Analysis of the Relationship between Poverty and Crime

Introduction 

Different literature materials blame unemployment and the increasing gap of income inequality as the driving causes of violent criminal activities and other social ailments such as prostitution and sex trafficking, and drug abuse. This assertion is supported by the increased cases of violent crimes in informal and low-income settlements ( Anser et al., 2020) . Also, the educational attainment is compromised in communities with high levels of poverty as the households cannot afford quality education and other basic needs. This situation forces young people to drop out of schools to seek informal employments to earn an income. Due to their dismal education, these people fail to secure well-paying jobs; thus, they cannot get out of poverty. The US Census Bureau defines poverty as the inability of the income of a person or household to meet the minimum required to cover essential needs ( Brandman University. 2018) . Thus, a person is poor if they do not meet the threshold for basic survival. According to this definition, some 40.6 million Americans (about 12.7% of the national population) live below the poverty line, which comes with severe social issues ( Jenks & Fuller, 2016) . The highest rates of poverty are among African Americans, where 27.4% of the people live below the poverty line. Several studies have shown that a direct relationship exists between poverty and violent crime ( Jenks & Fuller, 2016) . This report uses various statistical methods to analyze the relationship between poverty and crime and improve the situation. 

Organization Sponsoring this Study 

For several years, the Josca Charity Organization has been at the forefront in fighting poverty in the informal settlements throughout Loss Angeles and its environs. The organization established a trend of increased crime in these regions. Also, the organization determined that most people who engaged in violent crime and other social crimes, and whose names were available in the city’s criminal list came from backgrounds with inadequate education and thus were not qualified for formal jobs. Several theories have been promoted to try and explain these behaviors. Since most of the criminals came from single-parent families, it was assumed that single parenthood is a catalyst to crime among children. However, this theory had limitations as it did not explain children brought up with both parents but turned started engaging in criminal activities. The level of household income was the only item that most homes in these regions shared. Thus, a preliminary conclusion linked poverty to crime. This report gives Josca Charity Organization the missing link between poverty and crime, thus proving their preliminary conclusion. The results of this research will help the organization to develop and implement the recommendation for solving the problem. 

Research Questions and Objectives 

The primary objectives of this report are: 

To determine the link or relationship between household income and crime. 

To formulate actionable recommendations to reducing crime in informal settlements. 

To create awareness on the risks that poverty and crime present to the economic and social wellbeing of the society. 

The research question for this study was: How does income distribution relate to criminal activities in Loss Angeles, California. An observational research design was selected as it involved voluntary interviewing people in a public courthouse to determine the potential causes of criminal activities ( Statistics Solutions, 2020) . The study presumed that exposure to poverty and crime influenced people’s reasoning and criminal activities, thereby meeting the criteria for California’s Three Strikes Law. Thus, understanding these dynamics would help develop proper measures of preventing the various types of crimes. 

Data Collection and Methodology 

To collect data for this study, a sample of 31 random respondents at the Loss Angeles courthouse were asked to disclose their household incomes and self-reported criminal activities voluntarily. Half of the respondents for this study were bystanders, while the other half were awaiting trials for different criminal activities. The respondents were of mixed sexes and ethnic groups. The technique used in choosing the sample was plausible and most appropriate for the study. This is because it eliminated bias or discrimination of any kind. Also, most arrests do not translate to conviction; thus, picking respondents from the courthouse ensured that only people awaiting trials or people who have interacted with crime during the study were interviewed. Voluntary disclosure eliminated risks of coercion; thus, only respondents who were willing to give out their data were included in the study. 

Despite the measures to ensure quality and valid data was collected, the study was still susceptible to biases and problems. For instance, one could assume that because the respondents were present in the courthouse, they were all criminals. However, it was found out that only half of the respondents were awaiting different forms of trials, while the other half were bystanders. Another possible bias is that the study would link everybody with low income to criminal activities. However, this may not be true. The study relied on the respondents to be truthful while disclosing information about their incomes and self-reported crime cases. Unfortunately, some respondents may give false information, thereby derailing the validity of the study. 

Type of Data 

The study made use of qualitative data as it relied on observations, interviews, and frequencies that were collected in the form of narratives ( McDonald & University of Delaware. 2009) . The respondents were asked to provide information on their self-reported criminal activities voluntarily. However, some elements of quantitative data were evident as the researchers conducted headcounts on the number of respondents and assigned numerical values. Also, the respondents’ incomes could be quantified; hence this data is quantitative. The nominal level of measurement was applied in collecting this data. 

Description of Variables 

The independent variables for this study were the income of the respondents’ families, and it was measured in thousands of dollars. The independent variable, on the other hand, was the number of self-reported criminal activities. Some confounding variables for this study included the respondents’ sexes, age, and ethnic groups (. Also missing were the types of criminal activities the respondents were charged with, or trials they were awaiting. The absence of these variables would affect the study in different ways. For example, the absence of respondents’ ages makes it difficult to evaluate if age was a factor in criminal activities. Information on their sexes would help establish the prevalence of criminal activities among the males and female residents of Loss Angeles. The same case would apply if the information on the respondents’ ethnic groupings were recorded. Thus, the absence of these variables undermines the conclusiveness of this study. 

Part 2: 

The Data 

The table below (Table 1 of the Appendix) shows the results of the survey. 

This data was used to give various visual representations on the trends of self-reported criminal activity against the income levels of respondents. The first visual representation is the scatter plot below. 

Fig. 1: Scatter Plot 

The trend line in the scatterplot shows an inverse relationship between family income and self-reported criminal activities. This relationship shows that communities with higher incomes tend to experience less criminal activities. The bar graph in fig. 2 below, however, does not provide a conclusive statement on the relationship between crime and poverty. 

Fig. 2: Bar Graph 

The line graph in fig. 3 further reiterates the same point as the bar graph above that no clear link exists between poverty and crime. 

Fig. 3: Line Graph 

The data on the number of self-reported criminal activities cannot be described as normally distributed. The data is aligned towards one side of the graph, as visualized in the histogram below. 

Fig. 4: Histogram showing the frequency distribution of the family incomes. 

From the histogram for family income in fig. 4 above, the bell-curve needed to classify a data set as normally distributed is absent. The measures of central tendency (mean, mode, and median), the standard deviation, and the range were determines, as shown in the table below. 

Mean	$52.5806
Median	$35
Mode	$40
Range	$195
Standard Deviation	$46.218881
Maximum	$200
Minimum	$5
Q1	$25
Q3	$65

The histogram for criminal activities also shows that the data is not distributed normally, and the bell curve is missing, as shown in fig. 5 below. 

Fig. 5: Histogram showing the distribution of criminal activities. 

From the graph, it is evident that respondents that reported lower criminal activities had a higher frequency as compared to the respondents with higher self-reported criminal activities. The table below shows measures of central tendency, the standard deviation, and the quartile ranges. 

Mean	5.097
Median	3
Mode	1
Range	20
Standard Deviation	4.969
Maximum	20
Minimum	0
Q1	1
Q3	9

Part 3: 

Analysis 

The hypothesis claim for this study was: 

Low incomes increase criminal activities. To test this claim, the null hypothesis and alternative hypothesis were developed, as shown below. 

The null hypothesis, H 0 = Low incomes, increase criminal activities. 

The alternative hypothesis, H 1 = High incomes, increase criminal activities. 

The possible outcomes of this hypothesis can be summarized in the table below: 

Claim: Low incomes increase crime 

	State of Nature
Decision	H 0 True	H 0 False
Reject H 0	Low incomes increase crime  Insufficient supporting evidence to the claim  Alpha error	High incomes increase crime  Insufficient supporting evidence to the claim  -Correct Assessment
Fail to reject H 0	Low incomes increase crime  Sufficient evidence supporting the claim  Correct assessment	High incomes increase crime  Sufficient evidence supporting the claim.  Beta error

The most appropriate test for this study is the test of two dependent means. This study compared two sets of scores: family incomes and self-reported criminal activities. This test helps in establishing a correlation between the two variables under investigation. A two-tailed t-test with a significance level of 0.1 was used to determine whether or not to reject the hypothesis claim. An online calculator was used to find the values of t and please, as shown below: 

Fig. 6: Two-tailed t-test analysis 

The value of t is -4.082106. The value of p is .00049. The result is significant at p < .10. 

To reject the null hypothesis, H 0 , the value of p needs to be less than 0.05; otherwise, the alternative hypothesis is rejected ( McDonald & University of Delaware. 2009) . From this computation, the value of p was found to be p = 0.00049, which is less than 0.05; hence a null hypothesis is rejected. This means that the alternative hypothesis, H 1, is not rejected. The results of this analysis show that unlike popular belief, poverty does not increase crime. 

Part 4: 

Recommendations 

The results of this study go against every assertion that that was previously made regarding the relationship between poverty and crime. However, this raises an even tricky question: why is a crime more prevalent in the informal settlements and other areas where the majority of inhabitants are low incomes earners? Perhaps the entire case is more psychological than financial, and the link existing between poverty and crime is a psychological creation ( Cabrera-Barona et al., 2019) . Also, the increased prevalence of violent crime in low-income neighborhoods may be a result of exposure, especially at early development stages. Usually, children adapt to the behaviors they witness or actions they experience in their environment. Children who are raised in these neighborhoods, thus, grow up witnessing a violent crime in their surroundings ( Dong et al., 2020) . As a result, their brains develop with the imprint that crime and violence are the normal order; hence they embrace such activities. The low levels of education in these hoods worsen the situation in that children are not exposed to the right content. Education opens the children’s mental capacity and reasoning, thereby enabling them to reason out things differently. Without this educational exposure, these children grow up with fixed minds to promote violence to make a living. The circulation of hard drugs in low-income settlements also makes the situation worse. An uneducated and intoxicated teenager is more likely to commit crime than their educated and non-intoxicated peers. Drug addiction and the lack of money further makes a difficult situation terrible. To address the problem of violence and crime in the City of Loss Angeles, this report outlined the following recommendations. 

Improve the standards of education in informal and low-income settlements – Education opens the minds of children and teenagers. With open minds, the young people, irrespective of their financial status, will reason for better ways of earning a living than engaging in violent crime ( UNODC Vienna. 2019) . To improve the quality of education in the low-income areas, Josca Charity Organization, in cooperation with the local, state, and federal governments, can build better, more affordable learning institutions. These institutions should be equipped with books, valuable equipment, and they should have enough teachers to ensure that the children get a quality education that puts them at a fair level to compete with their peers from high-income hoods. Additional learning activities should be provided in social centers such as churches and public halls. 

Improve security and Combat drug trafficking – The second recommendation to solving crime in informal settlements is by improving security in these areas. The local and state governments, in collaboration with private stakeholders, should ensure that children grow up in safe environments that are free from violence and crime. Law enforcers should step up their efforts in ensuring that drug trafficking and sex trafficking are combated to the latter. A safe environment means children will not be exposed to violence and crime, thereby giving them the desire to become useful people in society ( UNODC Vienna. 2019) . The school curricular should be structured such that they enhance ethical norms while discouraging drugs and crime. The result of this action is that children will grow up in a positive and harmless environment, and they will strive to use their lives positively. 

Offer psychological counseling to children growing in these settlements – Children living in low-income neighborhoods experience many challenges that may interfere with their mental development. For instance, they barely have enough to eat. Their parents work round the clock to provide basic needs; thus, the children spend most of their time without the close watch of parents. Other children from single-parent families face the stigma due to missing one of their parents throughout their lives. These challenges may weigh so heavily on the children and dire psychological consequences ( UNODC Vienna. 2019) . Thus, these children require constant motivation, counseling, and reassurance to overcome these problems. In severe instances, teenagers may be duped into believing that drug use may help them forget the problems surrounding them. With no family to guide them, these children may sink into the abyss of addiction and, subsequently, crime. In such an instance, the children need to be rehabilitated accommodated, as this makes them feel loved. 

Limitations of the Study 

The study cannot be used to establish a conclusive report on the relationship between poverty and crime for many reasons. First, a sample size of only 31 people is relatively small for a study on such an important topic. Secondly, the study focused on only two variables; family income and self-reported criminal activities. Several critical variables, such as sex, gender, and ethnicity, were not considered. Third, a scientific study should be conducted in different geographical areas to improve its conclusiveness. As for this study, all respondents were collected from a common point of gathering without considering their areas of residence. Moreover, lastly, the study relied on self-reported information that is subject to dishonesty and distortion. 

References 

Anser, M. K., Yousaf, Z., Nassani, A. A., Alotaibi, S. M., Kabbani, A., & Zaman, K. (2020). Dynamic linkages between poverty, inequality, crime, and social expenditures in a panel of 16 countries: Two-step GMM estimates.  Journal of Economic Structures ,  9 (1).  https://doi.org/10.1186/s40008-020-00220-6 

Brandman University. (2018, October 23).  What is criminal justice? A closer look at the field and those who work in it .  https://www.brandman.edu/news-and-events/blog/what-is-criminal-justice 

Cabrera-Barona, P. F., Jimenez, G., & Melo, P. (2019). Types of crime, poverty, population density, and police presence in the metropolitan district of Quito.  ISPRS International Journal of Geo-Information ,  8 (12), 558.  https://doi.org/10.3390/ijgi8120558 

Dong, B., Egger, P. H., & Guo, Y. (2020). Is poverty the mother of crime? Evidence from homicide rates in China.  PLOS ONE ,  15 (5), e0233034.  https://doi.org/10.1371/journal.pone.0233034 

Jenks, D. A., & Fuller, J. R. (2016).  Global crime and justice . Taylor & Francis. 

McDonald, J. H., & University of Delaware. (2009).  Handbook of biological statistics . 

Statistics Solutions 2020. (n.d.).  Data levels of measurement . Statistics Solutions.  https://www.statisticssolutions.com/data-levels-of-measurement/ 

UNODC Vienna. (2019).  GLOBAL STUDY ON HOMICIDE . United Nations Office on Drugs and Crime.  https://www.unodc.org/documents/data-and-analysis/gsh/Booklet1.pdf 

Appendix 

Table 1 

Family Income (thousands)	Number of self-reported instances of criminal activity
160	1
5	8
12	0
100	1
18	7
200	0
16	2
25	10
40	5
32	13
14	20
90	1
30	9
40	6
15	14
28	10
30	8
120	0
30	10
40	4
35	4
47	2
51	3
75	0
130	1
55	3
25	9
15	3
35	0
65	1
52	3