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See Attachments
**files (2)**
– ## RoughDraftInstuctions.docx
– ## img04222026_0001.pdf
**## RoughDraftInstuctions.docx**
**Rough Draft of Results/Conclusion Section of Research Proposal**
**Instructions:**
Submit a very rough draft of the
**results AND conclusion/discussion section** of your research manuscript.(USE ATTACHED AND REVIEW NOTES/FEEDBACK) I am essentially looking for a thorough rendering of the following:
1. The univariate analysis of your manuscript, including all descriptive statistics (i.e., descriptive statistics AND measures of dispersion/variation). Include all relevant tables.
2. Bivariate analysis and results. Report on the bivariate relationship between your independent and dependent variable, as well as any control variables that may be related to your dependent variable. Include all relevant tables.
3. The primary multivariate findings/results of your research. Do you find data to support your hypothesis? Does your regression model significantly predict your dependent variable? How well does your regression model “fit” your outcome measure/dependent variable? What variables significantly predict your dependent variable? What is the strongest predictor? Include all relevant tables.
4. The discussion/conclusion section should tie everything back together – i.e., what was your topic? Data source? Variables of interest? Primary findings? What does this all mean? What were the limitations of your study? Directions for future research?
**Format:**
There is no specific formatting requirements to this assignment, aside from the fact that the project
**MUST** be typed, with
**1” margins throughout the document.** All students must use
**12-point, Times New Roman Font**.
**## img04222026_0001.pdf**
Does Staffing Shortages Intensify the Harmful Effects of Overcrowding in U.S.
Correctional Facilities
Data and Methods
This paper examines the impact of staffing deficits and the negative impact of
overcrowding in American prisons. Through the national secondary data, it is possible to create
an evaluation of the situation in organizations over a broad range of institutions. The discussion
relies on publicly available datasets that are generated by the Bureau of Justice Statistics (BJS)
and the U.S. Census Bureau, which can be detailed in measuring the facility capacity, population
pressures, staffing patterns, and institutional harms. The combination of these sources provides a
complete understanding of the influence of structural constraints on safety and operations in jails.
Data Sources
The data analysis in this paper will rely on the Census of State and Federal Correctional
Facilities (CSFCF). The 2005 CSFCF is a very comprehensive source of information on the
~ou he -*u dale 7
facility features, capacity, staffing and program availability (Stephan, 2008)!Since the CSFCF
contains data on security level, inmate characteristics, and organizational characteristics, it can
be viewed as a central data to analyzing the organizational attributes of overcrowding and
staffing.
dcda ane morC nh .
The analysis uses the BJS Prisoners in 2020 statistical tables to contextualize recent
trends in population and confirm interventions regarding capacity and custody counts by
showing significant decreases in the American prison population under the impact of the
C0VID-19 pandemic (Carson, 2021b). These data sets offer credible state-level indicators of
3
capacity, custody levels, and changes in patterns of imprisonment, helpful to supplementary
models used to test powerfulness.
The Mortality in State and Federal Prisons, 2001-2019 statistical tables provide detailed
information about the deaths by suicide, homicide, illness, intoxication, and accidents, which is
used to measure institutional harm (Carson, 2021a). The use of mortality patterns assists in the
capture of the extent of institutional strain that is severe and provides an outcome measure that is
attached to overcrowding as well as staffing.
Lastly, to complement the use of cross-referencing on the facility-level and enhancing the
quality of institutional identifiers, the paper uses the report by the U.S. Census Bureau on the
Coverage of Prisons and Detention Facilities in the 2020 Census. This dataset evaluates the
coverage of facilities and the accuracy of the classification, demonstrating that census
enumeration covered virtually all facilities identified by DOJ, but uncovered significant gaps in
small or specialized facilities (Garcia et al., 2024). The data aid in the proper connection of
facility attributes across sources. All the datasets which were used are for public use and do not
have any identifiable personal information.
Key Variables
Dependent Variables
In accordance with the BJS reporting standards, all harm indicators are then translated into
rates per 1,000 inmates to accommodate population variations (Carson, 2021b). There are a few
indicators of institutional harm:
– Inmate-on-inmate assaults (CSFCF).
– Inmate-on-staff assaults (CSFCF).
4
Critical negative events, such as suicide, murder, or drunkenness (Mortality in State and
Federal Prisons).
Operational disruptions, such as lockdowns or major rule violations (CSFCF).
Independent Variables
1. Overcrowding
Overcrowding will be measured as the percent of rated capacity filled:
Overcrowding = \frac{Average Dally Population} (Facility Capacity) x 100
Facilities with a greater than 100 percent capacity will be deemed overcrowded.
Sensitivity analyses will also be done in a continuous measure. This action is in line with
national records on documentation that indicate that facilities in certain states are running beyond
their capacity (Stephan, 2008).
2. Staffing Shortfalls
The increased ratios and vacancy rates show more serious staffing gaps. The shortfall in
staffing is determined by:
Vacancy rate of correctional officers (CSFCF).
The prison-to-staff ratio which has been complemented with state figures of Prisoners in
2020 (Carson, 2021).
3. Interaction Term
The main hypothesis is that the negative impact of overcrowding is enhanced by low staffing
levels. The following interaction term will be built:
Overcrowding x Staffing Shortfall
5
A significant positive coefficient would support the research hypothesis.
Control Variables
Control variables include:
Security level
– Facility size and age
– Gender composition
– Geographic region
– Availability of treatment and rehabilitative programming (Stephan, 2008)
– Proportion of inmates convicted of violent offenses
Analytic Strategy
The effects of overcrowding will be assessed using a multivariate regression model to
determine whether staffing shortages increase the impact of overcrowding. Ordinary least
squares (OLS) models describe continuous outcomes, whereas count-based incident measures
are referred to negative binomial regression, which takes into account that BJS data show that
incidents in prisons and mortality are frequently over-dispersed (Carson, 2021).
Models will be estimated sequentially:
1. Baseline model with overcrowding and controls.
2. Staffing model adding staffing shortfall.
3. Full model including the interaction term.
6
Year fixed effects capture the effects of time variations in the prison populations, including
the variation related to the COVID-19 interruptions (Carson, 2021). These standard errors are
state clustered.
Missing Data and Quality Checks
Multiplex imputation is applied in cases where there is more than 5 percent missingness
and it is observed to be random. The distributional assumptions and multicollinearity are
evaluated using descriptive statistics and variance-inflation diagnostics. The Census Bureau data
consists of cross-validated facility identifiers, which is why the institutions are correctly aligned
(Garcia et al., 2024).
Ethical Considerations
Since every source of data is public-use and includes no identification information, the
project is not human subjects research and thus does not need the IRB.
7
References
Carson, E. A. (2021). Mortality in state and federal prisons, 2001-2019 — Statistical tables (NCJ
300953). Bureau of Justice Statistics. https://bjs.ojp.gov/content/pub/pdf/msfp0119st.pdf
Carson, E. A. (2021). Prisoners in 2020— Statistical tables (NCJ 302776). Bureau of Justice
Statistics. https://bjs.oip.gov/content/pub/pdf/p20st.pdf
Garcia, M. M., Finlay, K., Speer, C. E., Willhide, E., Patti, K. N., & Loveless, T. A. (2024).
Coverage of prisons and detention facilities in the 2020 Census. U.S. Census Bureau.
https ://www2 . census. gov/I ibrary/worki ng-papers/2024/econ/coverage-of-pri sons-and-
detention-facilities-2020-census.pdf
Stephan, J. J. (2008). Census of state and federal correctional facilities, 2005 (NCJ 222182).
Bureau of Justice Statistics. https://bjs.oip.gov/content/pub/pdf/csfcf05.pdf
Tosh\)dz,r FeecabaY P
Juba use ore dc,~-ase1 -_ _ more -Hw,n a-e is
jusE too ~n~ch ~. 1\-e –
\ere . Le}-` s one anal
see whc,} Yve cc~n do 1r~i~h ~ ~,
**## img04222026_0001.pdf**
Does Staffing Shortages Intensify the Harmful Effects of Overcrowding in U.S.
Correctional Facilities
Data and Methods
This paper examines the impact of staffing deficits and the negative impact of
overcrowding in American prisons. Through the national secondary data, it is possible to create
an evaluation of the situation in organizations over a broad range of institutions. The discussion
relies on publicly available datasets that are generated by the Bureau of Justice Statistics (BJS)
and the U.S. Census Bureau, which can be detailed in measuring the facility capacity, population
pressures, staffing patterns, and institutional harms. The combination of these sources provides a
complete understanding of the influence of structural constraints on safety and operations in jails.
Data Sources
The data analysis in this paper will rely on the Census of State and Federal Correctional
Facilities (CSFCF). The 2005 CSFCF is a very comprehensive source of information on the
~ou he -*u dale 7
facility features, capacity, staffing and program availability (Stephan, 2008)!Since the CSFCF
contains data on security level, inmate characteristics, and organizational characteristics, it can
be viewed as a central data to analyzing the organizational attributes of overcrowding and
staffing.
dcda ane morC nh .
The analysis uses the BJS Prisoners in 2020 statistical tables to contextualize recent
trends in population and confirm interventions regarding capacity and custody counts by
showing significant decreases in the American prison population under the impact of the
C0VID-19 pandemic (Carson, 2021b). These data sets offer credible state-level indicators of
3
capacity, custody levels, and changes in patterns of imprisonment, helpful to supplementary
models used to test powerfulness.
The Mortality in State and Federal Prisons, 2001-2019 statistical tables provide detailed
information about the deaths by suicide, homicide, illness, intoxication, and accidents, which is
used to measure institutional harm (Carson, 2021a). The use of mortality patterns assists in the
capture of the extent of institutional strain that is severe and provides an outcome measure that is
attached to overcrowding as well as staffing.
Lastly, to complement the use of cross-referencing on the facility-level and enhancing the
quality of institutional identifiers, the paper uses the report by the U.S. Census Bureau on the
Coverage of Prisons and Detention Facilities in the 2020 Census. This dataset evaluates the
coverage of facilities and the accuracy of the classification, demonstrating that census
enumeration covered virtually all facilities identified by DOJ, but uncovered significant gaps in
small or specialized facilities (Garcia et al., 2024). The data aid in the proper connection of
facility attributes across sources. All the datasets which were used are for public use and do not
have any identifiable personal information.
Key Variables
Dependent Variables
In accordance with the BJS reporting standards, all harm indicators are then translated into
rates per 1,000 inmates to accommodate population variations (Carson, 2021b). There are a few
indicators of institutional harm:
– Inmate-on-inmate assaults (CSFCF).
– Inmate-on-staff assaults (CSFCF).
4
Critical negative events, such as suicide, murder, or drunkenness (Mortality in State and
Federal Prisons).
Operational disruptions, such as lockdowns or major rule violations (CSFCF).
Independent Variables
1. Overcrowding
Overcrowding will be measured as the percent of rated capacity filled:
Overcrowding = \frac{Average Dally Population} (Facility Capacity) x 100
Facilities with a greater than 100 percent capacity will be deemed overcrowded.
Sensitivity analyses will also be done in a continuous measure. This action is in line with
national records on documentation that indicate that facilities in certain states are running beyond
their capacity (Stephan, 2008).
2. Staffing Shortfalls
The increased ratios and vacancy rates show more serious staffing gaps. The shortfall in
staffing is determined by:
Vacancy rate of correctional officers (CSFCF).
The prison-to-staff ratio which has been complemented with state figures of Prisoners in
2020 (Carson, 2021).
3. Interaction Term
The main hypothesis is that the negative impact of overcrowding is enhanced by low staffing
levels. The following interaction term will be built:
Overcrowding x Staffing Shortfall
5
A significant positive coefficient would support the research hypothesis.
Control Variables
Control variables include:
Security level
– Facility size and age
– Gender composition
– Geographic region
– Availability of treatment and rehabilitative programming (Stephan, 2008)
– Proportion of inmates convicted of violent offenses
Analytic Strategy
The effects of overcrowding will be assessed using a multivariate regression model to
determine whether staffing shortages increase the impact of overcrowding. Ordinary least
squares (OLS) models describe continuous outcomes, whereas count-based incident measures
are referred to negative binomial regression, which takes into account that BJS data show that
incidents in prisons and mortality are frequently over-dispersed (Carson, 2021).
Models will be estimated sequentially:
1. Baseline model with overcrowding and controls.
2. Staffing model adding staffing shortfall.
3. Full model including the interaction term.
6
Year fixed effects capture the effects of time variations in the prison populations, including
the variation related to the COVID-19 interruptions (Carson, 2021). These standard errors are
state clustered.
Missing Data and Quality Checks
Multiplex imputation is applied in cases where there is more than 5 percent missingness
and it is observed to be random. The distributional assumptions and multicollinearity are
evaluated using descriptive statistics and variance-inflation diagnostics. The Census Bureau data
consists of cross-validated facility identifiers, which is why the institutions are correctly aligned
(Garcia et al., 2024).
Ethical Considerations
Since every source of data is public-use and includes no identification information, the
project is not human subjects research and thus does not need the IRB.
7
References
Carson, E. A. (2021). Mortality in state and federal prisons, 2001-2019 — Statistical tables (NCJ
300953). Bureau of Justice Statistics. https://bjs.ojp.gov/content/pub/pdf/msfp0119st.pdf
Carson, E. A. (2021). Prisoners in 2020— Statistical tables (NCJ 302776). Bureau of Justice
Statistics. https://bjs.oip.gov/content/pub/pdf/p20st.pdf
Garcia, M. M., Finlay, K., Speer, C. E., Willhide, E., Patti, K. N., & Loveless, T. A. (2024).
Coverage of prisons and detention facilities in the 2020 Census. U.S. Census Bureau.
https ://www2 . census. gov/I ibrary/worki ng-papers/2024/econ/coverage-of-pri sons-and-
detention-facilities-2020-census.pdf
Stephan, J. J. (2008). Census of state and federal correctional facilities, 2005 (NCJ 222182).
Bureau of Justice Statistics. https://bjs.oip.gov/content/pub/pdf/csfcf05.pdf
Tosh\)dz,r FeecabaY P
Juba use ore dc,~-ase1 -_ _ more -Hw,n a-e is
jusE too ~n~ch ~. 1\-e –
\ere . Le}-` s one anal
see whc,} Yve cc~n do 1r~i~h ~ ~,
**## RoughDraftInstuctions.docx**
**Rough Draft of Results/Conclusion Section of Research Proposal**
**Instructions:**
Submit a very rough draft of the
**results AND conclusion/discussion section** of your research manuscript.(USE ATTACHED AND REVIEW NOTES/FEEDBACK) I am essentially looking for a thorough rendering of the following:
1. The univariate analysis of your manuscript, including all descriptive statistics (i.e., descriptive statistics AND measures of dispersion/variation). Include all relevant tables.
2. Bivariate analysis and results. Report on the bivariate relationship between your independent and dependent variable, as well as any control variables that may be related to your dependent variable. Include all relevant tables.
3. The primary multivariate findings/results of your research. Do you find data to support your hypothesis? Does your regression model significantly predict your dependent variable? How well does your regression model “fit” your outcome measure/dependent variable? What variables significantly predict your dependent variable? What is the strongest predictor? Include all relevant tables.
4. The discussion/conclusion section should tie everything back together – i.e., what was your topic? Data source? Variables of interest? Primary findings? What does this all mean? What were the limitations of your study? Directions for future research?
**Format:**
There is no specific formatting requirements to this assignment, aside from the fact that the project
**MUST** be typed, with
**1” margins throughout the document.** All students must use
**12-point, Times New Roman Font**.
**## img04222026_0001.pdf**
Does Staffing Shortages Intensify the Harmful Effects of Overcrowding in U.S.
Correctional Facilities
Data and Methods
This paper examines the impact of staffing deficits and the negative impact of
overcrowding in American prisons. Through the national secondary data, it is possible to create
an evaluation of the situation in organizations over a broad range of institutions. The discussion
relies on publicly available datasets that are generated by the Bureau of Justice Statistics (BJS)
and the U.S. Census Bureau, which can be detailed in measuring the facility capacity, population
pressures, staffing patterns, and institutional harms. The combination of these sources provides a
complete understanding of the influence of structural constraints on safety and operations in jails.
Data Sources
The data analysis in this paper will rely on the Census of State and Federal Correctional
Facilities (CSFCF). The 2005 CSFCF is a very comprehensive source of information on the
~ou he -*u dale 7
facility features, capacity, staffing and program availability (Stephan, 2008)!Since the CSFCF
contains data on security level, inmate characteristics, and organizational characteristics, it can
be viewed as a central data to analyzing the organizational attributes of overcrowding and
staffing.
dcda ane morC nh .
The analysis uses the BJS Prisoners in 2020 statistical tables to contextualize recent
trends in population and confirm interventions regarding capacity and custody counts by
showing significant decreases in the American prison population under the impact of the
C0VID-19 pandemic (Carson, 2021b). These data sets offer credible state-level indicators of
3
capacity, custody levels, and changes in patterns of imprisonment, helpful to supplementary
models used to test powerfulness.
The Mortality in State and Federal Prisons, 2001-2019 statistical tables provide detailed
information about the deaths by suicide, homicide, illness, intoxication, and accidents, which is
used to measure institutional harm (Carson, 2021a). The use of mortality patterns assists in the
capture of the extent of institutional strain that is severe and provides an outcome measure that is
attached to overcrowding as well as staffing.
Lastly, to complement the use of cross-referencing on the facility-level and enhancing the
quality of institutional identifiers, the paper uses the report by the U.S. Census Bureau on the
Coverage of Prisons and Detention Facilities in the 2020 Census. This dataset evaluates the
coverage of facilities and the accuracy of the classification, demonstrating that census
enumeration covered virtually all facilities identified by DOJ, but uncovered significant gaps in
small or specialized facilities (Garcia et al., 2024). The data aid in the proper connection of
facility attributes across sources. All the datasets which were used are for public use and do not
have any identifiable personal information.
Key Variables
Dependent Variables
In accordance with the BJS reporting standards, all harm indicators are then translated into
rates per 1,000 inmates to accommodate population variations (Carson, 2021b). There are a few
indicators of institutional harm:
– Inmate-on-inmate assaults (CSFCF).
– Inmate-on-staff assaults (CSFCF).
4
Critical negative events, such as suicide, murder, or drunkenness (Mortality in State and
Federal Prisons).
Operational disruptions, such as lockdowns or major rule violations (CSFCF).
Independent Variables
1. Overcrowding
Overcrowding will be measured as the percent of rated capacity filled:
Overcrowding = \frac{Average Dally Population} (Facility Capacity) x 100
Facilities with a greater than 100 percent capacity will be deemed overcrowded.
Sensitivity analyses will also be done in a continuous measure. This action is in line with
national records on documentation that indicate that facilities in certain states are running beyond
their capacity (Stephan, 2008).
2. Staffing Shortfalls
The increased ratios and vacancy rates show more serious staffing gaps. The shortfall in
staffing is determined by:
Vacancy rate of correctional officers (CSFCF).
The prison-to-staff ratio which has been complemented with state figures of Prisoners in
2020 (Carson, 2021).
3. Interaction Term
The main hypothesis is that the negative impact of overcrowding is enhanced by low staffing
levels. The following interaction term will be built:
Overcrowding x Staffing Shortfall
5
A significant positive coefficient would support the research hypothesis.
Control Variables
Control variables include:
Security level
– Facility size and age
– Gender composition
– Geographic region
– Availability of treatment and rehabilitative programming (Stephan, 2008)
– Proportion of inmates convicted of violent offenses
Analytic Strategy
The effects of overcrowding will be assessed using a multivariate regression model to
determine whether staffing shortages increase the impact of overcrowding. Ordinary least
squares (OLS) models describe continuous outcomes, whereas count-based incident measures
are referred to negative binomial regression, which takes into account that BJS data show that
incidents in prisons and mortality are frequently over-dispersed (Carson, 2021).
Models will be estimated sequentially:
1. Baseline model with overcrowding and controls.
2. Staffing model adding staffing shortfall.
3. Full model including the interaction term.
6
Year fixed effects capture the effects of time variations in the prison populations, including
the variation related to the COVID-19 interruptions (Carson, 2021). These standard errors are
state clustered.
Missing Data and Quality Checks
Multiplex imputation is applied in cases where there is more than 5 percent missingness
and it is observed to be random. The distributional assumptions and multicollinearity are
evaluated using descriptive statistics and variance-inflation diagnostics. The Census Bureau data
consists of cross-validated facility identifiers, which is why the institutions are correctly aligned
(Garcia et al., 2024).
Ethical Considerations
Since every source of data is public-use and includes no identification information, the
project is not human subjects research and thus does not need the IRB.
7
References
Carson, E. A. (2021). Mortality in state and federal prisons, 2001-2019 — Statistical tables (NCJ
300953). Bureau of Justice Statistics. https://bjs.ojp.gov/content/pub/pdf/msfp0119st.pdf
Carson, E. A. (2021). Prisoners in 2020— Statistical tables (NCJ 302776). Bureau of Justice
Statistics. https://bjs.oip.gov/content/pub/pdf/p20st.pdf
Garcia, M. M., Finlay, K., Speer, C. E., Willhide, E., Patti, K. N., & Loveless, T. A. (2024).
Coverage of prisons and detention facilities in the 2020 Census. U.S. Census Bureau.
https ://www2 . census. gov/I ibrary/worki ng-papers/2024/econ/coverage-of-pri sons-and-
detention-facilities-2020-census.pdf
Stephan, J. J. (2008). Census of state and federal correctional facilities, 2005 (NCJ 222182).
Bureau of Justice Statistics. https://bjs.oip.gov/content/pub/pdf/csfcf05.pdf
Tosh\)dz,r FeecabaY P
Juba use ore dc,~-ase1 -_ _ more -Hw,n a-e is
jusE too ~n~ch ~. 1\-e –
\ere . Le}-` s one anal
see whc,} Yve cc~n do 1r~i~h ~ ~,
**## RoughDraftInstuctions.docx**
**Rough Draft of Results/Conclusion Section of Research Proposal**
**Instructions:**
Submit a very rough draft of the
**results AND conclusion/discussion section** of your research manuscript.(USE ATTACHED AND REVIEW NOTES/FEEDBACK) I am essentially looking for a thorough rendering of the following:
1. The univariate analysis of your manuscript, including all descriptive statistics (i.e., descriptive statistics AND measures of dispersion/variation). Include all relevant tables.
2. Bivariate analysis and results. Report on the bivariate relationship between your independent and dependent variable, as well as any control variables that may be related to your dependent variable. Include all relevant tables.
3. The primary multivariate findings/results of your research. Do you find data to support your hypothesis? Does your regression model significantly predict your dependent variable? How well does your regression model “fit” your outcome measure/dependent variable? What variables significantly predict your dependent variable? What is the strongest predictor? Include all relevant tables.
4. The discussion/conclusion section should tie everything back together – i.e., what was your topic? Data source? Variables of interest? Primary findings? What does this all mean? What were the limitations of your study? Directions for future research?
**Format:**
There is no specific formatting requirements to this assignment, aside from the fact that the project
**MUST** be typed, with
**1” margins throughout the document.** All students must use
**12-point, Times New Roman Font**.
**## img04222026_0001.pdf**
Does Staffing Shortages Intensify the Harmful Effects of Overcrowding in U.S.
Correctional Facilities
Data and Methods
This paper examines the impact of staffing deficits and the negative impact of
overcrowding in American prisons. Through the national secondary data, it is possible to create
an evaluation of the situation in organizations over a broad range of institutions. The discussion
relies on publicly available datasets that are generated by the Bureau of Justice Statistics (BJS)
and the U.S. Census Bureau, which can be detailed in measuring the facility capacity, population
pressures, staffing patterns, and institutional harms. The combination of these sources provides a
complete understanding of the influence of structural constraints on safety and operations in jails.
Data Sources
The data analysis in this paper will rely on the Census of State and Federal Correctional
Facilities (CSFCF). The 2005 CSFCF is a very comprehensive source of information on the
~ou he -*u dale 7
facility features, capacity, staffing and program availability (Stephan, 2008)!Since the CSFCF
contains data on security level, inmate characteristics, and organizational characteristics, it can
be viewed as a central data to analyzing the organizational attributes of overcrowding and
staffing.
dcda ane morC nh .
The analysis uses the BJS Prisoners in 2020 statistical tables to contextualize recent
trends in population and confirm interventions regarding capacity and custody counts by
showing significant decreases in the American prison population under the impact of the
C0VID-19 pandemic (Carson, 2021b). These data sets offer credible state-level indicators of
3
capacity, custody levels, and changes in patterns of imprisonment, helpful to supplementary
models used to test powerfulness.
The Mortality in State and Federal Prisons, 2001-2019 statistical tables provide detailed
information about the deaths by suicide, homicide, illness, intoxication, and accidents, which is
used to measure institutional harm (Carson, 2021a). The use of mortality patterns assists in the
capture of the extent of institutional strain that is severe and provides an outcome measure that is
attached to overcrowding as well as staffing.
Lastly, to complement the use of cross-referencing on the facility-level and enhancing the
quality of institutional identifiers, the paper uses the report by the U.S. Census Bureau on the
Coverage of Prisons and Detention Facilities in the 2020 Census. This dataset evaluates the
coverage of facilities and the accuracy of the classification, demonstrating that census
enumeration covered virtually all facilities identified by DOJ, but uncovered significant gaps in
small or specialized facilities (Garcia et al., 2024). The data aid in the proper connection of
facility attributes across sources. All the datasets which were used are for public use and do not
have any identifiable personal information.
Key Variables
Dependent Variables
In accordance with the BJS reporting standards, all harm indicators are then translated into
rates per 1,000 inmates to accommodate population variations (Carson, 2021b). There are a few
indicators of institutional harm:
– Inmate-on-inmate assaults (CSFCF).
– Inmate-on-staff assaults (CSFCF).
4
Critical negative events, such as suicide, murder, or drunkenness (Mortality in State and
Federal Prisons).
Operational disruptions, such as lockdowns or major rule violations (CSFCF).
Independent Variables
1. Overcrowding
Overcrowding will be measured as the percent of rated capacity filled:
Overcrowding = \frac{Average Dally Population} (Facility Capacity) x 100
Facilities with a greater than 100 percent capacity will be deemed overcrowded.
Sensitivity analyses will also be done in a continuous measure. This action is in line with
national records on documentation that indicate that facilities in certain states are running beyond
their capacity (Stephan, 2008).
2. Staffing Shortfalls
The increased ratios and vacancy rates show more serious staffing gaps. The shortfall in
staffing is determined by:
Vacancy rate of correctional officers (CSFCF).
The prison-to-staff ratio which has been complemented with state figures of Prisoners in
2020 (Carson, 2021).
3. Interaction Term
The main hypothesis is that the negative impact of overcrowding is enhanced by low staffing
levels. The following interaction term will be built:
Overcrowding x Staffing Shortfall
5
A significant positive coefficient would support the research hypothesis.
Control Variables
Control variables include:
Security level
– Facility size and age
– Gender composition
– Geographic region
– Availability of treatment and rehabilitative programming (Stephan, 2008)
– Proportion of inmates convicted of violent offenses
Analytic Strategy
The effects of overcrowding will be assessed using a multivariate regression model to
determine whether staffing shortages increase the impact of overcrowding. Ordinary least
squares (OLS) models describe continuous outcomes, whereas count-based incident measures
are referred to negative binomial regression, which takes into account that BJS data show that
incidents in prisons and mortality are frequently over-dispersed (Carson, 2021).
Models will be estimated sequentially:
1. Baseline model with overcrowding and controls.
2. Staffing model adding staffing shortfall.
3. Full model including the interaction term.
6
Year fixed effects capture the effects of time variations in the prison populations, including
the variation related to the COVID-19 interruptions (Carson, 2021). These standard errors are
state clustered.
Missing Data and Quality Checks
Multiplex imputation is applied in cases where there is more than 5 percent missingness
and it is observed to be random. The distributional assumptions and multicollinearity are
evaluated using descriptive statistics and variance-inflation diagnostics. The Census Bureau data
consists of cross-validated facility identifiers, which is why the institutions are correctly aligned
(Garcia et al., 2024).
Ethical Considerations
Since every source of data is public-use and includes no identification information, the
project is not human subjects research and thus does not need the IRB.
7
References
Carson, E. A. (2021). Mortality in state and federal prisons, 2001-2019 — Statistical tables (NCJ
300953). Bureau of Justice Statistics. https://bjs.ojp.gov/content/pub/pdf/msfp0119st.pdf
Carson, E. A. (2021). Prisoners in 2020— Statistical tables (NCJ 302776). Bureau of Justice
Statistics. https://bjs.oip.gov/content/pub/pdf/p20st.pdf
Garcia, M. M., Finlay, K., Speer, C. E., Willhide, E., Patti, K. N., & Loveless, T. A. (2024).
Coverage of prisons and detention facilities in the 2020 Census. U.S. Census Bureau.
https ://www2 . census. gov/I ibrary/worki ng-papers/2024/econ/coverage-of-pri sons-and-
detention-facilities-2020-census.pdf
Stephan, J. J. (2008). Census of state and federal correctional facilities, 2005 (NCJ 222182).
Bureau of Justice Statistics. https://bjs.oip.gov/content/pub/pdf/csfcf05.pdf
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