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The Impact of Black Male Incarceration on Black Females’ Fertility, Schooling, and Employment in the U.S., 1979-2000

The Impact of Black Male Incarceration on Black Females’ Fertility, Schooling, and Employment in the U.S., 1979-2000. Stéphane Mechoulan University of Toronto Work in Progress – preliminary and incomplete Please do not quote April 2006. Motivation.

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The Impact of Black Male Incarceration on Black Females’ Fertility, Schooling, and Employment in the U.S., 1979-2000

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  1. The Impact of Black Male Incarceration on Black Females’ Fertility, Schooling, and Employment in the U.S., 1979-2000 Stéphane Mechoulan University of Toronto Work in Progress – preliminary and incomplete Please do not quote April 2006

  2. Motivation • Unfavorable sex ratio for Black women in the U.S., driven by massive incarceration of Black men • Example: 1 in 8 (!) Black males age 25-29 was incarcerated in 2004 In contrast: 1 in 28 Hispanic males and 1 in 59 White males in the same age group (BJS data) • One of every three black males can expect to go to prison in their lifetime (The Sentencing Project). • Also, prevalence of imprisonment more than fifteen times higher for Black males than for Black females in 2001 (BJS data)

  3. Objective: finding causal links between black male incarceration and black females’ outcomes • Non marital teen fertility higher in absolute value among Black women, but declining faster • Labor force participation lower among young single Black females but rising, when that of young single white women stable or declining • Black women bridge racial gap in education faster than Black men • Even though they have not caught up with white females regarding 4 year degree+ graduation, they have nearly caught up in high school completion and college enrollment

  4. Is the Incarceration of Black men responsible? • Explosion of incarceration in the U.S.: +360% in prisoners per inhabitant between 1978 and 2004 • Black males make 43+% of inmates in 2000 • Sources: War on Drugs, sentencing changes, prison construction 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.

  5. Overview of Results • Strong causal effect on non marital teen fertility • Strong causal effect on black female education • Strong causal effect on black female employment • OLS effect on marriage not robust to IV

  6. Literature Review • Sex Ratio imbalance studies • Wilson’s (1987) hypothesis and followers • Economics and the U.S. Judicial system • Little attention on the external effects of male incarceration on women • Main exception: Charles and Luoh (2006) • Major Differences with present study: • Constrained by use of decennial censuses • Use aggregated data by race / state / year / age brackets • Present aggregate results for all races, no Diff in Diff • Use different set of instruments • Ignores fertility

  7. Data • BJS data on prisoners by state, gender and race since 1978 • Not broken by age, but 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 • Dependent variable: incarceration rate per 20-54 y/o male population for each race

  8. Data merge • Data on fertility from June CPS (some gaps) • Data on schooling, marital status and employment from March CPS (no gap) • Merging of BJS / Census and CPS data on a state / race / year level

  9. 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

  10. Is it a relevant statistic? • Marriage Markets: 95+% of Black women who marry choose a Black man (U.S. Census) • Jail statistics misleading / unreliable but negligible compared to prisons • Prisons: 89% of prisoners are state prisoners (in 2000) • Proportion of state prisoners incarcerated in a state different from the one they lived in at the time of committing their offense is negligible and does not affect the assignment of prisoners by state in the data

  11. Methods • Benchmark: OLS • 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 (preferred specification) • Assumptions: • Decisions made by young women do not cause the behaviors that result in men being incarcerated • Prisoners lived in the state where they committed their offense

  12. Robustness checks • Weights accounting for adult Black male population • Difference in Difference, using Whites as control group • Calls for adding white* all controls • Clustering standard errors by state / year and state / year / race • Triple differencing? • Instruments: sentencing changes (>10 considered) and major jumps in prison capacity ( “If you build it, they will come (back)” effect)

  13. Use of filters to select best instrumental variables • To be considered valid, each instrument must be significant in the first stage: • In the BJS / Census based source file on incarceration • Unweighted / weighted • In the CPS data • Unweighted / weighted • Pass Sargan tests of overidentifying restrictions

  14. Instruments chosen • Sentencing Changes over the period • Truth in Sentencing (29 changes) • Presumptive Sentencing (5 changes) • Synthetic indicator of parole restriction (29 changes) • Major Prison Capacity Expansions over the period (use of dummy variables because too many years missing regarding capacity) • California (1987) • Maine (1996) • New Hampshire (1989) • North Dakota (1998) • South Dakota (1996) • Texas (1994) • Wisconsin (1998) • F tests > 40 in all specifications: strongly reject non-significance of instruments

  15. 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)

  16. Linear Regressions with robust standard errorsSample: June CPS unmarried Black women 18-20 (1979-85, 1990, 1992, 1994-1995, 1998, 2000)Dependent Variable: “whether a mother” (1) (2) (3) (4) (5) Black Prison rate 20-54 y/o 0.0014 0.001 -0.0021 -0.0393 -0.0432 (0.003) (0.0049) (0 .0076) (0.0149)*** (0.0192)** Year Yes Yes Yes Yes State Yes Yes Yes State*Trend Yes Yes State*Trend^2 Yes Adjusted R^2 0.0173 0.019 0.0317 0.0358 0.0357 # Observations 5,369 5,369 5,369 5,369 5,369 All models control for age, age^2 Robustness Checks (6) (7) (8) (IV) Prison rate -0.0585 0.0516 -0.1306 (0.0242)** (0.0467) (0.0612)** Prison rate*Black -0.0948 (0.0504)* Black and Whites No Yes No Weights Yes No No Adjusted R^2 0.0238 0.1075 0.032 # Observations 5,369 34,356 5,369 Models (6)-(8) control for age, age2 and contain year effects, state effects, state*trend effects and state*trend2 effects. In models (7) the prison rate is race-specific and all the controls interacted with the white dummy are added.

  17. Comments • Positive effect of male incarceration for White females (more significant in IV specifications) • Consistently negative effect for Black females • Supports the idea of a nonlinear effect: • For small deviations in the sex ratio, more male bargaining power, more extra marital relations and pregnancies – presumably what happens in the white sample • For large enough deviations, the sheer shortage of men decreases fertility – likely what happens in the black sample • Cannot tell through which channel (abortion, pill, condom use, expectation of absentee father, etc.) given CPS data

  18. Linear Regressions with robust standard errorsSample: March CPS unmarried Black women age 20 (1979-2000)Dependent Variable: “Last attended grade at age 20”* (1) (2) (3) (4) (5) Black Prison rate 20-54 y/o -0.0691 -0.0225 0.023 0.1023 0.2163 (0.0134)*** (0.02) (0.0367) (0.0606)* (0.0943)** Year Yes Yes Yes Yes State Yes Yes Yes State*Trend Yes Yes State*Trend^2 Yes Adjusted R^2 0.0079 0.0121 0.0189 0.0207 0.0228 # Observations 2,762 2,762 2,762 2,762 2,762 Robustness Checks (6) (7) (8) (IV) Prison rate 0.2827 0.4939 0.3420 (0.1018)*** (0.3335) (0.2058)* Prison rate*Black -0.2775 (0.347) Black and Whites No Yes No Weights Yes No No Adjusted R^2 0.0083 0.0631 0.0214 # Observations 2,762 17,731 2,762 Models (6)-(8) control for age, age2 and contain year effects, state effects, state*trend effects and state*trend2 effects. In models (7) the prison rate is race-specific and all the controls interacted with the white dummy are added. *Recode from 1992 on based on Jaeger (1997)

  19. Comments • Insignificant positive effect of male incarceration on White females’ education (significant for age 21, 20-21 or 20-22 but never in IV specifications), significant positive effect on Black females • No significant differential effect

  20. Linear Regressions with robust standard errorsSample: March CPS unmarried Black women age 20-21(1979-1993 and 1996-2000)Dependent Variable: “Employed full time” ` (1) (2) (3) (4) (5) Black Prison rate 20-54 y/o 0.0089 0.0084 -0.0002 0.0321 0.0434 (0.003)*** (0.0048)* (0.0083) (0.015)** (0.02)** Year Yes Yes Yes Yes State Yes Yes Yes State*Trend Yes Yes State*Trend^2 Yes Adjusted R^2 0.008 0.0139 0.0256 0.0323 0.0326 # Observations 4,799 4,799 4,799 4,799 4,799 All models control for age Robustness Checks (6) (7) (8) (IV) Prison rate 0.0735 -0.2135 0.1522 (0.0259)*** (0.0886)** (0.0565)*** Prison rate*Black 0.2569 (0.0908)*** Black and Whites No Yes No Weights Yes No No Adjusted R^2 0.0227 0.0257 0.0263 # Observations 4,799 30,909 4,799 Models (6)-(8) control for age, age^2 and contain year effects, state effects, state*trend effects and state*trend^2 effects. In models (7) the prison rate is race-specific and all the controls interacted with the white dummy are added.

  21. Comments • In response to male incarceration, young single black women increase their employment; single white women decrease theirs • Significant differential effect • Consistent with graphical pattern • From regressions of white women employment on black male incarceration, controlling for white male incarceration, some evidence of crowding out effect on white females jobs

  22. Impact on Marriage • Significant OLS, Diff in Diff results for Blacks • Insignificant IV results • Concurs with Wood (1990) that missing Black men may not be “marriage material’’ in the first place but at odds with Charles and Luoh’s (2006) results

  23. Conclusion • Multiple crowding out effects • Welfare Analysis: black women worse off if black men incarceration viewed as additional constraint: first order effect. More complex picture if, say, black women can replace incarcerated black men on the jobs that those can no longer work on or/and if incarceration drives unskilled wage up • More outcomes can be studied • Other data sets could tell us better which women are most affected

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