during this
time.22

El 2007 recession is another important instance of how economic condi-
tions divergently shape the lives of women. During the recession, Black and Lati-
na women across levels of educational attainment experienced the highest un-
employment rates compared with women from other racial and ethnic groups.23
Even after the recession officially ended, the unemployment rates for Latina and
Black women remained high: the number of Latina and Black young women who
were unemployed increased from 25.3 por ciento en 2007 a 40.5 por ciento en 2010.24
Similarmente, while the postrecession poverty gap between men and women reached
a historic low in 2010 (con 16.2 percent of women and 14.0 percent of men living
in poverty), poverty rates were highest among Latina and Black women.25 Both
historical and contemporary economic shifts highlight the exceptionally precari-
ous position of Women of Color in the American economy.

Public policies are widely purported to provide stability and security in the
face of such precarity. But do policies counterbalance the racial disproportionali-
ties of the economy or do they perpetuate such imbalances? This question is too
large for any single essay. De este modo, we focus deliberately on social and economic pol-
icies designed to support those who are most vulnerable to shifts in the economy,
with an emphasis on the divergent implications of such policies for women who
are differentially positioned within the labor market.

T he social policies we are most concerned with are those primarily directed at

helping people to secure the necessities of material survival like food, med-
ical care, and cash. Key social policies include the Supplemental Nutrition
Assistance Program (SNAP), TANF, Medicaid, and the Special Supplemental Nu-
trition Program for Women, Infants, and Children (WIC). A diferencia de, the economic
policies we emphasize are less oriented toward providing specific material resourc-
es and more geared toward shaping the structure and returns of the labor market.
Such economic policies include minimum wage laws, prevailing wage laws, trabajar-
ers compensation policies, and disability insurance policies. Admittedly, some pol-
icies–like the earned income tax credit (EITC)–straddle the boundaries of the pol-
icy domains we delineate. Notwithstanding the fluidity of the division between so-
cial and economic policies, highlighting this difference is useful for several reasons.
Primero, it maps onto practice. Many scholars, practitioners, and policy-makers
implicitly (and sometimes explicitly) consider these policy realms as separate

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149 (1) Winter 2020Jamila Michener & Margaret Teresa Brower

dominios. Segundo, these policy categories have different implications for the ex-
periences and needs of women. Social policies generally meet the basic needs of
women across various strata of the labor market, with a particular applicability
to women living in or near poverty. Economic policies are most relevant to wom-
en who are (or have recently been) empleado, particularly those occupying low-
wage jobs.

This distinction informs the design of the empirical analysis that we offer be-
low by helping us to develop expectations about how policies should affect wom-
en. In particular, we anticipate that social policies will matter most for women
who are unemployed and economic policies will be most consequential for wom-
en who are employed. En efecto, social policies provide unemployed women with
supplemental income, resources, and public services (such as food stamps and
Medicaid) while economic policies tend to provide benefits associated with being
empleado (such as tax credits and workers compensation).

In addition to these core assumptions concerning labor market positioning
and policy type, we also expect that both social and economic policies will have
distinct implications for women across racial groups. Existing research provides
us with a basis for anticipating dissimilar policy effects across racial and ethnic
grupos. Por ejemplo, recent studies indicate that TANF, a particularly salient so-
cial policy, exacerbates the Black-White child poverty gap.26 Even more general-
ly, access to the benefits that Latina and Black women disproportionately rely on
is often quite constrained: research suggests that 88 percent of women in poverty
with children–many of whom are Women of Color–are not receiving social ben-
efits like cash assistance or food and nutritional benefits.27

Economic policies follow a similar pattern. In the 1970s and 1980s, econom-
ic nondiscrimination policies such as the Equal Employment Opportunity Act
(EEO) were used as a political tool to reduce gender inequality in the labor force.
Yet these policies did not shift racialized inequality among women.28 While the
EEO had the largest effect on Black women’s economic position compared with
White women, Black women still experienced less wage gains overall compared
with White women.29 Moreover, decades after the EEO, Black and Latina wom-
en continued to experience labor market discrimination, which affected their em-
ployment status, wage earnings, and economic mobility.30

Altogether, interdisciplinary research on race and public policy gives us sub-
stantial reason to expect that both social and economic policies will have differen-
tial consequences across racial and ethnic groups.

T o explore this hypothesis, we begin with a description of the contempo-

rary landscape of economic inequality across these groups. We highlight
four dimensions of economic status for Black, Blanco, and Latina women:
1) educational attainment; 2) employment status; 3) earnings; y 4) poverty

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Dédalo, la Revista de la Academia Estadounidense de las Artes & SciencesRace, Gender & Economic Inequality in the United States

nivel. These dimensions are not exhaustive; there are other metrics relevant to
economic positioning. Still, taken together, these outcomes highlight separate,
interrelated, and complementary elements of economic standing. Notablemente, ellos
are each to some degree a function of both economic conditions and policy real-
ities. Educational attainment is a first-order foundation of economic positioning
that affects (albeit differentially across groups) one’s economic trajectory across
the life course. The federal government along with states and localities play a large
part in determining access to and quality of education. Employment status is de-
termined by factors including educational attainment, national and local labor
market conditions, y (crucialmente) economic policies such as nondiscrimination
políticas, laws regulating contracts, and much more. Similarmente, one’s work income
is a product of both individual-level and macroeconomic factors, but is also con-
tingent on a wide range of policy interventions such as minimum wage statutes.
Finalmente, the extent to which a person is living below the poverty line is influenced
by all of the other dimensions we consider (education, employment, wages) y
is also significantly conditioned by public policy.

Patterns of inequality between women of different racial groups are widely re-
ported but often in a piecemeal fashion and rarely with an eye toward an intersec-
tional assessment of women’s economic positioning. We bring together baseline
economic data to paint a comprehensive picture. As expected, we find substan-
tial racial disparities across each of the dimensions noted above. Figures 1–4 il-
lustrate these outcomes.

Primero, there are wide disparities in educational attainment. Cifra 1 shows that
en 2017, White women led the way in terms of the share of women (ages twenty-
five and older) with a bachelor’s degree (34 por ciento). Black women were signifi-
cantly less likely to obtain this degree (24 por ciento) and Latina women almost half
as likely as White women to obtain a bachelor’s degree (18 por ciento).

Similar patterns emerge with employment. Cifra 2 displays the share of wom-
en who reported being unemployed in 2016. Even during this postrecession time
of economic upsurge, Black women had the highest rate of unemployment (7.8
por ciento), followed by Latina women (6.3 por ciento). White women had the lowest
unemployment rate (4.2 por ciento).

Turning to earnings, Cifra 3 charts the wide disparity in median earnings be-
tween White, Negro, and Latina women. En 2017, White women’s weekly earn-
ings were $814 per week, comparado con $673 for Black women and $618 for Lati- na women. Finalmente, a look at poverty uncovers comparable patterns. Cifra 4 highlights ra- cial differences in poverty rates. En 2013, White women had the lowest poverty rate (11.7 por ciento), followed by Latina women (24 por ciento) and Black women (25.7 por ciento). It is quite striking that White women are less than half as likely as either Black women or Latina women to be living in poverty. 105 l D o w n o a d e desde h t t p : / / directo . mi t . / e d u d a e d a r t i c e – pd / l f / / / / 1 4 9 1 1 0 0 1 8 3 1 6 5 1 d a e d _ a _ 0 1 7 7 6 pd / . f por invitado 0 7 septiembre 2 0 2 3 149 (1) Winter 2020Jamila Michener & Margaret Teresa Brower Figure 1 Women Bachelor’s Degree Holders or Higher in 2017 by Race/Ethnicity, Ages Twenty-Five and Older t n e c r e P 40 35 30 25 20 15 10 5 0 White Black Latina Source: United States Census Bureau, American Community Survey 2017, available through IPUMS USA (Integrated Public-Use Microdata Series), https://ipums.org. Cifra 2 Women’s Unemployment Rate in 2016 by Race/Ethnicity, Ages Sixteen and Older 9 8 7 6 5 4 3 2 1 0 t n e c r e P White Black Latina Source: United States Census Bureau, Current Population Survey 2016, available through IPUMS (Integrated Public-Use Microdata Series), https://ipums.org. 106 l D o w n o a d e desde h t t p : / / directo . mi t . / e d u d a e d a r t i c e – pd / l f / / / / 1 4 9 1 1 0 0 1 8 3 1 6 5 1 d a e d _ a _ 0 1 7 7 6 pd / . f por invitado 0 7 septiembre 2 0 2 3 Dédalo, la Revista de la Academia Estadounidense de las Artes & SciencesRace, Gender & Economic Inequality in the United States Figure 3 Women’s Median Weekly Earnings in 2017 by Race/Ethnicity, Ages Sixteen and Older $900

$800 $700

$600 $500

$400 $300

$200 $100

Blanco

Negro

Latina

Fuente: United States Census Bureau, Current Population Survey 2017, accessed via the
Institute for Women’s Policy Research, https://iwpr.org.

Cifra 4
Women’s Poverty Rates in 2013 by Race/Ethnicity, Ages Eighteen and Older

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PAG

Blanco

Negro

Latina

Fuente: United States Census Bureau, Current Population Survey 2013, accessed via Status of
Women in the States, https://statusofwomendata.org.

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149 (1) Winter 2020Jamila Michener & Margaret Teresa Brower

The patterns shown above are not surprising, but they are important. Dispar-
ities among groups of women are often muted or overlooked in favor of compar-
isons with men. White men generally outpace all women economically.31 Black
men sometimes fare worse than Black women (especially with respect to educa-
tional outcomes).32 Comparisons to men across and within racial groups are often
highlighted over and above differences between women. By focusing on compar-
isons among women, we show that across most metrics of economic well-being,
Black and Latina women are considerably disadvantaged.

W hat role does public policy play in structuring this state of affairs?

Making strong causal arguments is beyond the scope of this essay. Él
is difficult enough to make a convincing case that a single policy inter-
vention has affected a single economic outcome for a single racial group. We cannot
offer causal evidence that a set of economic and social policies caused aggregate
changes in multiple patterns of inequality across numerous groups of women. En-
lugar, we offer correlational analyses to make a prima facie case that state-level so-
cial and economic policies have varied implications across groups of women. Nosotros
argue that this highlights the need for careful thinking about the heterogeneity of
policy effects. We cannot fully explain why the specific patterns we find exist. En-
lugar, we use these analyses as a springboard for encouraging further exploration
of the policy dimensions of racial differences in economic outcomes.

Our immediate empirical objective is to gauge whether state-level social poli-
cies have varying associations with women’s economic status across racial groups.
Our emphasis is on the racially heterogeneous individual-level upshots of state-level
política. This means that we are not primarily concerned with whether receiv-
ing a particular policy benefit at the individual level is associated with improved
individual-level economic positioning. Bastante, we highlight whether the type or
generosity of benefits at the state level correlates with individual-level econom-
ic status. Put most straightforward, we consider the consequences of state-policy
choices for individual-level outcomes.

Empirically identifying the relationship between economic status and pub-
lic policy is difficult for numerous reasons.33 In particular, economic status is cor-
related with both access to and experiences with public policy, especially at the in-
dividual level. Using state-level policies as our main independent variables helps
to mitigate this. More substantively, taking this approach allows us to consider
the consequences of state-level policy regimes for women across racial groups.
This is in line with our larger emphasis: not on the discrete “effects” of any single
policy for an individual person who receives that policy benefit, but on the over-
arching role of social and economic policy in structuring outcomes for women.

We also recognize that one’s economic position is complex and not dependent
on one factor, such as wages or poverty. De este modo, we make the choice to include an

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Dédalo, la Revista de la Academia Estadounidense de las Artes & SciencesRace, Gender & Economic Inequality in the United States

index variable that accounts for this complexity. We conceptualize economic status
as an (additive) function of three factors that each (dichotomously) reflect an im-
portant aspect of respondents’ position in the economy: 1) whether a respondent
had any education beyond high school; 2) whether a respondent is below or above
the official poverty line; y 3) whether a respondent earns a wage above the me-
dian of sampled respondents. We chose to include dichotomous measures of these
outcomes because these markers (such as having college experience or being be-
low or above the poverty line) are often associated with substantial differences in
economic trajectory.34 The index we created gauges respondents’ combined posi-
tioning in each of these domains. Increasing scores indicate more “positive” eco-
nomic status (the highest-scoring respondents have an education beyond high
escuela, wages above the median, and are not living in poverty).

To construct this economic status index, we used 2009 individual-level micro-
data from the Annual Social and Economic Supplement of the Current Popula-
tion Survey (CPS) available through the Integrated Public Use Microdata Series
(IPUMS).35 The CPS contains responses from over seventy-five thousand Black,
Blanco (no hispano), and Hispanic/Latina women across the United States.36
We selected 2009 as the year for our analysis both for ease and for its theoretical
valor. Our honesty about presenting correlations (as opposed to causal estimates)
follows from this choice. Coming at the tail end of the most recent recession
(2007–2009), 2009 was one of the most difficult years in recent economic mem-
ory, and the supportive and stabilizing effects of public policy were acutely impor-
tant during this time. We thus underscore a time that is especially significant vis-à-
vis how policy operates when women are most vulnerable in the larger economy.

Our key independent variables gauge social and economic policy at the state
nivel. These variables come from multiple sources, but each is housed in the Cor-
relates of State Policy database.37 Our social policy variables include measures of
states’ provision of food assistance (levels of SNAP and WIC participation), cash
(TANF benefit levels), and health care (proportion of population with any public
health insurance). Our economic policy variables include measures of the state
EITC rate; the availability of state disability insurance; an indicator of whether
the state minimum wage is above the federal minimum; an indicator of whether a
state has prevailing wage laws; and a measure of states’ average amount for unem-
ployment compensation. Finalmente, we incorporate a basic set of controls at the indi-
vidual level (from the CPS), including age, marital status, number of children, citi-
zenship status, disability status; and at the state level (from the Correlates of State
Policy data set), including state poverty rate and state general expenditures.38

To examine the correlations between economic status and state policy, we em-
ploy multilevel regression.39 Following the theoretical expectations described
earlier, we model economic status separately for each racial/ethnic subgroup as
well as for women who are employed and unemployed.

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149 (1) Winter 2020Jamila Michener & Margaret Teresa Brower

Cifra 5
State Policy and Women’s Economic Position in 2009 by Race
and Employment

Employed

Unemployed

TANF

SNAP

WIC

Public Health
Insurance

EITC

Minimum Wage

Prevailing Wage

Disability
Insurance

Unemployment
Compensation

-1

-0.5

0.5

1.5

-1

-0.5

0.5

1.5

Blanco

Latina

Negro

Fuente: United States Census Bureau, Current Population Survey Annual Social and Economic
Supplement, available through IPUMS (Integrated Public-Use Microdata Series) and CPS (Canalla-
rent Population Survey), https://ipums.org.

R ecall that the goal of these models is to assess the heterogeneity of cor-

relations between women’s economic status and state-level public policy
across racial groups. Tables 1 y 2 along with Figure 5 illustrate significant
heterogeneity.40 We can neither explain nor account for each of the correlations.
En cambio, we describe some notable patterns. State TANF policy has few significant
correlations with women’s economic status, with one exception: a marginally sig-
nificant economic boost for unemployed Latina women.41 Higher levels of state
SNAP benefits are moderately (positively) correlated with economic positioning
for employed White women. More expansive WIC policy appears to correlate sig-
nificantly (and positively) to economic status for unemployed Black and White
women. State provisions of public health insurance are associated with more

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Dédalo, la Revista de la Academia Estadounidense de las Artes & SciencesRace, Gender & Economic Inequality in the United States

Mesa 1
State Policies and Women’s Economic Position in 2009 by Race (Employed)

Age

Married

Number of Children

Disability

Citizen

State Poverty Rate

State General Expenditures

TANF

SNAP

WIC

Public Health Insurance

EITC

Minimum Wage

Prevailing Wage

Disability Insurance

Unemployment Compensation

Nivel 1 norte (Individual)

Nivel 2 norte (Estado)

Blanco
0.0172***

(0.000918)

0.457***

(0.0266)

0.0762***

(0.0112)

−0.683***

(0.0610)

0.373***

(0.0966)

−0.00265

(0.00835)

0.00000

(0.00000)

0.00530

(0.0295)

0.0385*

(0.0232)

0.0275

(0.111)

0.0274

(0.200)

0.791***

(0.253)

0.0855*

(0.0498)

−0.0674

(0.0502)

0.0155

(0.117)

0.000019

(0.000339)

29,728

Latina
0.0129***

(0.00219)

0.385***

(0.0577)

−0.106***

(0.0229)

−0.564***

(0.162)

1.021***

(0.0605)

−0.0108

(0.0213)

−0.00000

(0.00000)

−0.0224

(0.0780)

0.0276

(0.0645)

−0.323

(0.268)

0.430

(0.465)

0.428

(0.738)

−0.0845

(0.132)

−0.0562

(0.142)

−0.00243

(0.296)

0.000149

(0.000948)

6,243

Negro
0.0187***

(0.00220)

0.498***

(0.0603)

−0.0312

(0.0239)

−0.806***

(0.141)

0.442***

(0.121)

−0.0296**

(0.0139)

−0.00000

(0.00000)

0.00886

(0.0545)

0.00581

(0.0538)

0.121

(0.139)

0.187

(0.265)

0.849*

(0.476)

−0.104

(0.0982)

−0.0185

(0.0860)

−0.233

(0.199)

−0.000192

(0.000546)

5,168

Nota: Standard errors in parenthesis. *** pag<0.01, ** p<0.05, * p<0.1. Source: United States Census Bureau, Current Population Survey Annual Social and Economic Supplement, available through IPUMS (Integrated Public-Use Microdata Series) and CPS (Current Population Survey), https://ipums.org. 111 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u d a e d a r t i c e - p d / l f / / / / 1 4 9 1 1 0 0 1 8 3 1 6 5 1 d a e d _ a _ 0 1 7 7 6 p d / . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 149 (1) Winter 2020Jamila Michener & Margaret Teresa Brower Table 2 State Policies and Women’s Economic Position in 2009 by Race (Unemployed) Age Married Number of Children Disability Citizen State Poverty Rate State General Expenditures TANF SNAP WIC Public Health Insurance EITC Minimum Wage Prevailing Wage Disability Insurance Unemployment Compensation Level 1 N (Individual) Level 2 N (State) White 0.00118** (0.000502) 0.538*** (0.0225) 0.0828*** (0.0100) −0.350*** (0.0269) 0.329*** (0.0731) −0.0217*** (0.00761) −0.00000 (0.00000) 0.0402 (0.0276) 0.0186 (0.0219) 0.201* (0.105) −0.0104 (0.187) 0.0402 (0.243) 0.0936** (0.0471) −0.0881* (0.0473) 0.124 (0.111) −0.00007 (0.000316) 22,406 50 Latina 0.00004 (0.00109) 0.457*** (0.0440) −0.0220 (0.0162) −0.286*** (0.0605) 0.533*** (0.0427) −0.0342*** (0.00896) −0.00000** (0.00000) 0.0633* (0.0366) −0.000906 (0.0349) 0.0428 (0.0985) 0.336** (0.160) 0.366 (0.393) −0.0380 (0.0586) −0.0383 (0.0691) 0.138 (0.129) −0.000884* (0.000458) 22,406 50 Black 0.00240** (0.00115) 0.582*** (0.0561) 0.0468** (0.0213) −0.414*** (0.0584) 0.313*** (0.102) −0.00813 (0.00938) −0.00000 (0.00000) 0.0227 (0.0375) 0.0594 (0.0408) 0.338*** (0.0895) −0.0141 (0.169) 1.076*** (0.339) −0.0146 (0.0687) 0.0846 (0.0638) −0.141 (0.144) −0.000141 (0.000397) 4,942 49 Note: Standard errors in parenthesis. *** p<0.01, ** p<0.05, * p<0.1. Source: United States Census Bureau, Current Population Survey Annual Social and Economic Supplement, available through IPUMS (Integrated Public-Use Microdata Series) and CPS (Current Population Survey), https://ipums.org. 112 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u d a e d a r t i c e - p d / l f / / / / 1 4 9 1 1 0 0 1 8 3 1 6 5 1 d a e d _ a _ 0 1 7 7 6 p d / . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Dædalus, the Journal of the American Academy of Arts & SciencesRace, Gender & Economic Inequality in the United States positive economic status for unemployed Latina women. A higher state EITC rate stands out as having positive associations with improved economic status for em- ployed Black and White women, and even for unemployed Black women. How- ever, the EITC is not correlated with Latina women’s economic positioning. State minimum wage laws that are above the federal minimum wage are associated with economic improvements for both employed and unemployed White wom- en, while prevailing wage laws are (marginally) negatively correlated with eco- nomic positioning for employed White women. State unemployment compensa- tion is (marginally) negatively correlated with unemployed Latina women’s eco- nomic status. W hile we offer no easy takeaways, our central argument is that wom- en’s economic positioning and the policies that shape it are heter- ogenous across racial and ethnic groups. We offer an index variable as a way of measuring the complex positionality of women in the economy. Our goal in doing so is not to determine a perfect measurement of economic standing, but to account for the multidimensionality of women’s economic positionality in the United States. When we study the relationship between this positionality and public policies, we find considerable differences among women. Indeed, we find that public policies have significant (positive and negative) relationships with women’s economic position that differ by race and ethnicity. Although Latina and Black women share many similarities in terms of how they are disadvantaged by the labor market, their economic positions have very dif- ferent relationships with social and economic policies. For Latina women, TANF and public health insurance are positively correlated with their economic posi- tion while for Black women, WIC and EITC are positively correlated. Meanwhile, though both White and Black unemployed women’s economic positions are posi- tively correlated with state WIC policy, no such correlation exists for Latina wom- en. These outcomes are important because they illustrate that differences among women–their employment status, race, ethnicity–underlie variation in the rela- tionships between their economic standing and policies that are facially neutral. We do not attempt to determine the causal mechanisms driving these differ- ences among women. Instead, we point to well-established mechanisms from pre- vious literature to make sense of the observed inequities. Political institutions like federalism and partisanship both structure and incentivize unequal policy benefits, divergent policy experiences, and inequitable policy outcomes for people across states, localities, and demographic categories. These institutional parameters map onto state racial and ethnic composition. In this way, institutions and the forms of policy design and implementation that they enable shape the extent to which policy is either a buffer against inequality or a channel through which it operates. We provide state-level policy analyses to highlight some of these processes, not to 113 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u d a e d a r t i c e - p d / l f / / / / 1 4 9 1 1 0 0 1 8 3 1 6 5 1 d a e d _ a _ 0 1 7 7 6 p d / . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 149 (1) Winter 2020Jamila Michener & Margaret Teresa Brower determine the specific mechanisms driving inequality among women, but to illus- trate that state policy regimes have racialized consequences for women’s econom- ic standing. One of our key contributions here is to underscore the policy implications of an intersectional approach to economic inequality. Women of Color are in a unique- ly precarious economic position in the United States. Making significant progress with regard to poverty reduction and economic mobility hinges in significant part on their economic status and trajectory. More fully understanding that trajectory –and the policy avenues for altering it–requires attentiveness to how policy oper- ates across racial groups. Moreover, the dual policy dimensions we concentrate on here (social policies and economic policies) are often considered separately, either with respect to individual policies or with respect to only one policy dimension. Though the correlations we highlight should not be taken at face value, they do provide prima facie evidence that in the realms of both social policy and econom- ic policy, the choices that we make about how and where to invest have differential consequences for racial disparities among women. We hope to encourage scholars to ask why, to delve more deeply into specific mechanisms, and to more thorough- ly identify the processes that account for heterogeneous policy effects across racial groups. Racial equitability is one important metric by which we can prioritize and assess policy. First, however, we must ask and answer many more questions about the contours of racially heterogeneous policy effects. about the authors Jamila Michener is an assistant professor in the Department of Government at Cornell University. She is the author of Fragmented Democracy: Medicaid, Federalism, and Unequal Politics (2018) and has published in such journals as Political Behavior, Pol- icy Studies Journal, and the Journal of Health, Politics, Policy and Law. Margaret Teresa Brower is a Ph.D. candidate and Urban Fellow at the University of Chicago. She has published in such journals as Journal of College and Character, Change: The Magazine of Higher Learning, and Diversity & Democracy. endnotes 1 Joseph G. Altonji and Rebecca M. Blank, “Race and Gender in the Labor Market,” in Hand- book of Labor Economics, vol. 3, ed. Orley Ashenfelter and David Card (Amsterdam: El- sevier, 1995): 3143–3259; Teresa L. Amott and Julie A. Matthaei, Race, Gender, and Work: A Multi-Cultural Economic History of Women in the United States (Boston: South End Press, 1996); Irene Browne, ed., Latinas and African American Women at Work: Race, Gender, and 114 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u d a e d a r t i c e - p d / l f / / / / 1 4 9 1 1 0 0 1 8 3 1 6 5 1 d a e d _ a _ 0 1 7 7 6 p d / . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Dædalus, the Journal of the American Academy of Arts & SciencesRace, Gender & Economic Inequality in the United States Economic Inequality (New York: Russell Sage Foundation, 2000); Leslie McCall, Com- plex Inequality: Gender, Class and Race in the New Economy (New York: Routledge, 2001); and Donald Tomaskovic-Devey, Gender & Racial Inequality at Work: The Sources and Consequenc- es of Job Segregation (Ithaca: Cornell University Press, 1993). 2 Dedrick Asante-Muhammad, “Racial Wealth Divide Snapshot: Women and the Racial Wealth Divide,” Prosperity Now, March 29, 2018, https://prosperitynow.org/blog/ racial-wealth-divide-snapshot-women-and-racial-wealth-divide. 3 Ibid; Will McGrew, “How Workplace Segregation Fosters Wage Discrimination for Afri- can American Women” (Washington, D.C.: Washington Center for Equitable Growth, 2018), https://equitablegrowth.org/wp-content/uploads/2019/06/082818-african-american -women-paygap.pdf. 4 Though the umbrella category “Women of Color”–which we capitalize as a label refer- ring to multiple racial groups–includes Asian American and Native American women, we focus here on comparisons between White women, Black women, and Latina wom- en. This is in part because Asian American and Native American women have distinc- tive economic outcomes and relationships to public policy, so the factors we point to here operate differently enough for them that it is not appropriate simply to fold them into the analysis. In addition, data on the economic status of Asian American and Na- tive American women are sparser and less comprehensive. Ultimately, our hope is that by shining a spotlight on the policy dimensions of race, gender, and economic inequal- ity, we create scholarly and intellectual space for others to examine and highlight the dynamics among groups of women that we do not consider here. Altonji and Blank, “Race and Gender in the Labor Market”; Enobong Branch, Opportunity Denied: Limit- ing Black Women to Devalued Work (New Brunswick, N.J.: Rutgers University Press, 2011); and Margery Austin Turner, Michael Fix, and Raymond J. Struyk, Opportunities Denied, Opportunities Diminished: Racial Discrimination in Hiring (Washington, D.C.: The Urban In- stitute, 1991). 5 Hajer Al-Faham, Angelique Davis, and Rose Ernst, “Intersectionality: From Theory to Practice,” Annual Review of Law and Social Science 15 (2019): 247–265; Cathy J. Cohen, The Boundaries of Blackness: AIDS and the Breakdown of Black Politics (Chicago: University of Chi- cago Press, 1999); Patricia Hill Collins, “Intersections of Race, Class, Gender, and Na- tion: Some Implications for Black Family Studies,” Journal of Comparative Family Studies 29 (1) (1998): 27–36; Kimberlé Williams Crenshaw, “Mapping the Margins: Intersection- ality, Identity Politics, and Violence against Women of Color,” Stanford Law Review 43 (6) (1991): 1241–1299; Ange-Marie Hancock, “Intersectionality as a Normative and Empir- ical Paradigm,” Politics & Gender 3 (2) (2007): 248–254; and Jamila Michener, Andrew Dilts, and Cathy Cohen, “African-American Women: Intersectionality in Politics,” in Oxford Handbook of African American Citizenship, 1865–Present, ed. Henry Louis Gates Jr., Claude Steele, Lawrence D. Bobo, et al. (New York: Oxford University Press, 2012). 6 We follow sociologist Bob Jessop in defining the state in terms of four key elements: “(1) a politically organized coercive, administrative and symbolic apparatus endowed with general and specific powers; (2) a clearly demarcated core territory under more or less uncontested continuous control . . . (3) a stable population under which the state’s political authority and decisions are binding”; and (4) it is an “idea” that “denotes the political imaginary.” Bob Jessop, “State Theory,” in Handbook on Theories of Governance, ed. Christopher Ansell and Jacob Torfing (Northampton, Mass.: Edward Elgar Publish- ing, Inc., 2016), 72–73. 115 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u d a e d a r t i c e - p d / l f / / / / 1 4 9 1 1 0 0 1 8 3 1 6 5 1 d a e d _ a _ 0 1 7 7 6 p d . / f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 149 (1) Winter 2020Jamila Michener & Margaret Teresa Brower 7 Jamila Michener, “Social Class and Racialized Political Experience,” The Forum 15 (1) (2017): 93–110. 8 Price Fishback, “How Successful Was the New Deal? The Microeconomic Impact of New Deal Spending and Lending Policies in the 1930s,” Journal of Economic Literature 55 (4) (2017): 1435–1485; Ira Katznelson, When Affirmative Action Was White: An Untold Histo- ry of Racial Inequality in Twentieth-Century America (New York: W. W. Norton & Company, 2005); Robert Lieberman, Shifting the Color Line: Race and the American Welfare State (Cam- bridge, Mass.: Harvard University Press, 2001); and Suzanne Mettler, Dividing Citizens: Gender and Federalism in New Deal Public Policy (Ithaca, N.Y.: Cornell University Press, 1998). 9 Linda Gordon, Pitied but Not Entitled: Single Mothers and the History of Welfare, 1890–1935 (New York: Free Press, 1994); and Mettler, Dividing Citizens. 10 Mettler, Dividing Citizens. 11 Harmony Goldberg, “‘Prepare to Win’: Domestic Workers United’s Strategic Transition Following Passage of the New York Domestic Workers’ Bill of Rights,” in New Labor in New York: Precarious Workers and the Future of the Labor Movement, ed. Ruth Milkman and Ed Ott (Ithaca, N.Y.: Cornell University Press, 2014), 268–288. 12 Robert C. Lieberman, “Race and the Organization of Welfare Policy,” in Classifying by Race, ed. Paul E. Peterson (Princeton, N.J.: Princeton University Press, 1995), 156–187; and Mettler, Dividing Citizens. 13 Alexa A. Hendley and Natasha F. Bilimoria, “Minorities and Social Security: An Analysis of Ethnic Differences in the Current Program,” Social Security Bulletin 62 (2) (1999): 59; and Eugene C. Steuerle, Karen E. Smith, and Caleb Quakenbush, “Has Social Security Redis- tributed to Whites from People of Color?” (Washington, D.C.: The Urban Institute, 2013), https://www.urban.org/sites/default/files/alfresco/publication-pdfs/412943-Has -Social-Security-Redistributed-to-Whites-from-People-of-Color-.pdf. 14 Ibid. 15 Jamila Michener, “Policy Feedback in a Racialized Polity,” Policy Studies Journal 47 (2) (2019): 423–450. 16 Robert C. Lieberman and John S. Lapinski, “American Federalism, Race and the Admin- istration of Welfare,” British Journal of Political Science 31 (2) (2001): 303–329. 17 Zachary Parolin, “Temporary Assistance for Needy Families and the Black-White Child Poverty Gap in the United States,” Socio-Economic Review (2019), https://doi.org/10.1093 /ser/mwz025; and Joe Soss, Richard C. Fording, and Sanford F. Schram, Disciplining the Poor: Neoliberal Paternalism and the Persistent Power of Race (Chicago: University of Chica- go Press, 2011). 18 Jamila Michener, Fragmented Democracy: Medicaid, Federalism, and Unequal Politics (New York: Cambridge University Press, 2018). 19 Suzanne Mettler, Soldiers to Citizens: The GI Bill and the Making of the Greatest Generation (New York: Oxford University Press, 1998); Michener, Fragmented Democracy; and Joe Soss, Unwanted Claims: The Politics of Participation in the U.S. Welfare System (Ann Arbor: Univer- sity of Michigan Press, 2000). 20 Francine D. Blau and Andrea H. Beller, “Black-White Earnings over the 1970s and 1980s: Gender Differences in Trends,” The Review of Economics and Statistics 74 (2) (1992): 276–286; Irene Browne, “Opportunities Lost? Race, Industrial Restructuring, and Employment 116 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u d a e d a r t i c e - p d / l f / / / / 1 4 9 1 1 0 0 1 8 3 1 6 5 1 d a e d _ a _ 0 1 7 7 6 p d / . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Dædalus, the Journal of the American Academy of Arts & SciencesRace, Gender & Economic Inequality in the United States among Young Women Heading Households,” Social Forces 78 (3) (2000): 907–929; Har- ry J. Holzer, What Employers Want: Job Prospects for Less-Educated Workers (New York: Russell Sage Foundation, 1996); Chinhui Juhn, Kevin M. Murphy, and Brooks Pierce, “Wage In- equality and the Rise in Returns to Skill,” Journal of Political Economy 101 (3) (1993): 410– 442; and Lawrence F. Katz and Kevin M. Murphy, “Changes in Relative Wages, 1963–87: Supply and Demand Factors,” Quarterly Journal of Economics 107 (1) (1992): 35–78. 21 Irene Browne, Latinas and African American Women at Work: Race, Gender, and Economic In- equality (New York: Russell Sage Foundation, 2000). 22 Francine D. Blau and Lawrence M. Kahn, “Swimming Upstream: Trends in the Gen- der Wage Differential in the 1980s,” Journal of Labor Economics 15 (1) (1997): 1–42; Mary Corcoran, “The Economic Progress of African American Women,” in Latinas and African American Women at Work, ed. Browne, 35–60; and Diana Furchtgott-Roth and Christine Stolba, Women’s Figures: The Economic Progress of Women in America (Washington, D.C.: AEI Press, 1996). 23 Andrew Sum and Ishwar Khatiwada, “The Nation’s Underemployed in the ‘Great Reces- sion’ of 2007–09,” Monthly Labor Review 133 (11) (2010): 3–15. 24 Chandra Childers and Gladys McLean, “Black and Hispanic Women Lag in Recovering from the Recession” (Washington, D.C.: Institute for Women’s Policy Research, 2017). 25 Jasmine Tucker and Caitlin Lowell, “National Snapshot: Poverty among Women & Fami- lies 2015” (Washington, D.C.: National Women’s Law Center, 2016), https://nwlc.org/ wp-content/uploads/2016/09/Poverty-Snapshot-Factsheet-2016.pdf; and Jeff Hayes and Heidi Hartmann, “Women and Men Living on the Edge: Economic Insecurity after the Great Recession” (Washington, D.C.: Institute for Women’s Policy Research, 2011). 26 Parolin, “Temporary Assistance for Needy Families and the Black-White Child Poverty Gap in the United States.” 27 Andrew Cherlin, Bianca Frogner, David Ribar, and Robert Moffitt, “Welfare Reform in the Mid-2000s: How African American and Hispanic Families in Three Cities are Far- ing,” The Annals of the American Academy of Political and Social Science 621 (1) (2009): 178– 201; and Dorothy E. Roberts, “Welfare and the Problem of Black Citizenship,” Yale Law Journal 105 (6) (1996): 1563–1601. 28 Eileen Boris and Michael Honey, “Gender, Race, and the Policies of the Labor Depart- ment-Promoting Equal Job Opportunity for Women and Minority Men, of Little Con- cern in the Department’s Early Years, Made Headway in the 1960’s and 1970’s,” Monthly Labor Review 111 (26) (1988). 29 James P. Smith and Finis Welch, “Affirmative Action and Labor Markets,” Journal of La- bor Economics 2 (2) (1984): 269–301. 30 Altonji and Blank, “Race and Gender in the Labor Market”; Hilary Hoynes, Douglas L. Miller, and Jessamyn Schaller, “Who Suffers During Recessions?” Journal of Economic Perspectives 26 (3) (2012): 27–48; Franklin James and Steve W. DelCastillo, “Measur- ing Job Discrimination by Private Employers against Young Black and Hispanic Males Seeking Entry Level Work in the Denver Metropolitan Area,” unpublished manuscript (1991); David Neumark, Roy J. Bank, and Kyle D. Van Nort, “Sex Discrimination in Restaurant Hiring: An Audit Study,” The Quarterly Journal of Economics 111 (3) (1996): 915–941; and Turner et al., Opportunities Denied, Opportunities Diminished. 117 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u d a e d a r t i c e - p d / l f / / / / 1 4 9 1 1 0 0 1 8 3 1 6 5 1 d a e d _ a _ 0 1 7 7 6 p d . / f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 149 (1) Winter 2020Jamila Michener & Margaret Teresa Brower 31 Ariane Hegewisch and Emma Williams-Baron, “The Gender Wage Gap: 2017 Earnings Differences by Race and Ethnicity” (Washington, D.C.: Institute for Women’s Policy Re- search, 2017), https://iwpr.org/publications/gender-wage-gap-2017-race-ethnicity/. 32 Robert Bruce Slater, “The Growing Gender Gap in Black Higher Education,” The Journal of Blacks in Higher Education (3) (1994): 52–59. 33 Attempting to measure how individual-level policy benefits affect individual-level eco- nomic outcomes using cross sectional data is not optimal. Such a setup is flawed (in part) because it necessitates that our independent and dependent variables are difficult to disentangle. Economic status is part of what determines whether one can get access to social policies, so measuring how said policies affect economic status at the individ- ual level would conflate the right- and left-hand sides of our regression models. Our al- ternative, using state-level policies as the main independent variables, does not exempt us from such concerns, but it helps. The distinction between our outcomes and pre- dictors is more marked when we measure policies at the state level, since this shifts us from assessing how receiving SNAP, Medicaid, or other policies affect economic status for an individual to examining how varying state investments in those policies shape outcomes differentially for individual women. 34 Michael R. Carter and Christopher B. Barrett, “The Economics of Poverty Traps and Persistent Poverty: An Asset-Based Approach,” The Journal of Development Studies 42 (2) (2006): 178–199; and Emily Pressler, Cybele Raver, and Michael D. Masucci, “Increas- ing Low-Income Mothers’ Educational Attainment: Implications for Anti-Poverty Pro- grams and Policy,” Journal of Applied Research on Children 7 (1) (2016): 1–26. 35 Sarah Flood, Miriam King, Steven Ruggles, and J. Robert Warren, Integrated Public Use Microdata Series, Current Population Survey, Version 4.0 [machine-readable data- base] (Minneapolis: University of Minnesota, 2015). 36 An individual was considered “non-Hispanic white” if they did not report Hispanic eth- nicity and indicated being White only, not in combination with any other race group. Anyone who self-identified as Hispanic but did not identify as White was considered Hispanic. Individuals categorized as Black were those who identified as Black only. Mixed-raced individuals, though important, were not included in the analyses. 37 Marty P. Jordan and Matt Grossmann, The Correlates of State Policy Project v.2.1 (East Lan- sing, Mich.: Institute for Public Policy and Social Research, 2017). 38 We selected individual-level (level 1) controls that were important aspects of determining women’s economic position and state-level (level 2) controls that were related to states’ level of need (poverty) and to their general economic capacity (general expenditures). 39 Multilevel modeling is appropriate given the nested structure of the state (individuals with- in states). For more on this, see Stephen Raudenbush and Anthony S. Byrk, Hierarchical Linear Models: Applications and Data Analysis Methods (New York: Sage Publications, 2002). 40 These estimates represent statistical correlations between a range of variables (listed on the left side of the table) and women’s economic position (across the indicated racial groups). Each correlation is an estimate of a single variable’s relationship to women’s economic position (while holding the other variables constant). 41 Marginal correlations indicate a significance level of p<0.1. 118 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u d a e d a r t i c e - p d / l f / / / / 1 4 9 1 1 0 0 1 8 3 1 6 5 1 d a e d _ a _ 0 1 7 7 6 p d . / f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Dædalus, the Journal of the American Academy of Arts & SciencesRace, Gender & Economic Inequality in the United States
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