Poverty and Crime

Poverty and Crime

Crime exists everywhere in the United States – in rural and urban areas, in the East and West, and among all types of people.  This has led many government officials, especially those in urban areas, to focus largely on the reduction of crime among their respective constituencies and has led others to speculate on the factors that influence the amount of crime and how those factors can be controlled

In the US, urban crime is often perceived as a problem amid areas with high poverty levels.  This may be the case; however, many other factors, such as unemployment, population density, minority population, age distribution, and locality in the US, are correlated with crime and affect poverty as well.  When these factors are controlled for, how much does poverty affect crime?  Knowing more accurately how poverty affects crime can help us know if focusing on the reduction of poverty can aid in crime reduction or if money and effort should go to other areas.

Poverty’s effects on crime can be explained through a variety of reasons.  “There is a higher rate of mental illness in the poor than in the rich” (Brill 40).  Poverty can lead to high levels of stress that in turn may lead individuals to commit theft, robbery, or other violent acts.  Moreover, poverty may lead to an actual or perceived inferior education, which would cause youth to count on less access to quality schools, jobs, and role models, decreasing the opportunity costs of crime and increasing the probability of youth spending time on the street associating with gangs, etc (Ludwig 1).

Crime offers a way in which impoverished people can obtain material goods that they cannot attain through legitimate means.  Often threat or force can help them acquire even more goods, this induces them to commit violent acts such as robbery, which is the second most common violent crime.  For many impoverished people, the prize that crime yields may outweigh the risk of being caught, especially given that their opportunity cost is lower than that of a wealthier person.  Thus, poverty should increase crime rates.

However, many other factors influence crime and are correlated with poverty as well.  Higher unemployment would certainly increase poverty and at the same time lead to more crime due to depression associated with being unemployed.  Personal income per capita, which is inversely correlated with the poverty level, still may increase crime since greater wealth means greater benefits to thieves and robbers.  Furthermore, because of social class gaps, personal income per capita rates may not affect poverty to a great extent (the income may be concentrated in a small percentage of the population).  It might even accentuate the difference between the upper and lower classes, thereby inducing more crime.

Variations in the composition of population can affect crime in different ways.  First, adolescents are often responsible for crimes committed.  “The poor delinquent child... is more apt to be expelled from school or have a police record than a well-to-do delinquent...” (Brill 40).  A higher percentage of inhabitants under the age of twenty-five may lead to higher crime rates.  On the other hand, the elderly, because of their possessions and vulnerability, are believed to be the most frequent victims of crime.

The degree of minority population in an area is also correlated with poverty due to the disproportional amount of minorities living in impoverished urban areas.  In addition, racism towards minorities can lead to lower wages and fewer jobs, resulting in higher poverty rates.  In 1995, all Metropolitan Areas with unemployment rates over 12% also had a population composed of at least 30% minorities.

Geographic regions within the US have different characteristics and therefore lead to differing levels of both crime and poverty.  The 1999 UCR report, for example, indicates that law enforcement personnel varied between 2.5 and 4.3 persons per 1000 population among differing regions of the US.  Climate, associated with geographical location, is also believed to affect crime - more temperate climates being positively correlated with crime.  Cultural factors such as recreational activities, religious characteristics, and family cohesiveness are all associated with geographic regions of the US and influence crime.

Because of the manner in which population density influences living conditions (ie:  houses vs. apartment complexes), it is also likely to be correlated with both poverty and crime.  Studies have found that “more densely populated neighborhoods tend to be poorer, have higher percentages of persons in the age range of 12 to 20, have larger concentrations of single-parent households, and larger nonwhite populations” (Short 52).

This study first examines how poverty affects crime in the simple regression model.  Then, controlling for the aforementioned factors - race, unemployment, personal income, population density, geographic location, and age distribution - it again examines the relationship between crime and poverty and how this relationship is influenced when these factors are held constant.

            All the data used in this study come from the 1997-1998 State and Metropolitan Area Data Book from the Bureau of the Census.  The data covers 322 Metropolitan Areas, including 245 MSA’s, 17 CMSA’s, 15 PMSA’s, and 12 NEMSA’s (which are essentially MA’s of different sizes and characteristics).  Each MA contains a population of at least 100,000 inhabitants of which fifty percent or more live in urban areas.  Seventeen of the 322 MA’s have been eliminated due to a deficiency in the data.

            Since this study is based on crime in urban areas, these data provide an excellent resource for determining the causes of crime.  Data from larger areas (such as states) would be too general and too many conflicting characteristics within the area would be incorporated into the data.  The Metropolitan Areas provide a sample composed of similar makeup, but with sufficient variation to provide a good model.  Each area has different poverty levels, population density, etc.

List and Description of variables

Dependent Variables

Total Crime

total number of the seven index crimes reported to police per 100,000 inhabitants

Violent Crime

total number of the four violent crimes reported to police per 100,000 inhabitants

Independent Variables

Population Dens.

thousands of inhabitants per square mile

Pop. Under 25

percentage of population under 25 years old

Pop. Over 65

percentage of population over 65 years old

Poverty

percentage of population below the poverty level

Unemployment

percentage of population unemployed

Black

percentage of black population

Asian

percentage of Asian and Pacific Islander population

Hispanic

percentage of Hispanic population; may be of any race

PI per Capita

personal income per capita measured in thousands of US (1995) dollars

Dummy Variables - Regions of the US

NE

New England:  ME, NH, VT, MA, RI, CT

MA

Middle Atlantic: NY, NJ, PA

SA

South Atlantic: DE, MD, DC, VA, WV, NC, SC, GA, FL

ENC

East North Central: OH, IN, IL, MI, WI

WNC

West North Central: MN, IA, MO, ND, SD, NE, KS

ESC

East South Central: KY, TN, AL, MS

WSC

West South Central: AR, LA, OK, TX

PAC

Pacific: WA, OR, CA, AK, HI

MNT

Mountain: MT, ID, WY, CO, NM, AZ, UT, NV

 

            The population data from the Data Book are based on the assumption that “population change can be represented by administrative data in a statistical model.”  The Bureau of the Census conducts a nationwide census every ten years.  It then uses data from documents that somehow reflect the change in the population - birth and death certificates for example - to estimate how the population is changing and what its current level is.  This paper includes independent variables based on population density, race/ethnic group, and age which are taken from this data (State and Metropolitan Area Data Book A-1).

            The Data Book obtains crime data from the Uniform Crime Reporting Program, which consists of data voluntarily submitted to either the FBI or state UCR Programs by law enforcement agencies across the United States.  The two dependent variables in this study are based on this data.  “Total Crime” represents the seven index crimes:  murder and nonnegligent manslaughter, forcible rape, robbery, aggravated assault, burglary, larceny-theft, and motor vehicle theft.  These are known as “index crimes” because of their seriousness, frequency of occurrence, and likelihood of being reported to police.  “Violent Crime” represents the four violent crimes as defined by the UCR program:  murder and nonnegligent manslaughter, forcible rape, robbery, and aggravated assault.  The crime data are reported as crime rates - the number of crimes committed per 100,000 inhabitants.  Although “Violent Crime” is incorporated into “Total Crime,” it is used in this study to determine if poverty has effects specific to violent crimes; it also provides a second data set to observe.  (A-9)

            Unemployment data are based on the Current Population Survey, which are annual averages of monthly figures.  The unemployed “are all civilians who did not work during the survey week, who were available for work during the survey week (except for temporary illness), and who made specific efforts to find a job in the prior 4 weeks.  Persons waiting to be recalled to a job from which they had been laid off also are counted as unemployed” (A-11).

            Personal Income per Capita figures, as found in the Data Book, were taken from the Survey of Current Business conducted by the US Bureau of Economic Analysis.  They consist of the personal income received by, or on behalf of, all members of the area less personal contributions for social insurance.  That total is then divided by the resident population.  The figures provide a picture of the overall wealth of the area.

            Poverty data is based on the Current Population Survey as well.  “Families and persons were classified as below poverty level if their total family income or unrelated individual income was less than the poverty threshold specified for the applicable family size, age of householder, and number of related children under 18 present” (A-12).

 


            Descriptive Statistics

Variable

Mean

Std. Dev.

Minimum

Maximum

Total Crime

5414.5

1788.7

1114

13148

Violent Crime

607.28

326.75

33

1881

Pop. Density

.42359

.87778

.0053

11.808

Under 25

36.236

4.064

21.8

55.9

Over 65

12.646

3.554

4.7

34.3

Poverty

14.96

4.9998

5.9

41.1

Unemployment

5.6997

2.8551

1.8

28.7

Black

11.238

10.588

0.1

48.1

Asian

2.8049

4.7823

0.3

64.3

Hispanic

9.2669

14.293

0.4

94.9

PI per Capita

20.449

3.6567

10.347

34.282

NE

0.03934

0.19473

0

1

MA

0.11803

0.32318

0

1

ENC

0.14426

0.35193

0

1

WNC

0.05246

0.22332

0

1

SA

0.19672

0.39817

0

1

ESC

0.06885

0.25362

0

1

WSC

0.14754

0.35523

0

1

MNT

0.08525

0.27971

0

1

PAC

0.14754

0.35523

0

1

 

            The first analysis will examine two simple regressions of total crime and violent crime on poverty.  These reflect the affect that poverty has on crime before controlling for other variables.

Total Crime = β0 + β1(poverty) + µ

R-square =   0.1421     R-square Adjusted =   0.1392

Standard Error of the Estimate-Sigma =   1659.5

Variable

Est. Coefficient

Standard Error

 

 

Poverty

134.85

19.04

 

 

Constant

3397.1

300.2

 

 

 

 

 

Violent Crime = α0 + α1(poverty) + є

R-square =   0.1439     R-square Adjusted =   0.1411

Standard Error of the Estimate-Sigma =   302.83

Variable

Est. Coefficient

Standard Error

 

 

Poverty

24.789

3.474

 

 

Constant

236.42

54.79

 

 

 

            In both regressions, the estimated coefficient of poverty is very significant.  These regressions show that a one percent increase in the population below poverty level will lead to an increase of about 135 total crimes and about 25 violent crimes.

            Now the regressions of total and violent crime will be expanded to include all of the previously mentioned variables.  The models have been reported with heteroskedasticity-robust standard errors.  The geographic region that has been left out is the South Atlantic.  In addition, Caucasians are not included in the race/ethnic group list.

Total Crime

 

Violent Crime

R-square =  0.4363      R-square Adjusted =  0.4029

 

R-square =   0.4727     R-square Adjusted =   0.4414

Standard Error of the Estimate-Sigma =   1382.2

 

Standard Error of the Estimate-Sigma =   244.21

Variable

Est. Coefficient

Standard Error

 

Variable

Est. Coefficient

Standard Error

Poverty*

134.08

40.11

 

Poverty*

16.237

7.146

Population Dens.

4.6788

117.1

 

Population Dens.*

30.294

10.1

Unemployment*

-119.07

49.11

 

Unemployment

-11.171

10.03

Under 25

58.77

46.69

 

Under 25

-0.69455

7.19

Over 65o

63.89

37.87

 

Over 65o

11.651

6.345

Black*

60.187

14.64

 

Black*

17.431

2.735

Asian

3.4373

24.58

 

Asian

-1.4666

3.798

Hispanic

17.223

12.81

 

Hispanic*

5.6293

2.173

PI per Capita*

142.52

37.98

 

PI per Capita*

18.19

6.67

MA*

-1547

312.7

 

MA*

-146.54

49.8

ENC

-342.37

305.6

 

ENC

43.305

57.54

WNC

531.09

743.8

 

WNC

73.512

102

NE*

-653.64

332.1

 

NE

-31.975

76.68

ESC

-554.77

350.9

 

ESC

-93.855

66.74

WSC

23.448

331.7

 

WSC

24.489

64.23

MNT*

845.23

412.8

 

MNT

38.401

72.89

PACo

844.6

430.2

 

PAC*

187.04

69.01

Constant

-2544.4

2451

 

Constant

-342.46

388.2

oSignificant at the 10% level

 

oSignificant at the 10% level

*Significant at the 5% level

 

*Significant at the 5% level

The estimated coefficient of poverty shows that holding all the other variables constant, a 1% increase in the population under the poverty level will increase total crimes by 134 per 100,000 inhabitants.  It is surprising that this coefficient is almost identical to the simple model, even after adding and holding constant sixteen independent variables, but the standard error has increased which yields slightly less significance - although it still is significant at the 0.1% level.  The coefficient in the multivariate Violent Crime model decreased by about 33% compared with the simple model; its significance decreased as well.  In any case, both of these amounts are substantial.

            In order to obtain the elasticity of crime with respect to poverty, the regressions will be performed using the natural log of the crime rate and dividing the poverty data by 100 (so that the data reads as a number between 0 and 1 instead of a percentage between 0 and 100).

ln(Total Crime)

 

ln(Violent Crime)

R-square =   0.4764     R-square Adjusted =   0.4454

 

R-square =   0.4946     R-square Adjusted =   0.4647

Standard Error of the Estimate-Sigma =   0.27270

 

Standard Error of the Estimate-Sigma =   0.46917

Variable

Est Coefficient

Std Error

 

Variable

Est Coefficient

Std Error

Poverty*

2.1587

0.775

 

Poverty o

2.5682

1.31

Population Dens.

0.014904

0.02266

 

Populatn. Dens. o

0.037154

0.02088

Unemployment*

-0.019119

0.009085

 

Unemployment

-0.01676

0.01854

Under 25*

0.021036

0.009248

 

Under 25

-0.000049444

0.01441

Over 65o

0.014082

0.007229

 

Over 65

0.018493

0.0127

Black*

0.012319

0.002719

 

Black*

0.033024

0.004836

Asian

-0.00075461

0.004671

 

Asian

-0.0060179

0.005357

Hispanic

0.0029442

0.00205

 

Hispanic*

0.010896

0.003262

PI per Capita*

0.034803

0.007276

 

PI per Capita*

0.046468

0.01199

MA*

-0.39484

0.07122

 

MA*

-0.37916

0.1042

ENC

-0.067366

0.06495

 

ENC

0.055824

0.1081

WNC

0.046439

0.1208

 

WNC

-0.096352

0.2354

NE*

-0.11344

0.06982

 

NE

-0.14647

0.185

ESC

-0.077228

0.06542

 

ESC

-0.14096

0.12

WSC

0.035607

0.05517

 

WSC

0.12404

0.09242

MNT*

0.18064

0.07099

 

MNT

0.097143

0.1261

PAC*

0.17643

0.08775

 

PAC*

0.40353

0.1247

Constant

6.5173

0.4742

 

Constant

4.2651

0.7393

oSignificant at the 10% level

 

oSignificant at the 10% level

*Significant at the 5% level

 

*Significant at the 5% level

 

            The estimated coefficient of poverty now yields crime-poverty elasticity.  The ln(Total Crime) model shows that holding the other variables constant, a 1% increase in poverty leads to a 2.16% increase in total crime.  This coefficient is significant at the 0.6% level.  The ln(Violent Crime) model shows that a 1% increase in poverty leads to a 2.57% increase in violent crime and is significant only at the 5.1% level.

            The results of this analysis are consistent with other studies that have been conducted by economists, for example:

[Studies] found that homicides were disproportionately concentrated in areas of poverty.  Three of these (by Bullock [1955], Beasley and Antunes [1974], and Mladenka and Hill [1976]) studied violent crime in Houston.  Like Shaw and McKay [1969], each reported high correlations between violent crime rates and measures of poverty.  Areas in Houston with high rates of violent crime were also characterized by high population density and a high proportion of black residents.  (Short 51)

These studies as well as the above analysis show that poverty is correlated with violent crimes - increased poverty leads to increased violent crime.

Another study by Jens Ludwig, Greg J. Duncan, and Paul Hirschfield also that teens committed more violent crimes when they lived in high poverty areas than when they were relocated to low poverty areas.  “The offer to relocate families from high- to very low-poverty neighborhoods (census tracts with poverty rates below 10 percent) reduces juvenile arrests for violent offenses on the order of 30 to 50 percent of the arrest rate for controls” (Ludwig 13).  However, in their paper, Ludwig and his associates state that “high-poverty areas have on average higher rates of violent crime but lower rates of property crime than low-poverty areas” (13).  This contradicts the findings in the above analysis.  Nevertheless, Ludwig and associates offer explanations as to why this may be the case:  “teens are subject to heightened scrutiny in low-poverty areas, and national victimization surveys suggest that victims are more likely to report less-serious crimes to the police in suburbs than cities” (14).  They also believe that the experimental group used in their experiment may actually change their behavior when they are moved to a new area.

Although this study did not include regressions of nonviolent crime on poverty, the “Total Crime” models capture more nonviolent than violent effects.  This can be seen by comparing the means of the Total Crime and Violent Crime - Total Crime includes about 4800 nonviolent crimes on average.  The crimes Ludwig referred to may be less serious than the three nonviolent index crimes, thus resulting in a different correlation.

            Conclusion:  Evidence greatly suggests that increases in poverty lead to increased violent crime.  Evidence from this paper also suggests the same for all seven index crimes and careful studies can yield more verification of the matter[1]. 


Works Cited

Short, James F., Jr.  Poverty, Ethnicity, and Violent Crime.  Boulder, CO:  Westview Press,

1997.

Brill, Norman Q.  America’s Psychic Malignancy.  Springfield, IL:  Charles C Thomas

Publisher, 1993.

Ludwig, Jens, Greg J. Duncan, and Paul Hirschfield.  Urban Poverty and Juvenile Crime: 

Evidence From a Randomized Housing-Mobility Experiment.  20 April 2000. 

< http://www.jcpr.org/wpfiles/duncan.ludwig.revise4-25.PDF>



[1] Although this analysis provides an interesting outlook on the relationship between poverty and crime, it has various shortcomings.  The small scope of this examination yields a lack of independent variables and results omitted variable bias.  For a more accurate picture of the relationship between poverty and crime, variables such as police force, family structure, climate, divorce rate, religion, and educational attainment could be added.  Crime may be influenced by trends within a city that cannot be measured by any variables, so time series data of the Metropolitan Areas could help capture characteristics specific to a certain Area and would be helpful in this study.


By Blake Taylor, 2006
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