The document explores the factors driving local jail incarceration rates in the U.S., emphasizing that rural counties have higher incarceration rates compared to urban areas. It highlights significant variables influencing these rates, such as county poverty and demographics, and reveals that state policies may affect local jail rates. The analysis utilizes data from various counties between 2000 and 2013 and identifies a substantial portion of the rural-urban incarceration divide as explainable by socio-economic factors.

1. www.twosigma.com
Exploring the Urban – Rural Incarceration Divide:
Drivers of Local Jail Incarceration Rates in the U.S.
Rachael Weiss Riley
Two Sigma Data Clinic
Bloomberg Data for Good Exchange
September 24, 2017

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September 24, 2017
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3. The U.S. has the world’s highest incarceration rate
September 24, 2017 3

4. Rate has gone down slightly in recent years
September 24, 2017 4

5. Mass Incarceration: the whole pie
5
State
Prisons
1.3 m
Federal
Prisons
197 K
Total incarceration
2.3 m
Local Jails
630 K
September 24, 2017
Source: Prison Policy Initiative (2017)

6. Research focuses on state or federal prisons rather than
local county jails
September 24, 2017
0
20,000
40,000
60,000
80,000
100,000
"Prison" "Jail"
Results for in paper title,
from Google Scholar:
6
86,500
8,690

7. Vera launched the Incarceration Trends project to facilitate
county-level jail research
September 24, 2017 7

8. Rural counties have higher local jail rates
September 24, 2017
150
200
250
300
350
2000 2003 2006 2009 2012
Small and Mid Metros
Large Metro, Suburban
Large Metro, Urban
Rural
Local Jail Rate per 100,000 population
8

9. Research questions
September 24, 2017
1. What are the characteristics of a county
that are associated with local jail
incarceration rates?
2. In terms of local jail incarceration rates,
how do counties compare to their peers?
9

10. The following variables were included in the final model:
September 24, 2017
YearUrban
Code
County’s
metro/rural
classification
(4 categories)
2000 to
2013
+ + Other Characteristics
Latinos as percent of total jail population
Blacks as percent of total jail population
Percentage of total jail population awaiting trial
Inmates held for other counties (per 100,000) by county
Inmates held for the state (per 100,000) by county
Jail
The outcome variable is the
local jail population per
100,000 by county
10

11. The following variables were included in the final model:
September 24, 2017
Urban
Code
Year
County’s
metro/rural
classification
(4 categories)
2000 to
2013
+ + Other Characteristics
Hispanics as percent of county population
Non-Hispanic blacks as percent of county population
Percent of county living in poverty
Percent unemployed in county
County’s welfare spending (per 100,000)
County
County’s Police and Corrections spending (per 100,000)
The outcome variable is the
local jail population per
100,000 by county
11

12. The following variables were included in the final model:
September 24, 2017
Urban
Code
Year
County’s
metro/rural
classification
(4 categories)
2000 to
2013
+ + Other Characteristics
Percent of federal prisoners held in local county jails
State’s total prison population (per 100,000)
State
The outcome variable is the
local jail population per
100,000 by county
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13. 3,145 counties with data from 1970 –
2013, but many had missing data
2,858 counties: 1,783 rural; 690 small/mid
metro; 338 suburban; 47 urban
Dependent variable represents counts Poisson distribution (log-linear model)
Observations not independent
(multiple years for each county leads to
correlation within counties)
Generalized estimating equations (GEE)
model accounts for within-county
correlations
Prior to 2000, different census
classification and lacking covariate data
14 years of data (2000 – 2013)
September 24, 2017
A GEE model was specified to analyze the data
Starting point Implication
13

14. We considered multiple nested models that included different
combinations of variables
September 24, 2017
Urban Code + Year Effects:
Local jail rate ~ urban code + year
Urban Code + Year Effects + Other Characteristics:
Local jail rate ~ urban code + year + all additional variables
14

15. Half of the urban/rural jail rate gap
is explainable by our set of characteristics
September 24, 2017 15
34.6%
31.4%
8.1%
14.8%
11.7%
not significant
0% 10% 20% 30% 40%
Rural vs.
Large Metro, Suburban
Rural vs.
Large Metro, Urban
Rural vs.
Small and Mid Metros
Percent change in local jail rates
(rural vs. metro)
Urban Code + Year
Urban Code + Year + Other
Characteristics

16. County-level poverty
has the strongest association with local jail rates
September 24, 2017
County poverty (%)
County unemployment (%)
Federal inmates held in local jails (%) by state
Non-Hispanic black jail pop. (%)
Total prison pop. (per 100,000) by state
Jail inmates (per 100,000) held for other counties
County welfare spending ($/100,000)
Hispanic jail pop. (%)
County Hispanic pop. (%)
Jail inmates awaiting trial (%)
County police & corrections spending ($/100,000) Not Significant
Significant
Jail inmates (per 100,000) held for the state
County non-Hispanic black population (%)
Percent change in local jail rates
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17. How do counties compare to their peers?
September 24, 2017
Response residuals = observed – expected rates
 Positive: observed (actual) rate is higher than expected
 Negative: observed (actual) rate is lower than expected
Residuals were calculated from a separate model that excludes race/ethnicity to
identify under- and over-performing counties
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18. How do counties compare to their peers?
September 24, 2017 18
Arizona
591
64
Average local jail
rate per county

19. -122
152
Average residuals
per county
How do counties compare to their peers?
September 24, 2017 19
Arizona
Maricopa County, AZ
0
50
100
150
200
250
300
Actual: 228
Expected rate: 114
Nat’l median: 206

20. 152
-122
Average residuals
per county
Geographic clusters revealed at a more local level after
controlling for other characteristics
September 24, 2017 20

21. Residual clusters appear to adhere to state boundaries
suggesting state policies impact jail rates
September 24, 2017
Average residuals
per county -122 152
21

22. What we learned
September 24, 2017
 Half of the rural-urban divide can be explained by model covariates
 Poverty is important, more so than crime and arrest rates
 Residuals reveal local and state geographical clusters
 State policies may play an important role in local jail rates
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23. Thank you
September 24, 2017
dataclinicinfo@twosigma.com
Co-authors:
Jacob Kang-Brown (Vera)
Chris Mulligan (Data Clinic)
Vinod Valsalam (Data Clinic)
Soumyo Chakraborty (Data Clinic)
Christian Henrichson (Vera)
Christine Zhang (Data Clinic)
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