How will education impact recidivism of prisoners? In the United States, crime rate represents more than just a security issue, the racial attention and separation between classes are just a couple of obvious examples. The significance of the amount of non-white prisoners has been a continuing social issue for decades, and the level of education of these prisoners is one of the important reasons of it happening.
Racial equality has been an ongoing issue in the United States. According to Bureau of Justice statics (BJS 2012), 66% of the prisoners were either black or Hispanic in 2011. The statics demonstrate the disadvantage of being black and Hispanic in the U.S, as well as the discrimination against them. Few of these colored prisoners received proper education, which reduced the opportunities of them getting jobs, and increase the chance of committing crimes. As for discrimination, false accusations towards black and Hispanic people are still relatively common, the stereotype of non-white people being potential criminals still exists.
On a theoretical basis, recidivism refers to the relapse into criminal activity and is measured by former inmate`s return to prison. Rates of recidivism are a reflection of the level to which released inmates have been rehabilitated and role of rectification programs play in preparing inmates to go back to the society.For example, the rate of recidivism in U.S. is approximately two-thirdswhich basically means about two-thirds of the released inmates will be re-imprisoned within short period of about three years.The high level of recidivism result in tremendous costs both in terms of public safety as well as tax spent to arrest, prosecute and imprison re-offenders. In addition, high level of recidivism leads to devastating social costs to the communities and families of offenders, and individual costs to the offenders.
The proposed research captures issues relating to the impact of education on recidivism of the prisoners. The question of whether prisoner education eliminates the possibility of returning to prison, and the obligation that the state holds towards the respondents. The dependent variables of the study included the level of education of the prisoners, the learning environment, and the racial discrimination faced by the blacks and Hispanics residing in the United States of America.
Giving attention to rates of recidivism is a significant way to study the role of prisons in rehabilitating inmates. Traditionally, prisons were designed to punish and confine law-breaking citizens. On the contrary, as more tax collection goes to correctional budgets, public policy and opinion have increasingly demanded that prisons expand programs that are mandated to rehabilitate inmates and prepare them for return to their communities.Effectiveness of prisons in rehabilitating inmates can be measured inways besides recidivism. Some of the examples can be reduction in substance abuse among released inmates and increases in their employment rates as well as their educational levels (Percival 2009).However, recidivism offers a more about measure of a prison`s efforts to rehabilitate inmates. In addition, recidivism affects a major social and economic concern, which is rate of crime, whereas recidivism produces desirable outcomes in that they educate prisoners or help in the recovery from substance abuse and ultimately crime rates of re-imprisonment.
The proposed research is aimed at testing the hypothesis that prisoners who actively engage in education programs are least likely or have a lower probability of recidivating, which is defined as parole revocation within three years after they are they have served their prison term. The hypothesis is tested through use of data for federal prison releases, whereby controlling for different background as well as the post release measures and these include post-release employment status.
The hypothesis is simplified by use of independent variable “X” in connection to dependable variable “Y” whereby y ~f (X)
Y – Represents inmates` likelihood of recidivism by the prisoners
X – Representing inmates` participation in education programs
Human capital theory suggests that education and employment programs may be a means to lower recidivism rates. Employing a theoretical model will demonstrate that development of human capital raises opportunity cost of crime. This suggests an inverse relationship between the probability of committing a crime and the level of human capital developed. The reasoning proposes that human capital developed while incarcerated often impact the likelihood of committing a crime after release, hence impacting inmate recidivism (Lochner 2004). The proposed researches develop a hypothetical model with two types of human capital, criminal and legal. According to Mocan et al (Mocan 2005), time in prison depreciates legal human capital and at the same time increases the likelihood of prisoner recidivism. There is evidence that support these hypotheses. Micon et al proposes that job training and education programs are a reliable way to overcome depreciation of legal human capital. However, there are several studies that have attempted to establish whether program participation consistently lowers offending behavior in the future and the findings have been incompatible (Vacca2004).
Analysis on different existing studies reveals that lack of consensus with regard to effectiveness of education and employment programs come as a result of poor methodological assessment. Hence, this research proposal suggests that participant`s recidivate a reduced rate compared to non-participants, in contrast a generally weak methodology employed in most studies avoid ascriptionof observed effects to the program activities (Lochner2004).
Percival (2009) presents racial segregation as one of the other factors that would affect the likelihood of a prisoner to commit a crime after release. Unlike other studies that do not consider that there could be other factors, the study shows that the environment outside prison could predetermine the frequency and nature of crimes. As the current study analyzes whether the disparities in ethnic groups lead to increased chances of a certain group being involved in crime, Percival`s study provides a basis on which the study can base its ideas.
Vacca (2004) identifies an extremely crucial consideration in education. He points out lack of willingness to learn among inmates as one of the reasons why the program is not successful. Unlike other studies, he points out difference in levels of education among inmates, which could affect what they are to learn in the curriculum developed. However, the studies fail to identify suggestions to the society that could lead the released inmates being embraced back in a way that will help them implement what they have learnt. This is due to the perception the society will have over them as having formerly being jailed that makes them less employable despite the qualifications attained.
According to Harer (Harer 1994), the rates of semi-annual periods based on a logitmodel, unlike the previous research, an inclusion of dummy variables for both the pre and post-release employment. In his study Harer monetizes yearly imprisonment cost savings of education programs in the guiding principles conclusion that are largely potent to savings from these programs. The other concern regards hazard function modeling. Besides a constant hazard rate model not being employed, a more direct and important impact is found prior to convictions include different forms of drug abuse for example heroin, alcohol and so forth. An inverse effect is observed in salient factors score, for instance stable employment before imprisonment, social lay offexperiences, and employment at release living with family (spouse) after imprisonment. The educational program is significant according to Harer findings education programs among other rehabilitation programs for example prison labor, presents similar benefits that have an impact on recidivism. Inconsistency in findings among different studies does not come a s surprise given that the correctional industry and education interventions happens albeit late in life in any artificial environment.
The chapter takes into consideration the instruments utilized prior to and in the course of the study.The proposed research models duration of time until recidivating as a parametric framework, which allows recovery of the functional forms for hazard and (Mocan 2005). The decision to adopt modeling framework was motivated by the desire to produce measures of present value of net cost savings from remaining out of prison attributable to participation in prison education as well as employment programs. Derivation of these functions is a central component of the calculation of the present value of cost saving benefits in connection to participation in prison programs (Lochner 2004).
There are several reasons to why prisoners participate in these programs. They include social communication, encouragement from correctional personnel to participate and the expected impact with regard to parole hearings. The presumption of these programs has a major impact on recidivism, whereby inmates` participate since their involvement increases the likelihood of their employment after serving their prison time or on their release. In addition, these programs have an impact on the released prisoners` ability to perform efficiently in the job market, and ultimately impact on their likelihood to return to prison.
The proposed research intends to obtain answers to the following concerns: effects of level of education on recidivism of the prisoners evaluate the effects of emotional implication of the prisoners on recidivism and lastly, examine the impact of racism imposed to the colored prisoners on recidivism. It goes a head to identify two ways through which participation in programs might causally impact inmate`s probability to a successful job after serving prison term. To begin with, there is the likelihood for improved participation among inmates` characteristics that would keep them out of prison after release even in the absence of participation. Therefore, prisoners may employee the programs as to signifying signaling to potential employers that contain favorable labor market signaling.
In this case the participating inmate seems more attractive to potential employer rather than non-participating prisoners since participation signify willingness to practice good work habits. Secondly, a more independent labor market signaling once a former prisoner is hired. Involvement in prison programs has a possibility of improving their skills and working habits, this makes participants successful employees and at the same time increases job duration after being employed (Vacca2004). The two effects reduce prisoner recidivism. Hence, the proposed researches expectprogram participation improves the length of time until returning to prison. Considering both labor market signaling influence and the human capital to impact on, the program participation has a direct causal influence on prisoner recidivism.The expected date does not allow for identity of the effect separately.
Level of education of the prisoners, the low level of education prompts the needs to result into crimes and drug abuse to cope with the situation at hand. The situation increases the chances of recidivism. Providing education programs for the prisoners assists in ensuring that the prisoners can pay for their daily needs after prison. This way the society ensures reduced crime rates in the society. The learning environment in prison refers to the setting and the atmosphere in which the prisoners experience their training. The idea of the being confined in the same place one is learning leads to mind blocks experienced by the prison student. Change of environment ensures learning as the prisoners are now away from their routine residence (Vacca2004).
The use of descriptive research will be employed to find out the details related to the phenomenon related to the study. The methods facilitate the knowledge of the population under study. The method ensures systematic description with accurate and factual information.The study targets various prisoners confined at various prisons in the United States and some of the prison wardens.Stratified sampling design will be used in the study, the target population experience sections of divisions named stratum. The method will be prompted by the heterogeneous nature of the respondents and based on their criminal charges. The samples willdraw from the stratums hence the random picking of the respondents will apply.The prisoners of the United States will be the sample size in this case.
The data collection to be employed includes sources found on prisons and prisoners in the United States.Selected data will be analyzed in different categories: time spent in prison, age at release, number of in imprisonment,education received in prison and previous education level. Each variable will show a sign related to thelikelihood for post-release employment of prisoners. For example, education is expected to have a positive impact on the chance of prisoners not committing crimes again, but this assumption will be tested by the data collected, by observing if the prisoners` education level in the United States have a positive or negative relationship with number of in imprisonment.
If the educational programs have impact on the crime rate, the policymakers should make plans on building more schools in the urban area, especially where Hispanic and Black population dominates. The results can also be found in the percentage of the educated prisoners return to jail, comparing to the previous years where there were no educational facilities. The earlier stage of the result may not be consistent, considering the resistance of the prisoners on the new programs. However, once the program becomes more acceptable for the inmates, education will be taken more seriously, and eventually shows sizable decline on the crime rate.
Draft table for results of the analysis
Likelihood of recidivism
Age at release in years
Time spent in prison in months
Number of previous imprisonment
Education received in prison in month
Previous education level
Employment status post release (=1 if employed)
Results and Discussion
In order to determine the relationship between the dependent and independent variables, regression analysis was performed on the data obtained from the Bureau of Justice Statistics (2013), and from the studies conducted by Nally (2012) and Fabelo et al (2000). Data from these sources have high accuracy and validity as will be heretofore discussed: the Bureau of Justice Statistics (BJS), which was established in December 7, 1979, is a component of the Office of Justice Programs in the United States (US) Department of Justice (DOJ). The BJS performs the following tasks for the DOJ: collect, analyze, and disseminate relevant information on crime, victims of crime, criminal offenders, and operation of justice systems on different levels of government. These data are then used to create relevant policies to control and prevent crimes as well as in ensuring that justice is both evenhanded and efficient. At present, the BJS has been successful in collecting information from fifteen (15) states in America which includes Indiana and Texas. The information collected from these states include: gender of offenders, length of stay in prison, level of education when offenders were incarcerated, age, etc. These data are relevant for this research in determining the likeability of recidivism. Nevertheless, it should be pointed out that the following variables are not found in the BJS portal: Employment status post release (X2), Education received in prison in month (X4), and Time spent in prison in months (X6). In order to continue with the determination of the correlations between these three independent variables and the dependent variable (Y), two researches where utilized. These two researches are those conducted by Fabelo (2000), and Nally et al (2012). Fabelo in his study entitled, “Impact of Educational Achievement of Inmates in Windham School District on Recidivism,” he used the knowledge repositories of the Texas Department of Public Safety (DPS) and the Texas Department of Criminal Justice (TDCJ).Nally et al (2012), on their work entitled, “The Post-Release Employment and Recidivism Among Different Types of Offenders With A Different Level of Education: A 5-Year Follow-Up Study in Indiana,” used the knowledge repositories of Indiana Department of Correction (IDOC), the IDOC Education Division, and the Indiana Department of Workforce Development (DWD). The raw data used on these studies were obtained from their respective publications.
From the above sources the following data variables are defined:
(1) Y, which is the key dependent variable, may be represented by the five sub-variables or supporting variables designated by the BJS, namely: Rearrested, Readjudicated, Reconvicted, Reincarcerated, or Reimprisoned. For this research Y would be the average rate of these 5 sub-variables. The recidivism data in BJS were that for the year 1996 and 2002. This definition of the Y variable will be used throughout the analysis.
(2) X1, which is an independent variable, is defined as the age of the offender upon his or her release. It is important to consider this variable, because it is a major factor in determining the level of education a particular individual can have while he or she is within the prison premises (Nally et al., 2012). This variable will be measured in months.
(3) X2, is an independent variable and defined as the time spent in prison in months. It is assumed in this research that the longer the individual stays in prison the lower the likelihood of recidivism will be. This variable will be measured in months.
(4) X3, is another independent variable which is equivalent to the Number of previous imprisonment the offender had. It is assumed in this study that education will have little positive effects on such individuals.
(5) X4, is the key independent variable which evaluates the level of education attained by the offender while he or she was imprisoned. The unit of measure used for this variable is called the Educational Achievement Score (EAS), this unit measure`s how well the individual performed in his or her studies. Note that this measure was chosen in order to consider the effect of IQ differences. The EAS is an indirect measure of the IQ of the offender as well as a direct measure of what he or she has learned (Fabelo, 2000).
(6) X5, is an independent variable which is equivalent to the educational attainment of the offender before imprisonment. It is expected in this research that such individuals will have lower rates of recidivism.
(7) X6, is the last independent variable. It is equivalent to the employment status of the offender after release. It is expected in this research that employment after release decreases that likelihood of recidivism.
The relationship of the independent variables (X1, …, X6) to Y were determined using the built in functions of Microsoft Excel and recidivism analysis toll used by the BJS. Accordingly, the best fit curves were determined for each relationship: (Y to X1,…, Y to X6). This was done by determining the Pearson Product Moment Correlation Value (R), of all trial functions, namely: Linear function, Polynomial function, Exponential function, and Logarithmic function. The trial function with the highest R value would be the best fit function yielding the best fit curve. Note that the R is a measure of the degree of correlation between at least two variables.
Analysis and Findings
Figure 1: Correlation between likelihood of recidivism and age of release
There is a strong correlation between Y and X1 as indicated by the R value of 0.9088. The best fit curve is a second degree polynomial curve. This means that offenders released at the age of late 30s are most likely to recidivate. Those ages below 20s and above 50s are the least to recidivate.
Figure 2: Correlation between likelihood of recidivism and time spent in prison
The best fit curve for the correlation between Y and X2 is a linear. The high R value is an indication of strong correlation (R = 0.7723). There is an indirect relationship between these two variables as indicated by the negative value of the slope (-0.1244). This means that more time a particular offender spends in prison, the less like he or she will recidivate.
Figure 3: Correlation between likelihood of recidivism and number of previous imprisonment
The relationship between the variables Y and X3 is best described by a linear curve with a positive slope (slope = 3.6501). The positive slope value is indicative of a positive correlation that is, there is a higher likelihood of recidivism for offenders who have been previously imprisoned multiple times. Note also that there is a strong correlation between these two variables as indicated by the high R value (R = 0.9254). Moreover, the high slope value means that a small increase in X3 would result to a significantly high increase in Y.
Figure 4: Correlation between likelihood of recidivism and level of education received expressed as education achievement score (EAS)
There is an overall decrease in the likelihood of recidivism with increasing level of education attained while in prison, except for those with education below 4. Note that the value of 4 is equivalent to “some high school.” The behavior of the correlation is best expressed by a polynomial curve with R2 equal to 0.9868.
Figure 5: Correlation between likelihood of recidivism and level of education of offender prior to imprisonment measured in years
There is general trend of decrease in the likelihood of recidivism as the level of educational attainment of the offender increases, with the exception of those who just reached below 10 years of education which show fewer tendencies to recidivate. This result is in consistence with the those given in figure 4 and may be related to the age of the respective offenders that is, those who have acquired low educational attainment both while they were not yet in prison and while they were in prison belong to the younger aged groups (Nally et al., 2012). The correlation between these two variables is best described by a polynomial curve with a correlation value of R = 0.7629.
Figure 6: Correlation between likelihood of recidivism and employment after release
There is a strong negative correlation between variables Y and X6, as indicated by the correlation and slope values, respectively (R = 0.944, slope = -1.1136). This means that as released offenders become more employable, which may be achieved through educating them while they are in prison. It is important to note also the high value of slope means that a small decrease in employability will result to significant decrease in the likelihood of recidivism.
A summary of the statistical findings is given in the table below.
Table 1: Summary of statistical findings
Correlation value (Weak < 0.5, Moderate, 0.5, Strong, >0.5)
Y and X1
Y and X2
Y and X3
Y and X4
Y and X5
Y and X6
Summary and Conclusions
This research aims to determine the extent of effect of educating inmates while they are incarcerated to the likelihood of recidivism. In order to achieve this aim, data on recidivism from the Bureau of Justice Statistics, Indiana Department of Correction (IDOC), the IDOC Education Division, and the Indiana Department of Workforce Development (DWD), Texas Department of Public Safety (DPS) and the Texas Department of Criminal Justice (TDCJ). The works of Nally et al (2012) and Fabelo (2000) were also extensively used. Results from this study are highly significant to prisons in making effective education programs aimed at reducing recidivism.
From the results and findings discussed above, it can be concluded that education, indeed can decrease the likelihood of recidivism at a significant level. It is also concluded in this study that prison programs which aim to educate its inmates should focus on increasing employability. A higher degree of focus should be given to those have had multiple imprisonment, and to those who have attained lower degree of education compared to their peers while they were not yet imprisoned because these groups of inmates show higher tendency to recidivate.
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Nally, J.M., Lockwood, S., Ho, T., Knutson, A. (2012). “The Post-Release Employment and Recidivism Among Different Types of Offenders With A Different Level of Education: A 5-Year Follow-Up Study in Indiana.” Justice Policy Journal, 9(1): 1-29.
Assignment 3 Su Yang