Effects of Income Levels on Incarceration Rates

Introduction 

It’s no secret that incarceration rates continue to be an issue in the United States and are ever rising, but what has become more apparent in the recent years is that there is a plethora of variables that have begun to come into play as being the cause for such trends. For my research proposal I would like to uncover the effect of income levels on incarceration rates. To do so I will address the empirical data of incarceration rates across various cities throughout the U.S which is the dependent variable of interest. Then I will evaluate the empirical data of income levels prior to incarceration which will be signified by child poverty rates which shows levels of income that individual is born into within the given city. This will represent the independent variable of interest. The unit of analysis will be the percentage of those incarcerated. By juxtaposing the data and running a regression of said variables, I look to answer the question of the role that income has on one’s likelihood of being incarcerated. Furthermore, by understanding patterns of poverty and cycles that exist due to one’s status after being incarcerated, I will suggest policy implications that may either aid the previously incarcerated to bounce back financially, or aid certain groups with already low incomes to break themselves from the institutional temptations that may enter them into the incarceration/poverty cycle.

Literature Review

The research paper titled “Prisons of Poverty: Uncovering the pre-incarceration incomes of the imprisoned,” by Bernadette Rabuy and Daniel Kopf, looked to analyze the earnings of individuals who were incarcerated (Prior to incarceration) and those who were not incarcerated. The authors chose to organize the data by race and gender and found that incarcerated people had a “median annual income of $19,185 prior to their incarceration which was 41% less than non-incarcerated people of similar ages”. Furthermore, not only are the median incomes of incarcerated people prior to incarceration lower than non-incarcerated people, but “incarcerated people are dramatically concentrated at the lowest ends of the national income distribution.” Another paper that discusses this same connection titled “Work and opportunity before and after incarceration,” by Adam Looney and Nicholas Turner examines economic characteristics of the incarcerated population.  They find that three years prior to incarceration, only “49 percent of prime-age men are employed, and, when employed, their median earnings were only $6,250”. Furthermore, they continue to look deeper into the past to understand whether these conditions that lead to incarceration begin much earlier than three years prior. They find that boys who grew up in families in the “bottom 10 percent of the income distribution-families earning less than about $14,000- are 20 times more likely to be in prison on a given day in their early 30s than children born to the wealthiest families- those earning more than $143,000”. The authors estimate that almost “one in ten boys born to lowest income families are incarcerated at age 30 and make up about 27 percent of prisoners” at that age. Ultimately the two articles work to conjure up the fact that the poorer your parents are, the more likely you are to be incarcerated. Both portraying the fact that pre-incarceration income has a large correlation with your likelihood of being incarcerated.

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 The next two sources have to deal with the effects of this loss of income and how becoming incarcerated yields so many other factors that breakdown individual’s ability to recover or progress financially. One source called “Incarceration’s Effect on Economic Mobility,” by The Pew Charitable Trusts analyzes data of incarceration rates by age, race, and education to uncover the growth, scale, and concentration of incarceration and furthermore, the various effects it has generationally. This paper includes a lot more variables of interest than simply pre-incarcerated income, and also dives deeper into the results and this idea of a cycle that begins to form as one is less likely to make substantial income after release from incarceration, and thus will have kids who will grow up with a lower income level, and since children born into lower income levels have higher risks of becoming incarcerated, they will simply fall into the same trap as their parents and the cycle will become hard to break. Additionally, the next source titled “Incarceration & Social Inequality,” by Bruce Western & Becky Pettit explain how the contours of American social inequality have been transformed by the rapid growth of incarceration rates. This paper focuses on the idea that America’s prisons and jails have produced a new social group, a group of social outcasts who are joined by the shared experience of incarceration, crime, poverty, racial minority, and low education. By analyzing data that show gaps in a variety of different areas that come together to depict one’s overall earnings mobility, the authors are able to assert the fact that although there are many effects of incarceration rates, all effects lead to one common denominator and it’s the fact that certain specific groups most of which are African American continue to suffer the most from incarceration rates and income levels and everything in between. I will juxtapose the data from the first two sources and the analytical interpretations of the second two sources to conjure up the overall hypothesis that pre-determined income levels, if low enough at one’s birth, may make us to be more likely to become incarcerated and lead us into one of the most detrimental poverty cycles we know in this country.

Empirical Model

Linear regression is used to estimate the relationship between incarceration rates and childhood poverty rates. In this paper we estimate the following regression model:

 Inc. Rt =  + 1Poverty Rt.+ 2SingleParent+  3College Attend.+4Race + u

In the model above ‘Inc. Rt’ measures the percentage of population that is incarcerated in a given city. Household variables include ‘Poverty Rt.’ which measures proportion of children who are born into poverty, and ‘SingleParent’ which measures percentage of households run by a single parent.

 ‘College’ variable measures percentage of population that attended a college or university. Finally, the ‘Race’ variable measures percentage of population that belongs to either one of three dummy variables: Black, Asian, or Other.

Data

Incarceration rate is the author’s estimate of the share of the 1980-1986 cohort living in these neighborhoods in 1996-2000 who were later incarcerated at age 30. Demographic and economic variables from the 2000 Decennial Census.

 Dependent variable defines incarceration rates as percentage of individuals who are incarcerated in any form of imprisonment.

 Independent variable of interest measures percentage of population that are born into poverty in a given city. The rate shows proportion of individuals who are considered below the federal poverty line at their time of birth. Meaning, the variable does not include those who may have entered below the poverty line at some point in their life, even though they were not born into poverty or low income. Bankruptcy, shortcomings, financial disasters are not included in the variable.

 City characteristics include single parent household percentage, college attendance, and race divided into three subgroups (Black, Asian, other).

Table 1 – Descriptive Statistics

   Variable |      Obs        Mean    Std. Dev.       Min        Max

————-+———————————————————

        City |           0

       IncRt |          51    .0684706    .0453878          0        .1

 PovertyRt |        51    .2685098    .1755738       .012     .56

     SingleP |         51    .1227451    .0450368        .06      .27

     College |         51    .4231373    .2625147        .13      .89

————-+———————————————————

       Black |         51    .4672549    .3309627          0         .98

       Asian |         51    .0282353      .04385            0         .24

       Other |         51    .0684314    .0691194        .01       .26

 Table 1 above shows a substantial variance in college attendance rates ranging as low as 13 % to as high as 89% with a mean of 42.3%.  Child poverty rates show a wide variance as well with a low of 1.2%, a high of 56%, and with a mean of 26.9%. Single Parent percentage has the smallest variance with a low of 6% and a high of 27%, meaning across the cities in the sample, there were no significant differences in the percentage of individuals with only one parent.

 

Empirical Results

Regression results in Table 2 iterate that child poverty rates are a key contributor to incarceration rates. Higher rates of child poverty lead to higher rates of incarceration across the U.S. When child poverty rates increase by 1%, incarceration rates grow by 8% illustrating a substantial positive correlation between the two variables. The race variable of being black also showed significance as a 1% increase in population of black individuals would lead to a 5.9% increase in incarceration rates.

Table 2

        Source |       SS            df         MS         Number of obs   =        51

 ————-+———————————-      F(6, 44)        =    121.17

        Model |     .097124776           6     .016187463     Prob > F        =    0.0000

     Residual |   .00587793           44    .000133589     R-squared       =    0.9429

 ————-+———————————-      Adj R-squared   =    0.9352

        Total |    .103002706          50    .002060054     Root MSE        =    .01156

——————————————————————————

       IncRt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]

————-+—————————————————————-

   PovertyRt |   .0803865   .0204823     3.92   0.000     .0391071    .1216658

     SingleP |  -.1125419   .0419052    -2.69   0.010    -.1969963   -.0280876

     College |  -.0499585   .0257766    -1.94   0.059     -.101908    .0019909

       Black |   .0588606   .0149253     3.94   0.000     .0287807    .0889405

       Asian |  -.0013029   .0515896    -0.03   0.980     -.105275    .1026692

       Other |   .0563438   .0429333     1.31   0.196    -.0301826    .1428703

       _cons |   .0505175   .0200744     2.52   0.016     .0100602    .0909749

It is also important to note that although the coefficients of ‘povertyrt’ and ‘black’ seem small, they are the only variables that are positively correlated with incarceration rates. This means that holding all other things equal, black people and those who are born into poverty suffer higher incarceration rates that any other group. Coincidentally, historical data shows us that these two groups have been linked for quite some time as black people statistically have a higher percentage of children born into poverty. The ‘other’ race variable is ambiguous as it’s inclusive of the White and Hispanic races who possess different levels of poverty and incarceration rates. Single parent percentage, college attendance percentage, and the variable of being Asian are all proven to be insignificant toward incarceration rates.

Conclusion & Policy Implications

 This research question is significant, because our society has, in the name of being tough on crime, made a series of policy choices that have fueled a cycle of poverty and incarceration. We send large numbers of people with low levels of education and low skills to prison, and then when they leave just as penniless as they were when they went in, we expect them to bear the burden of legally-acceptable employment discrimination. Acknowledging, as this report makes possible, that the people in prison were, before they went to prison, some of the poorest people in this country makes it even more important that we make policy choices that can break the cycle of poverty and incarceration. We have to put into understanding that although there are limited variables in this empirical model, there are a substantial amount of variables that oppose those who become incarcerated, and it’s these subtle variables like the ability to gain employment as a felon that stand in the way of one’s ability to make financial progress following incarceration. We call these institutional obstacles, because they are all the result of historical policies that were made decades ago that all work to hinder and outcast a specific group of people simply because they’ve been incarcerated. Furthermore, when we address the variable of race it becomes clear that the black race is a group that has felt the effects of this ‘cycle’ of poverty and incarceration the most. Conclusively, when we make this realization and juxtapose the fact that there are institutional as well as racial obstacles, we reach a new entity called ‘institutional racism’ that emerges as a deeper and more complex situation to deal with. This type of racism is a result of years of anti-black attitudes that have circled the world for centuries dating back to slavery. Moreover, when you have societies that are governed by individuals who possess this anti-black mindset, they create rules in light of this way of thinking and create a nation that is inevitably governed by this same way of thinking. Ultimately, a nation is created in spite of the black race, and all the rules and regulations that govern it institutionally work against the black race.  Therefore, policy implications that may lend increased aid to black people may be able to improve their financial state before being incarcerated, and may consequentially help improve the racial wage gaps that exist in this country. Re-trial of black people already incarcerated may be necessary as it is a true fact that racial profiling exists in the judicial system and many black people behind bars are not worthy of the sentenced they were given, but worthy of another chance. A real chance at a real American Dream.

 

References

Adam Looney and Nicholas Turner. “Work and Opportunity Before and After incarceration”. The Brookings Institute, March 2018

Bernadette Rabuy and Daniel Kopf. Prisons of Poverty: “Uncovering the pre-incarceration income of the imprisoned”. July 2015

The Pew Charitable Trusts, 2010. “Collateral Costs: Incarceration’s Effect on Economic Mobility”. Washington, DC: The Pew Charitable Trusts.

American Academy of Arts & Science, 2010. “Incarceration & Social Inequality”. Bruce Western & Becky Pettit

Decennial Census (2000). “neighborhood Rates of Incarceration”. Economic Studies at Brookings