The External Effects of Black Male Incarceration on Black Females

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The External Effects of Black Male Incarceration
on Black Females

• Stéphane Mechoulan
• University of Toronto
• January 2008

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Listening to Black Womens Plight

• Its hard because men have it easy. They have
  two to three women per man, so its very easy for
  him to not stay committed. A woman like me is
  looking for commitment and will try almost
  anything just to keep that commitment going ()
  Im gonna accept this BS hes giving me because
  () without him () its gonna be hard for me to
  find someone else to be with () seeing it as,
  if I let him go, this other womans gonna have
  him. () I dont want to be alone. African
  American woman, Syracuse, New York, 2003
• Cited in Lane et al. (2004) Marriage Promotion
  and Missing Men, Medical Anthropology Quarterly
  18(4) 405-28.

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Motivation

• Unfavorable sex ratio for Black women in the U.S.
  
• Example 1 in 8 (!) Black males age 25-29 were
  incarcerated in 2004
• In contrast 1 in 28 Hispanic and 1 in 59 White
  (BJS data)?
• 1 in 3 Black males can expect to go to prison in
  their lifetime (The Sentencing Project).
• Prevalence of imprisonment gt 15 x higher for
  Black males than for Black females (BJS data)?

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Objective finding causal links between black
male incarceration and black females outcomes

• Black non-marital teen fertility higher in
  absolute value
• But declining faster than for Whites
• Young Black single female Labor force
  participation lower
• But rising while that of young White women stable
  or declining
• Black women bridge racial gap in education faster
  than Black men

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Direction of the effect uncertain

• Example fertility
• Wilsons hypothesis
• Increase in incarceration contributes to the
  scarcity
• More bargaining on male side
• On the female side, expected increase in birth
  control measures
• Sheer magnitude of male shortage could mean fewer
  sexual relations altogether

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Is the Incarceration of Black men responsible?

• Explosion of incarceration in the U.S.
• Black males make 43 of inmates in 2000
• Sources War on Drugs, sentencing changes etc.
• Large variations in the last 30 years within
  states
• Allows for disentangling incarceration paths from
  state effects, year effects and secular trends in
  socio-economic changes within states

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Overview of Results

• Causal effect on non marital teen fertility
• Causal effect on black female education
• Causal effect on black female employment
• OLS effect on marriage not robust to IV

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Literature Review

• Sex Ratio imbalance studies / Wilsons (1987)
  hypothesis
• Economics and the U.S. Judicial system
• Little attention on the external effects of male
  incarceration on women
• Exceptions Charles and Luoh (2006), Kamdar
  (2007)?

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Data

• BJS data on prisoners by state, gender and race
  since 1978
• 95 of prisoners between the age of 20 and 54
• Population counts from the Census by state,
  gender, race and 5 year age brackets since 1970
• Independent variable of interest incarceration
  rate per 20-54 y/o male population for each race
• Bias from increasingly older prison population
  even if average served time up by months, not
  years
• Ageing prison population more pronounced among
  Whites than among Blacks, in particular quasi
  constant of young Black inmates per total Black
  inmate population in the 1990s

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Data merge

• Data on fertility from June CPS (some gaps)
• Data on schooling, marital status and employment
  from March CPS (fewer gaps)
• Merging of BJS / Census and CPS data on a state /
  race / year level with one year lag between adult
  male incarceration rate and observation of
  outcome of interest

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The Matching Problem

• Which incarceration rates to match with whom?
• Equivalently at what age are incarceration rates
  (most) relevant?
• Several ad hoc possible methods
• By looking at young women, we limit mismatch
  error when assigning incarceration rate of the
  preceding year to each observation

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Are the constructed prison rates relevant
statistics?

• Marriage Markets few interracial marriages
• Jail statistics misleading / unreliable but
  small compared to prisons
• Prisons 90 of prisoners are state prisoners
• Proportion of prisoners incarcerated in a state
  different from the one they lived in/offended
• negligible
• does not affect the assignment of prisoners by
  state

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Methods

• Benchmark OLS with standard errors clustered by
  state
• Different specifications, using in turn
• No controls
• year effects
• year effects state effects
• year effects state effects state trend
  effects
• year effects state effects state trend
  effects
• state trend2 effects
• year effects state effects state trend
  effects
• state trend2 effects other potentially
  relevant variables (local male unemployment
  rates, local measures of welfare generosity from
  Fang and Keane (2004))
• Assumptions
• Decisions made by young women do not cause the
  behaviors that result in men being incarcerated
• Male shortage first order effect vs. deterrence
  (source of bias)

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Further examination

• Difference estimation, using Whites as
  quasi-control group
• Instruments considered
• sentencing changes
• major jumps in prison capacity
• If you build it, they will come (back)

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Use of filters to select best instrumental
variables

• To be considered valid, each instrument to be
  significant in the first stage
• In the BJS / Census based source file on
  incarceration (balanced sample of 22 years (50
  states D.C.))
• In the CPS data (unbalanced sample)
• Pass Sargan tests of overidentifying restrictions

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Instruments chosen

• Sentencing Changes
• Presumptive Sentencing works best
• Major Prison Capacity Expansions
• Missing Data
• Identifying the pull effect with parole data
• Instruments have different effects for Black and
  White male incarceration
• F tests gt 25 in all specifications strongly
  reject non-significance of instruments
• Driven by the capacity IV. Sentencing change IV
  is weak
• Capacity expansion IV affects more marginal
  offenders

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Table 3 First Stage Linear Regressions with
robust standard errors clustered by
stateDependent Variable State/Year Black Male
Prisoners per 20-54 Black Men ()

• (1) (2)
• Presumptive Sentencing -0.542 -0.683
• (Dummy variable) (0.288) (0.682)
• Major Capacity Expansion 1.925 1.849
• (Dummy variable) (0.125) (0.093)
• Adjusted R2 0.977 0.985
• F-test (both IV 0) 158 199
• F-test (all extra variables 0) 1,779
• Observations 5,369 5,133
• Sample used in Table 3
• Models (1)-(2) contain year, state, statetrend
  and statetrend2 effects

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What is so compelling about Texas?

• Overcrowding litigation leading to prison
  capacity expansion spans over a decade
• overcrowding litigation filing (1978-79),
  preliminary court decision (1980-84), final
  decision (1985-91) and further action (subsequent
  court intervention, including the appointment of
  special monitors, contempt orders) (1992-93)
• Prison capacity expansion movement starts before
  1995
• Movement starts during democratic governor Ann
  Richards tenure, hence independently of George
  Bushs platform
• George Bush sex ed policies were abstinence-only
  based
• Literature says such policies are ineffective at
  best. Actually for non-Blacks it was a clear
  failure.

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Summary Statistics of Dependent Variables

• Observations Average Std Dev Min Max
• Whether a Mother, (Black) 5,369 0.2958 - 0 1
• Whether a Mother, (White) 28,987 0.0634 - 0
  1
• Educational attainment
• (Black) 2,762 12.422 1.5467 0 18
• Educational attainment
• (White) 14,969 12.9017 1.598 0 18
• Full Time Employed 4,799 0.2446 - 0 1
• (Black)
• Full Time Employed 26,110 0.3513 - 0 1
• (White)

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Table 5Linear Regressions with robust standard
errors clustered by stateSample June CPS
unmarried Black women age 18-20 (1979-85, 1990,
1992, 1994-1995, 1998, 2000)Dependent Variable
whether a mother

• (1) (2) (3) (4) (5) (6) (7) (8) (IV) (9)
  (IV) (10) (IV2) (11)(IV2)
• Black Prison rate
• 20-54 y/o 0.001 0.001 -0.002 -0.039 -0.043 -0.056
  -0.1 -0.1 -0.129 -0.148
• (0.004) (0.009) (0.011) (0.016) (0.021) (0.02
  4) (0.044) (0.05) (0.019) (0.022)
• Prison rate 0.052
• (0.052)
• Prison rateBlack -0.095
• (0.058)
• Year Yes Yes Yes Yes Yes Yes Yes Yes
  Yes Yes
• State Yes Yes Yes Yes Yes Yes Yes Yes Yes
• StateTrend Yes Yes Yes Yes Yes Yes Yes Yes
  
• StateTrend2 Yes Yes Yes Yes Yes Yes Yes
• Extra Controls Yes Yes Yes
• Adjusted R2 0.017 0.019 0.032 0.036 0.036 0.034 0.
  108 0.034 0.033 0.032 0.031
• Observations5,369 5,369 5,369 5,369 5,369 5,133
  34,356 5,369 5,133 5,369 5,133
• All models control for age, age2

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Comments

• Positive non-significant effect of male
  incarceration for White females
• Consistently significant negative effect for
  Black females
• Large coefficients reminiscent of Rosenzweig
  (1999)
• Supports the idea of a nonlinear effect
• Small deviations in the sex ratio -gt could
  explain findings in the White sample
• For large enough deviations, sheer shortage of
  men would decreases early fertility
• Cannot tell through which channel given CPS data

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Other significant outcomes

• Education significant positive effect of male
  incarceration on Black females age 20
• All specifications for initial CPS coding up to
  1991, only for IV specifications for CPS coding
  post 1991 (smaller sample)
• Employment Significant positive effect of male
  incarceration on Black females age 20-22 across
  specifications

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Table 6Linear Regressions with robust standard
errors clustered by stateSample March CPS
unmarried Black women age 20 (1979-1991)Dependen
t Variable Last attended grade?

• (1) (2) (3) (4) (5) (6) (7) (8) (IV) (9) (IV)
• Black Prison rate
• 20-54 y/o -0.028 -0.018 0.119 0.368 0.614 0.6 0.
  678 2.22
• (0.048) (0.06) (0.064) (0.163) (0.242) (0.271)
  (0.147) (0.915)
• Prison rate -0.833
• (0.754)
• Prison rateBlack 1.447
• (0.772)
• Year Yes Yes Yes Yes Yes Yes Yes Yes
• State Yes Yes Yes Yes Yes Yes Yes
• StateTrend Yes Yes Yes Yes Yes Yes
• StateTrend2 Yes Yes Yes Yes Yes
• Extra Controls Yes Yes
• Adjusted R2 0 0.001 0.013 0.02 0.018 0.022 0.319
  0.018 0.001
• Observations1,793 1,793 1,793 1,793 1,793 1,711
  11,643 1,793 1,711

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Table 7Linear Regressions with robust standard
errors clustered by stateSample March CPS Black
women age 20-22 (1979-93 and 1996-2000)Dependent
Variable employed full-time

• (1) (2) (3) (4) (5) (6) (7) (8) (IV) (9)
  (IV) (10) (IV2) (11) (IV2)
• Black Prison rate
• 20-54 y/o
• 0.01 0.008 -0.007 0.035 0.046 0.06 0.082 0.16 0.
  081 0.166
• (0.04) (0.008) (0.008) (0.01) (0.019) (0.0
  22) (0.012) (0.043) (0.013) (0.047)
  
• Prison rate -0.113
• (0.06)
• Prison rateBlack 0.159
• (0.06)
• Year Yes Yes Yes Yes Yes Yes Yes Yes
  Yes Yes
• State Yes Yes Yes Yes Yes Yes Yes Yes Yes
• StateTrend Yes Yes Yes Yes Yes Yes Yes Yes

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Impact on Marriage

• Significant OLS results for Blacks in some
  specifications
• Insignificant IV results
• Concurs with Wood (1990) missing Black men may
  not be marriage material in the first place
• At odds with Charles and Luoh (2006)
• Using 1980, 1990 and 2000 (i.e., mimicking
  their sample) I could reproduce their results

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Conclusions

• Welfare Analysis
• Complex picture but negative effect should be
  first-order
• More outcomes can be studied, e.g., welfare
  participation
• Richer data sets could tell us better which women
  are most affected and through which channels
  incarceration operates