Will Gentrification Affect Crime in Washington DC?

The present state of Washington DC is different from its conditions in the past two decades. Gentrification is the process of rebuilding cities to meet the residential needs of the middle-class and wealthy individuals. The poor individuals in the gentrifying cities relocate to the affordable neighborhoods. The enterprises in the gentrifying areas are also restructured to meet the demands of the wealthy individuals. Gentrification has positive or negative effects in the neighboring areas. It increases crime rates in the neighborhood in the short-term. The causes of the increase in the crime rates include the rise unemployment levels and costs of living. Gentrification develops the economy, cultural practices, and social groups in the neighboring areas (Kirk & Laub, 2010) . Gentrification increases the crime rates in the short-term but they reduce in the long-run in poor neighborhoods. 

This research paper proceeds as follows. The first section will focus on the relationship between gentrification and crime. It will also outline the effects of gentrification on the crime rates in the poor neighborhoods of Washington DC. The second section of the paper is an analysis of past literature that supports the effects of gentrification in the neighborhoods of developed or developing cities. The third section is an outline of theories that support the facts of this research paper. The fourth section focuses on the methods of analyzing the information about the research. The fifth section of this paper outlines the results and discussions of the analysis of data. The sixth part of the paper focuses on the findings of the research. The next section outlines the limitations and delimitations of the study. The final part of this paper is the conclusion. 

History of Gentrification in Washington 

Gentrification was introduced in Washington DC in the 1990s. In 1991, the murders rates increased to 479 deaths (Lawrence, 2013). The crime levels in the Washington neighborhoods increased since 1991 (Lawrence, 2013) . The investments in poor neighborhoods also increased. The poor neighborhoods in Washington increased from 90 to 113 in 2000 and 2015 respectively (Lawrence, 2013). The poverty levels rose due to an increase in housing prices in the metropolitan areas. However, the poverty levels in Washington’s neighborhood increased from 13% to 14% in 2000 and 2015 respectively (Lawrence, 2013). There was an increase of three percent of displaced people in the poor neighborhoods of Washington from 2000 to 2015. The poverty levels in poor neighborhoods of the state decreased by 8% from 2000 to 2015 (Lawrence, 2013) . 

Research Question 

Does gentrification affect the crime rates in neighborhoods of the developed city? 

Research Hypotheses 

H1: Housing has a significant influence on crime. 

H2: Crime has a significant influence on housing 

Gentrification and Crime 

Gentrification causes the displacement of poor individuals from the city. They settle in the neighboring areas where the houses are affordable and the cost of living is lower. The immediate effects of gentrification include an increase in tax revenue, improvement in facilities, and a rise in investments (Kirk & Laub, 2010) . As a result, the crime rates rise above average because the displaced residents encounter challenges in maintaining the high living standards. The crime rates reduce in the long-run after the residents in the poor neighborhoods acquire skills in meeting the expenses of the living standards. 

This study will focus on the influence of gentrification in the poor neighborhoods in Washington DC. The poverty rate of the Washington GC neighborhoods is 23% (Aikman, 2014). These areas are dominated by middle and low-income earners. The unemployment rate in these neighborhoods is high. The educational levels of most individuals in these locations are low. Many of the people have a high diploma. Most households in the neighborhoods are headed by single mothers. The single mother households in the neighborhoods are higher than those in Washington by 20% (Aikman, 2014) . As a result, the crime rates have increased in these neighborhoods due to the high poverty and unemployment levels. 

Literature Review 

Gentrification is a current issue, and this has prompted researchers to analyze its effects in the developed cities and the neighboring areas. Gentrification is the process of developing cities through rebuilding offices and residential areas. It has an influence on the crime rates in the neighboring locations of the gentrifying cities. The crime rates of the neighboring areas increase due to a rise in the poverty levels. The crime rates increase in the short-term but decrease in the long-run. Researchers do not offer conclusive evidence on the effects of gentrification on the crime levels in the neighboring locations of developed cities. The aim of carrying out the literature review is to assess the researchers’ contributions to analyzing the effects of gentrification on crime rates. The literature review contains articles from 2010 to date. The aim of using the peer-reviewed articles is to get current insights of the researchers on the consequences of gentrification on neighborhood crime rates. 

Gentrification has a negative influence on neighborhood crime rates. It increases the poverty levels and costs of living in the surrounding areas of the gentrified cities (Boggess & Hipp, 2016) . Aikman (2014) and Lawrence (2013) claim that gentrification increases the criminal activities in the neighboring areas due to the rise in expenses. Lawrence (2013) used the Granger-casualty test to examine the relationship between gentrification and crime. This study will use the Granger-casualty test to establish the effects of gentrification on the crime rates in the neighborhoods of Washington. Aikman (2014) used quantitative methods to establish the consequences of gentrification in criminal activities of five states in the USA. David, Palmer, and Pathak (2017) claim that crime rates increase but the regulations of rent can reduce the criminal activities. The authors state that a reduction in public housing decreases the costs of living. Residents in the neighborhoods can afford the houses, and it discourages them from engaging in crime. Kirk and Laub (2010) state that gentrification reduces personal crime but increases property crime. Lee (2010) and Papachristos et al. (2011) supports the argument on the increase in property crime after the development of the towns. The personal crimes reduce slightly but the overall criminal activities still increase. 

On the contrary, Noonan (2017) states that gentrification increases violent crimes in the metropolitan areas. He uses qualitative techniques to assess the information on the effects of gentrification in the neighborhoods. Alternatively, Anderson (2016) claims that gentrification increases safety in the neighborhoods. Investors make investments in the neighborhoods, which creates job opportunities for the residents. Smith (2014) attests that gentrification increases the rates of homicides in the surrounding locations of the developed cities. The neighborhoods become insecure due to the reduction in employment because they depend on the competitive opportunities in the city. 

The studies have provided information on the effects of gentrification on the crime rates of neighborhoods. The articles provide facts on the consequences of gentrification. The researchers do not support their facts with empirical data. The researchers could have used various tests to establish the effects of gentrification on crime in the neighboring areas rather than utilizing qualitative and quantitative methods. The knowledge of the effects of gentrification is important in the field of criminology. 

Theoretical Review 

Social Disorganization Theory 

The social disorganization theory describes the connection between ecological aspects of a community and the crime levels. The environment of gentrifying neighborhoods is characterized by instability, population growth, high unemployment, and a rise in the cost of living. People are shaped by the environmental factors of their locations (Walker & Zawisza, 2014). The behaviors of the residents are influenced by the challenges that affect their locations. The areas should introduce informal activities for the residents rather than delegating the police to establish security. The social networks in the neighborhoods are destabilized and they increase instability. They also reduce the informal activities which increase the crime rates. High instability and low informal actions influence the behaviors of the individuals in the surrounding areas of developed cities. 

Location Theory 

The location theory is used in this paper to assess the general consequences of gentrification on the crime rates. The location theory holds that cities that have not been restructured have high crime rates and low-income levels (Lawrence, 2013) . The neighboring areas have low crime rates and high-income levels. The residents with high incomes will occupy the city to reduce their transport and housing expenses. As a result, the crime rates will reduce in the developed city and rise in the surrounding areas. The theory supports the ideas of increased crime rates in the neighborhood of Washington. 

Methodology 

Data 

The paper retrieves data from the MPD (Metropolitan Police Department). The state of Washington has various sections which include police service areas, quadrants, police districts, and wards. Wards, quadrants, and police districts occupy a large area which makes it difficult to use their data in assessing the effects of gentrification in the neighborhood. The data is not adequate to produce significant outcomes. The eight wards and six years (2012-2017) will give 48 observations (Metropolitan Police Department, 2018) . The lagging factors can reduce the observations due to the application of the Granger-causality examination. Police service areas are used in this research to establish the differences in the neighborhoods. Gentrification is a continuous activity. The data for six years is not adequate to determine the effects of gentrifying the city on the crime levels in the neighborhood. 

The information was also retrieved from NeighborhoodInfoDC. com. The website contains data the DHRS (Department of human resources services) and tax information or every resident of Washington. The property values represent the new residents and the TANF (Temporary Assistance for Needy Families) values demonstrate the displaced individuals (NeighborhoodInfoDC.com, 2018) . TANF indicates that residents are receiving assistance from the government on house expenses. The research work has a limited scope, and this limits the assessment of the positive effects of gentrification on the crime levels. The analysis of the positive influence of gentrification on crime is important with regard to the location theory. 

Empirical Model 

The Granger-causality test was used to establish the relationship and effects of the variables. The independent variable is housing and the dependent variable is crime. Two tests were carried out separately to examine the dependent and independent variable. An OLS (ordinary least square) regression analysis was carried out to establish the relationship between housing and crime. The mean of the criminal activities that will take place in the surrounding areas is represented by CRIME. The median for housing prices for every individual in the police service area is represented by the word HOUSE. The term TANF represents the mean of all the citizens receiving subsidies from the government. 

The equation for the dependent variable is expressed as follows: 

CRIME = ß 0 + ß 1 CRIME t-1 

The equation for the independent variable is expressed as follows: 

CRIME = ß 0 + ß 1 CRIME t-1 

A Wald examination was carried out on the two regressions. The equation for the dependent variable is analyzed using the restricted model. Subsequently, the equation for the independent variable was tested using the unrestricted method. The F-test was used to establish the omission of the equation of the independent variable in the restricted method. F-Statistic was used to predict the crime rates in the neighboring areas of Washington. The equation for the F-Statistic is expressed as follows: 

F = (ESSR R – ESSU U )/ (DFR R – DFU U ) 

ESSU U /DFU R 

ESS represents the error of sum squares 

DFR represents the degree of freedom for the restricted model 

DFU represents the degree of freedom for the unrestricted model 

In case the F-Statistic is significant, then housing will cause an increase in crime in the surrounding areas of the city. 

The F-test is carried out for a second time to establish whether crime causes an increase in housing. The test was similar to the first Granger-causality examination but the equation as different. 

HOUSE = ß 0 + ß 1 CRIME + ß 2 HOUSE t-1 

HOUSE = ß 0 + ß 1 HOUSE t-1 

The F-Statistic is used to establish the effects of the dependent variable on the independent variable. The hypothesis of the study is house will cause the crime to fall in the city. This is supported by the location theory. However, a decrease in crime in the city will cause an increase in criminal activities in the surrounding areas. The overall test was then carried out using TANF as the factor. 

Results and Discussions 

The variables are presented separately to simplify the Granger-causality examinations. The results for the tests include those for housing and TANF. The two tests indicate the process of gentrification. Housing factor indicates the residents moving in the city. The TANF factor indicates the residents moving out of the city. The two regressions for this study include an influence of housing on crime and the effects of TANF on crime. The aim of analyzing the regressions was to assess the coefficients and effects. Table 1 (see Appendix One) illustrates that housing has a significant level of 0.01and a coefficient of -0.287. However, Table 2 (see Appendix Two) indicates that TANF has a significant level of 0.01 and a coefficient of 0.150. The aim of carrying out the regressions separately is because they have a high collinearity. The results of the study could be ineffective in case TANF and housing were used in the same regression equation. 

Table 3 (see Appendix Three) indicates the Granger causality test assessed the influence of crime on housing. The dependent and independent variable were regressed on crime using the unrestricted method. CRIME t-1 was regressed on crime in the restricted method. The F-value was 39.94 in the unrestricted method which is statistically related to the standard level of 0.01. HOUSE t-1 was regressed on house using the restricted method. The F- value is 102.68 which is statistically related to the significant range of 0.01. Basing on the results, crime has significant effects on housing. Alternatively, housing has significant effects on crime. 

Table 4 (see Appendix Four) illustrates the results of assessing the relationship between CRIME and TANF. The unrestricted method was used to express CRIME t-1 and TANF on the dependent variable (crime). TANF was excluded in the unrestricted method. Inversely, CRIME was excluded from the restricted method. Thus, CRIME t-1 was regressed on crime. The outcome of the F-statistic is 36.6 which is related to the significant range of 0.01. The results indicate that TANF has a significant relationship with CRIME. Also, CRIME has a significant effect on TAFN. 

Findings 

Basing on the results of the study of the effects of gentrification on the neighborhood of the city, the following findings are important to note: 

There is a significant relationship between housing and crime. An increase in new houses reduces crime in the city. The crime rates increase in the neighborhoods of the city. 

A reduction in crime rates does not guarantee an increase in housing. This indicates that there is an inverse relationship of crime on housing. 

Limitations of the Study 

The researcher cannot make conclusions n TANF and CRIME. These variables are statistically related. The results indicate that TAFN has a greater influence on CRIME. Secondly, there as limited access to earlier data on housing, TAFN, and police service areas. The whole process of gentrification in Washington was not examined in this study. Gentrification is a continuous process; it began in Washington since the 1990s. The results of the study cannot be reliable because the researcher omitted data on gentrification for almost two decades. The outcomes can meet the hypothetical expectations after all the data of the gentrification process is included. The study does not include the positive effects of gentrification on the crime rates in the neighboring areas. There is a need for further analysis to include the data for the gentrification process in Washington. The researchers should get access to all the data from the metropolitan police department. Researchers should take into account the positive influences of gentrification n the crime levels in the surrounding areas. 

Delimitations of the Study 

The results of this study support the concepts of social disorganization and location theories. The research gives proof that gentrification affects crime in the neighboring areas of developed cities. The scope of this study is wide because the data for all the eight wards for the years 2012 to 2017 were included. The sources of this study are peer-reviewed articles and journal which enhance the credibility of the study. All the sources give information on the effects of gentrification on crime levels. The research can be important in guiding cities in making plans during the gentrification process. The urban planners can use the information on the effects of gentrification in the crime rates of the neighborhoods and create strategies to mitigate these challenges. This study is useful to criminologists and police. It will assist in identifying the areas that have high crime rates. The can concentrate on areas with high crime rates. The research will assist other scholars who would like to carry out an analysis of the consequences of gentrification on crime levels in the metropolitan areas. 

Conclusions 

The results of the study clearly indicate that the effects of gentrification on crime in the surrounding areas of the city. The two regressions illustrate an increase in the prices of the houses reduces the number of poor people in the city. As a result, the crime rates in the city reduce but increase in the neighboring areas. The aim of this research paper was to identify the effects of gentrification on the neighborhoods of the city. The two tests produced similar results. This is an indication that the independent and dependent variables have a significant relationship. Housing and crime influence each other. The aim of the F-test is to analyze the difference between the two regressions. The differences in the F-value are not an indication that the variables have a significant influence on each other. 

This information can be used to make plans for a city. The government should introduce informal activities to the residents in the neighborhoods of Washington to reduce the crime rates. The police operations do not have a significant influence on decreasing the crime levels. The crime levels in the neighboring areas vary according to the number of displaced individuals. There is a need for further research in this area to identify the effects of gentrification in the neighboring areas. Researchers need to improve the credibility of the results by carrying including all the data of the gentrification process. 

References 

Aikman, M. (2014). Gentrification Effects on Crime Rates .  Urban Economics , 1-7. 

Aliprantis, D., & Hartley, D. (2015). Blowing it up and knocking it down: The local and city-wide effects of demolishing high concentration public housing on crime.  Journal of Urban Economics ,  88 , 67-81. 

Anderson, S. (2016). Gentrification and Chicago. ESSAI , 14 (1), 9. 

Barton, M. S. (2016). Gentrification and violent crime in New York City. Crime & Delinquency , 62 (9), 1180-1202. 

Boggess, L. N., & Hipp, J. R. (2016). The spatial dimensions of gentrification and the consequences for neighborhood crime. Justice Quarterly , 33 (4), 584-613. 

David, H., Palmer, C. J., & Pathak, P. A. (2017).  Gentrification and the Amenity Value of Crime Reductions: Evidence from Rent Deregulation  (No. w23914). National Bureau of Economic Research. 

Kirk, D. S., & Laub, J. H. (2010). Neighborhood change and crime in the modern metropolis. Crime and Justice , 39 (1), 441-502. 

Lawrence, W. (2013). Displacement in DC: A Case Study of Gentrification and Granger-Causality in Our Nation's Capitol. 

Lee, Y. Y. (2010). Gentrification and crime: Identification using the 1994 Northridge earthquake in Los Angeles. Journal of Urban Affairs , 32 (5), 549-577. 

Metropolitan Police Department. (2018). Research and Analysis Branch. Updated October 23, 2012.NBC4 Washington. (December 26, 2011). D.C. 

NeighborhoodInfoDC.com. (2011). Retrieved from http://www neighborhoodinfodc.org/psa/psa.html. 

Noonan, G. A. (2017). A spatial analysis of the relationship between violent neighborhood crime rates and alternative gentrification indicators in Louisville, KY (2010-2016). 

Papachristos, A. V., Smith, C. M., Scherer, M. L., & Fugiero, M. A. (2011). More coffee, less crime? The relationship between gentrification and neighborhood crime rates in Chicago, 1991 to 2005. City & Community , 10 (3), 215-240. 

Smith, C. M. (2014). The influence of gentrification on gang homicides in Chicago neighborhoods, 1994 to 2005.  Crime & Delinquency ,  60 (4), 569-591. 

Walker, J. T., & Zawisza, T. T. (2014). Social disorganization theory.  The Encyclopedia of Theoretical Criminology , 1-9. 

APPENDICES 

Appendix One 

Table 1: Housing Regression 

Variable		Explanation	Coefficient	Significance
Dependent variable
	Crime	No. of crimes in a given year
		Median house sale for a given year	-.287	-.2900(***)
Independent variable  Adjusted R 2
	Housing	.030
			Sample	241

Appendix Two 

Table Two: TANF Regression 

Variable		Explanation	Coefficient	Significance
Dependent variable
	Crime	No. of crimes in a given year
		Median house sale for a given year	.149	.6.620(***)
Independent variable  Adjusted R 2
	TANF	.143
			Sample	255

Appendix Three 

Table Three: Granger causality test on housing 

Variable	Unrestricted Model	Restricted Model
House	- .008 (-.395)
Crime t-1	.969 (63.456)***	.947 (70.887)***
ESS	1632379	1953520
Sample	200	255
F-test	38.89**
Crime	- .011 (-1.245)
House t-1	.959 (63.395)***	.958 (54.566)***
ESS	597865	897546
Sample	198	200
F-test	102.68***

Appendix Four 

Table Four: Granger causality test for TANF 

Variable	Unrestricted Model	Restricted Model
TANF	.011 (2.121)
Crime t-1	.969 (63.456)***	.947 (70.887)***
ESS	1632379	1953520
Sample	219	255
F-test	36.59**
Crime	-.042 (-2.459)
TANF t-1	.998 (143.350)***	.992 (159.898)***
ESS	2065459	2124658
Sample	205	210
F-test	6.29***