This document provides a summary of research on the relationship between immigration and crime. Several empirical studies are reviewed that use different data and methodologies to examine this issue. The studies generally find that while immigration may be positively correlated with some types of property crime, this correlation does not demonstrate causation. Legal status and access to the labor market are found to reduce criminal behavior among immigrants. Theoretical models of criminal behavior are also discussed, explaining how factors like probability of arrest, fines, and opportunities affect cost-benefit analyses of committing crimes.
1) The document reviews several studies that examine the relationship between immigration and crime in the United States and other countries.
2) The studies generally find that immigration does not have a significant relationship with increased crime rates, and may even contribute to decreased crime in some cases.
3) However, some studies found that increased immigration can be correlated with increased perceptions of crime and safety issues among native populations, even if actual crime rates remain unchanged or decrease.
The FBI released 2009 crime statistics showing that both violent and property crimes declined compared to 2008. Violent crimes decreased by 5.3% and property crimes decreased by 4.6%. Specifically, the rates of murder, robbery, aggravated assault, and rape all declined while rates of motor vehicle theft, larceny-theft, and burglary also decreased. Overall arrests decreased as well, with violent crime arrests declining by 191.2 per 100,000 people and property crime arrests declining by 571.1 per 100,000.
The ICC prosecutor is monitoring the situation in Kenya following the 2007 post-election violence where over 1,500 people were killed. In 2008, Kenya's main opposition party submitted communications to the ICC alleging crimes against humanity by the government. The ICC is analyzing information to determine if an investigation is warranted. Kenya has been working to establish a domestic tribunal to address the crimes.
The increasing juridification and judicialization of societies make understanding, measuring, preventing and combating the corruption plague much more complex since white-collar criminals and their political and judicial cronies continuously act to circumvent the rule of law. Therefore it is important to contextualize the major parameters involved in such dynamics in order to allow among other things quantitative modeling of corruption and related causal variables.
This document provides an update to a 1993 report on the role of information and communications technologies in hate crimes. It discusses hate crime laws and data in the U.S., examines how modern technologies like social media, bots, and video games may be used to advocate for hate crimes, and considers related First Amendment issues. While some criminal groups use online media, the document finds no evidence that electronic communications cause hate crimes or that hate criminals use online media more than other criminals. It recommends continued monitoring of speech that solicits, conspires or aids hate crimes, along with reform of Section 230 internet liability protections.
Abstract: The menace of corruption in Nigeria is very pervasive with global implications. So pervasive is corruption in Nigeria that almost every aspect of National life is affected one way or the other (Matthew et al 2013). According to Woodward 2015, psychosocial approach looks at individuals in the context of the combined influence that psychological factors and the surrounding social environment have on their physical and mental wellness and their ability to function. This approach is used in broad range of helping professions in health and social care settings as well as by medical and social science researchers. It is however difficult to provide the exact date that corruption became a subject of national discourse in Nigeria (Matthew et al 2013). The age of corruption in Nigeria however, has affected the socio-psychology of the citizenry as there have been little or no effective measures put in place to curb the menace of corruption. It is also undisputedly true that corruption in the Nigerian society has eaten deep into the law enforcement agencies, political parties, political leaders, judicial system, government and private ministries and parastatals, law makers, etc., and above all, the psycho-social standing of the citizenry is greatly affected. Thus, curbing corruption in Nigeria may seem too daunting to dare but before proffering critical remedies/strategies/recommendations that will help tremendously in curbing corruption in Nigeria, a closer look at some two major factors that have been grossly infected by corruption will be considered. These two factors are carefully selected because the multiplier effects of corruption we see today in Nigeria find their roots in these two factors which are political corruption and judicial corruption. If corruption in these two institutions mentioned is curbed, then corruption in other aspects of life would have been greatly diminished and the slogan “change begins with me” would become more productive in the reduction of corruption as well as conscience upliftment and Nigeria would be in her way forward to a corrupt-free nation.
Keywords: corruption.
Title: TOWARDS CURBING CORRUPTION IN NIGERIAN SOCIETY
Author: NWUZOR, E. EZIAKU, ANYAOGU, BONIFACE E
International Journal of Recent Research in Social Sciences and Humanities (IJRRSSH),
ISSN 2349-7831,
Paper Publications
This document discusses the issue of illegal immigration in the United States and argues that the official census estimates greatly undercount the actual number of illegal immigrants. It claims the number of illegal immigrants may be over 20 million, more than double official estimates. It outlines several reasons for this undercount, including illegal immigrants avoiding the census. It also discusses the significant economic and fiscal impacts of illegal immigration, such as impacts on jobs, wages, tax revenues, and government spending.
1) The document reviews several studies that examine the relationship between immigration and crime in the United States and other countries.
2) The studies generally find that immigration does not have a significant relationship with increased crime rates, and may even contribute to decreased crime in some cases.
3) However, some studies found that increased immigration can be correlated with increased perceptions of crime and safety issues among native populations, even if actual crime rates remain unchanged or decrease.
The FBI released 2009 crime statistics showing that both violent and property crimes declined compared to 2008. Violent crimes decreased by 5.3% and property crimes decreased by 4.6%. Specifically, the rates of murder, robbery, aggravated assault, and rape all declined while rates of motor vehicle theft, larceny-theft, and burglary also decreased. Overall arrests decreased as well, with violent crime arrests declining by 191.2 per 100,000 people and property crime arrests declining by 571.1 per 100,000.
The ICC prosecutor is monitoring the situation in Kenya following the 2007 post-election violence where over 1,500 people were killed. In 2008, Kenya's main opposition party submitted communications to the ICC alleging crimes against humanity by the government. The ICC is analyzing information to determine if an investigation is warranted. Kenya has been working to establish a domestic tribunal to address the crimes.
The increasing juridification and judicialization of societies make understanding, measuring, preventing and combating the corruption plague much more complex since white-collar criminals and their political and judicial cronies continuously act to circumvent the rule of law. Therefore it is important to contextualize the major parameters involved in such dynamics in order to allow among other things quantitative modeling of corruption and related causal variables.
This document provides an update to a 1993 report on the role of information and communications technologies in hate crimes. It discusses hate crime laws and data in the U.S., examines how modern technologies like social media, bots, and video games may be used to advocate for hate crimes, and considers related First Amendment issues. While some criminal groups use online media, the document finds no evidence that electronic communications cause hate crimes or that hate criminals use online media more than other criminals. It recommends continued monitoring of speech that solicits, conspires or aids hate crimes, along with reform of Section 230 internet liability protections.
Abstract: The menace of corruption in Nigeria is very pervasive with global implications. So pervasive is corruption in Nigeria that almost every aspect of National life is affected one way or the other (Matthew et al 2013). According to Woodward 2015, psychosocial approach looks at individuals in the context of the combined influence that psychological factors and the surrounding social environment have on their physical and mental wellness and their ability to function. This approach is used in broad range of helping professions in health and social care settings as well as by medical and social science researchers. It is however difficult to provide the exact date that corruption became a subject of national discourse in Nigeria (Matthew et al 2013). The age of corruption in Nigeria however, has affected the socio-psychology of the citizenry as there have been little or no effective measures put in place to curb the menace of corruption. It is also undisputedly true that corruption in the Nigerian society has eaten deep into the law enforcement agencies, political parties, political leaders, judicial system, government and private ministries and parastatals, law makers, etc., and above all, the psycho-social standing of the citizenry is greatly affected. Thus, curbing corruption in Nigeria may seem too daunting to dare but before proffering critical remedies/strategies/recommendations that will help tremendously in curbing corruption in Nigeria, a closer look at some two major factors that have been grossly infected by corruption will be considered. These two factors are carefully selected because the multiplier effects of corruption we see today in Nigeria find their roots in these two factors which are political corruption and judicial corruption. If corruption in these two institutions mentioned is curbed, then corruption in other aspects of life would have been greatly diminished and the slogan “change begins with me” would become more productive in the reduction of corruption as well as conscience upliftment and Nigeria would be in her way forward to a corrupt-free nation.
Keywords: corruption.
Title: TOWARDS CURBING CORRUPTION IN NIGERIAN SOCIETY
Author: NWUZOR, E. EZIAKU, ANYAOGU, BONIFACE E
International Journal of Recent Research in Social Sciences and Humanities (IJRRSSH),
ISSN 2349-7831,
Paper Publications
This document discusses the issue of illegal immigration in the United States and argues that the official census estimates greatly undercount the actual number of illegal immigrants. It claims the number of illegal immigrants may be over 20 million, more than double official estimates. It outlines several reasons for this undercount, including illegal immigrants avoiding the census. It also discusses the significant economic and fiscal impacts of illegal immigration, such as impacts on jobs, wages, tax revenues, and government spending.
Vanessa Schoening February 13, 2014Module 2 .docxMARRY7
Vanessa Schoening February 13, 2014
Module 2 Revised
Annotated Bibliography Revised
1. Bureau of Justice Statistics (1999). Number of homicides and population for cities with estimated population 100,000 or more, from 1985-1997.
This paper begins by acknowledging that most studies carried out into crimes have focused on the features of the populations such as age, employment and average earnings. The writers hold that the fact that population density has not been studied much represents a knowledge gap. The paper tries to find out whether population density has a negative or positive correlation with crime. Through sifting through previous studies, the paper tries to find out the nature of crimes that are rampant in varying populations.
2. Christens, B., & Speer, P. W. (2006). Predicting violent crime using urban and suburban densities. Behavior and Social Issues, 14(2), 113-127.
Christens and Speer go through past research and statistics and try to show the varying nature of crimes in different settlements areas. Their main focus is on violent and crime and they try to show that densely populated areas are more likely to see violent crime due to increased chances of conflict. Their other argument is that low-income areas are likely to see more violent crime.
3. Duany, A., Plater-Zyberk, E., & Speck, J. (2000). Suburban nation: The rise of sprawl and thedecline of the American dream. New York: North Point Press.
The authors of this book are founders of a movement that calls for an improved planning of settlements to avoid further spread of sprawls or informal settlements. The book highlights the effects of these settlements including economic and social ills. The book gives real life examples of how unplanned and congested settlements coupled with low income can cause crime. The book also offers solutions on how to improve the planning of cities and suburbs
4. Fulton, W., Pendall, R., Nguyen, M. & Harrison, A. (2001). Who sprawls most? How growth patterns differ across the U.S. Washington, D.C.: The Brookings Institution.
This book takes a look at population densities in various cities across the US between 1982 and 1997. The book also acknowledges that as cities grow, they continue to add previously unused land into their metropolitan territory. The book tries to compare the rate at which this land is being urbanized against the population growth. The book holds that cities in the west of the US have highly populated metropolitans and goes ahead to outline some of the challenges that may pose.
5. LaFree, G., Bursik, R.J., Short, J. & Taylor, R.B. (2000). The nature of crime: Continuity and change. Criminal Justice 2000, 1, 261-308.
The authors of this article begin by pointing out that the nature of crimes and reaction of societies towards various crimes do not remain constant. The writers hold that changing legislations and people’s attitudes can affect the nature of crimes. The availability of IT for instance creates a new ...
The document discusses the crime rate in Los Angeles and how it has fallen for the 10th straight year, making LA the safest big city in America. Violent crime decreased by 8.3% and murders remained low, with just 298 murders in 2012 compared to 1,092 murders 20 years ago. The decrease is attributed to innovative policing strategies focused on dismantling gangs, which were a major factor in LA's high crime rates in previous decades but have decreased significantly due to these policing efforts.
Citizenship Status and Arrest Patterns for Violentand NarcotVinaOconner450
Citizenship Status and Arrest Patterns for Violent
and Narcotic-Related Offenses in Federal Judicial
Districts along the U.S./Mexico Border
Deborah Sibila1 & Wendi Pollock2 & Scott Menard3
Received: 8 September 2016 /Accepted: 30 October 2016 /
Published online: 10 November 2016
# Southern Criminal Justice Association 2016
Abstract Media reports routinely reference the drug-related violence in Mexico,
linking crime in communities along the Southwest U.S. Border to illegal immigrants.
The primary purpose of the current research is to examine whether the media assertions
can be supported. Logistic regression models were run to determine the impact of
citizenship on the likelihood of disproportionate arrest for federal drug and violent
crimes, along the U.S./Mexico border. In arrests for homicide, assault, robbery, and
weapons offenses, U.S. citizens were disproportionately more likely than non-citizens
to be arrested. The only federal crime where non-citizens were disproportionately more
likely to be arrested than were U.S. citizens was for marijuana offenses. Results of the
current study challenge the myth of the criminal immigrant.
Keywords Citizenship . Arrest . Criminal immigrant . Gender
The myth of the criminal immigrant is perhaps one of the single most controversial
factors contributing to America’s present day anti-immigrant fervor. In their book, The
Am J Crim Just (2017) 42:469–488
DOI 10.1007/s12103-016-9375-1
* Wendi Pollock
[email protected]
Deborah Sibila
[email protected]
Scott Menard
[email protected]
1 Department of Government, Stephen F. Austin State University, Box 13045 SFA Station,
Nacogdoches, TX 75962, USA
2 Department of Social Sciences, Texas A&M University, 6300 Ocean Drive, Corpus Christi,
TX 78412, USA
3 Institute of Behavioral Science, University of Colorado, Boulder, USA
http://crossmark.crossref.org/dialog/?doi=10.1007/s12103-016-9375-1&domain=pdf
Immigration Time Bomb, authors Richard D. Lamm and Gary Imhoff contend that the
issue of immigration and crime is a critically divisive topic easily subject to misinter-
pretation (1985, p. 21). The belief that immigrants are more crime-prone than native-
born is not a twentieth century development. Debates on this controversy date back
more than 100 years (Hagan & Palloni, 1998; Martinez & Lee, 2000). Hagan and
Pallon believed that the nexus between immigration and crime is so misleading that it
constitutes a mythology (1999, p. 630). In a special report for the Immigration Policy
Center, professors Ruben Rumbaut and Walter Ewing wrote B[The] misperception that
the foreign-born, especially illegal, immigrants are responsible for higher crime rates is
deeply rooted in American public opinion and sustained by media anecdote and
popular myth^ (2007, p. 3). Lee (2013) similarly argues that immigrants have a long
history of serving as scapegoats for a vast array of America’s societal problems
including crime.
Public opinion surveys suggest that a significant nu ...
Violent Crimes Report for Continental U.S. (1980 - 2009)Afroxl87
The document discusses violent crime rates in the continental United States from 1980 to 2009 based on FBI Uniform Crime Reporting data. It finds that violent crime peaked in the early 1990s, with 1992 having the most reports of violent crime for the decade. The passage of the 1994 Violent Crime Control and Law Enforcement Act, which provided billions for law enforcement and crime prevention, corresponded with declining violent crime rates throughout the remainder of the 1990s. Rates fluctuated by state and decade, influenced by factors like the introduction of crack cocaine in the 1980s and economic conditions. Overall, reports of violent crime increased dramatically from the 1960s through the early 1990s before declining for the rest of the analyzed period.
This document analyzes the impact of organized crime infiltration of local governments in Italy. Using a national law that allowed dissolution of infiltrated municipal governments, the authors conduct a differences-in-differences analysis comparing public spending, taxes, and elections before and after infiltration. They find that infiltrated governments spend more on construction and waste but less on transportation, police, and collect fewer waste taxes. Regression discontinuity analysis also links right-wing election victories to higher infiltration rates. The document aims to shed light on the less violent but still impactful influence of organized crime.
Residential differentials in incidence and fear of crime perception in ibadanAlexander Decker
This document summarizes research on differences in crime incidence and fear of crime across residential areas in Ibadan, Nigeria. It finds that households in high and medium density residential areas experience higher average crime rates than those in low density areas. However, residents in low density areas report the highest levels of fear of crime, followed by medium and then high density areas. The pattern of fear does not match the actual pattern of crime rates. The study uses surveys of residents across different neighborhoods to examine both experienced crime and fear of crime. It aims to establish a link between the two and understand how to better address residents' concerns.
Do you think that Canada’s perceived liberal approach to both immigr.pdfforladies
Do you think that Canada’s perceived liberal approach to both immigration and refugees poses a
security threat to the United States?
Solution
Ans;- Yes, Canada’s perceived liberal approach to both immigration and refugees poses a
security threat to the United States,
mmigration has been connected with terrorism, immigration has also been related to increased
criminality, resulting in the perception that immigration is a threat to public security. The issue
of whether or not immigration actually results in increased crime rates is, again, an issue of
perception versus reality. While the public has become increasingly concerned about high crime
rates intensified by immigration and the threat that immigrants pose to public order, these
concerns are empirically unsound (Wang 2012:743). Contrary to popular opinion, several studies
on a number of states have found no strong correlation between immigration and criminality.
It cannot be denied that in some states, there has been a connection between increased
immigration flows and increased crime rates. There is, indeed, a trend showing that cities and
countries that have high crime rates tend to have a higher immigrant population. For instance, a
study found that in 2001, “the proportion of the prison population born abroad in Spain was
twenty five times higher than the proportion of immigrants in the population” (Westbrook
2010:101). However, as Westbrook (2010) insightfully argues, this has much more to do with
demographic factors than it does with simply having an immigrant status (101). In the case of
Spain, the majority of immigrants are those who have the highest incidence of criminal
behaviour: single men aged 18 to 35 (Westbrook 2010:101). Thus, in examining the relationship
between immigration and criminality, demographic variables must be taken into account.
There is an abundance of evidence which demonstrates that the correlation between immigration
and criminality is very weak or non-existent. A study of three American neighbourhoods
concludes that in general, immigration does not lead to increased levels of homicide among
Latinos and African Americans (Lee et al. 2001:559). Similarly, in another study, Butcher and
Piehl (1998) conclude that the flow of migration has no effect on a city’s crime rate (457). Bell et
al. (2010) investigate the relationship between immigration and crime during two particular
periods of large migration flows in the United Kingdom: during the wave of asylum seekers in
the 1990s and early 2000s, and the inflow of economic migrants from EU accession countries
beginning in 2004 (1). The study reports that neither wave impacted rates of violent crime, and
that immigrant arrest rates were no higher than native arrest rates (Bell et al. 2010:17). Evidently,
while widespread public opinion holds that immigration is a threat to public security, it is a
constructed threat, not founded upon empirical facts.
The terrorist attacks of September 2001 (9/11) in the United St.
Spatial analysis of Mexican Homicides and Proximity to the US BorderKezia Dinelt
This document describes a spatial analysis of homicides related to drug trafficking organizations (DTOs) in Mexico in 2010 in relation to proximity to the U.S. border. The author hypothesizes that DTO homicides will be more prevalent closer to border crossings due to drug trafficking between Mexico and the U.S. The analysis uses GIS shapefiles including Mexican municipalities, DTO homicides in 2010, U.S. border cities, and population/inequality data. Regression is performed to analyze the relationship between DTO homicides and distance from border points while controlling for population density and inequality.
Crime is considered as a serious issue. It makes people worried on their own safety especially when the crime rate is increasing. The study generated the trend analysis and forecasted data of the recorded crimes in Ozamiz City using autoregressive integrated moving average through the GRTEL software. Results showed that majority of the crimes recorded decreased from 2018 to 2021. These include crimes against person, crimes against property, crimes against personal liberty and security and violations against special laws particularly the comprehensive dangerous drugs Act of 2002. However, the violators who resisted persons in authority drastically increased. On the other hand, forecasted data revealed that most of the crimes will most likely increase in occurrence in the year 2022 and will be stabilized in the remaining years. The data gathered from this study can be used by the different law enforcement agencies in planning and creating intervention strategies towards mitigating the occurrence of crimes in Ozamiz City.
Since coming into office two years ago, Chinese President Xi Jinping has carried out a sweeping, highly publicized anticorruption campaign. Skeptics are debating whether the campaign is biased towards Mr. Xi’s rivals, and even possibly related to the current economic slowdown. What is less debated is the next stage of Mr. Xi’s anti-corruption strategy, which is going to alter the legal statutes. Amendment IX, proposed in October 2014, includes heavier penalties, but two important tools in the fight of corruption – one-sided leniency and asymmetric punishment – became more limited and discretional. We argue that studying a 1997 reform and its effects can shed some light onto why the Chinese leadership seems dissatisfied with the current legislation and the likely effects of the proposed changes.
Chapter ONE INTRODUCTIONThe Pervasiveness (and Limitations) of Measure.docxnoel23456789
Chapter ONE
INTRODUCTION
The Pervasiveness (and Limitations) of Measurement
[Numbers] can bamboozle and not enlighten, terrorize not guide, and all too easily end up abused and distrusted. Potent but shifty, the role of numbers is frighteningly ambiguous.
—Blastland & Dilnot (2009, p. xi)
On September 23, 1999, NASA fired rockets that were intended to put its Mars Climate spacecraft into a stable, low-altitude orbit over the planet. But after the rockets were fired, the spacecraft disappeared—scientists speculated that it had either crashed on the Martian surface or had escaped the planet completely. This disaster was a result of confusion over measurement units—the manufacturer of the spacecraft had specified the rocket thrust in pounds, whereas NASA assumed that the thrust had been specified in metric system newtons (Browne, 2001).
Measurement is obviously very important in the physical sciences; it is equally important in the social sciences, including the discipline of criminology. Criminologists, policy makers, and the general public are concerned about the levels of crime in society, and the media frequently report on the extent and nature of crime. These media reports typically rely on official data and victimization studies and often focus on whether crime is increasing or decreasing.
Both official crime and victimization data indicated that property and violent crime in the United States were in a state of relatively steady decline from the early 1990s to 2000. But in late May 2001, the release of Federal Bureau of Investigation (FBI) official crime data, widely publicized in the media, indicated that crime was no longer declining. This prompted newspaper headlines such as “Decade-Long Crime Drop Ends†(Lichtblau, 2001a) and led commentators such as James Alan Fox, dean of the College of Criminal Justice at Northeastern University, to assert, “It seems that the crime drop is officially over. … We have finally squeezed all the air out of the balloon†(as quoted in Butterfield, 2001a). However, some two weeks after the release of these official data, a report based on victimization data indicated that violent crime had decreased by 15% between 1999 and 2000, the largest one-year decrease since the federal government began collecting national victimization data in 1973 (Rennison, 2001). The release of these data prompted headlines such as “Crime Is: Up? Down? Who Knows?†(Lichtblau, 2001b) and led James Alan Fox to declare, “This is good news, but it’s not great news†(as quoted in Bendavid, 2001).
How do we reconcile the conflicting messages regarding crime trends from these two data sources? First, although most media sources commenting on the FBI data failed to mention this caveat, the official data report was in fact based on preliminary data: “[The report] does not contain official figures for crime rates in 2000†(Butterfield, 2001a). Second, and more important, the underlying reason for these differe.
This document provides a summary of predictive policing technologies and their implications. It discusses how policing has evolved from political to community-based models using broken windows theory. Two predictive policing software programs, PredPol and HunchLab, use data analysis to predict future crime locations. While these programs aim to efficiently allocate police resources, concerns include privacy violations, implementation costs, and potential biases. The document examines debates around predictive policing and big data collection in law enforcement.
Human rights oriented article that examines whether the transitional justice measures implemented in Serbia have had the desired effect of contributing to reconciliation after the Yugoslav conflict in the 1990s.
This study examines factors that impact crime rates across major U.S. cities using data from 251 cities. Ordinary least squares regression is used to analyze the relationship between crime rates and variables like ethnicity, income, gender, age, population density, and political affiliation. Preliminary results found higher crime rates were associated with higher percentages of the population aged 25-34, higher population density, and higher percentages of divorced individuals. Lower crime rates were linked to more vacant housing units, higher median household incomes, and cities that lean Democratic politically. The study aims to help understand why crime rates vary significantly between cities with seemingly similar populations.
Literature Review for third party crime reporting.pdfsdfghj21
This document summarizes literature on third-party crime reporting across cultures. It discusses how perceived social cohesion and trust in police affects crime reporting rates in individualistic vs. collectivistic cultures. In cultures with high social cohesion, third parties may be less likely to report crimes to avoid social consequences, while those in individualistic cultures may feel obligated to notify authorities. The document also examines how factors like gender, number of cosocialized siblings, and crime type influence willingness to report in different societies.
The criminal justice system aims to punish and prevent crime through departments like the police, Crown Prosecution Service, and judiciary. However, the system is criticized for being biased in how it defines crime and deviance as well as in how it disproportionately targets some ethnic groups. While necessary to maintain order, the system excludes many minority groups and solidifies distrust between these groups and institutions through its labeling and lack of addressing socioeconomic inequalities. Reforms have been introduced but fail to overcome issues of potential politicization or institutional discrimination.
ARTIFICIAL INTELLIGENCE MODELS FOR CRIME PREDICTION IN URBAN SPACESmlaij
This work presents research based on evidence with neural networks for the development of predictive
crime models, finding the data sets used are focused on historical crime data, crime classification, types of
theft at different scales of space and time, counting crime and conflict points in urban areas. Among some
results, 81% precision is observed in the prediction of the Neural Network algorithm and ranges in the
prediction of crime occurrence at a space-time point between 75% and 90% using LSTM (Long-ShortSpace-Time). It is also observed in this review, that in the field of justice, systems based on intelligent
technologies have been incorporated, to carry out activities such as legal advice, prediction and decisionmaking, national and international cooperation in the fight against crime, police and intelligence services,
control systems with facial recognition, search and processing of legal information, predictive
surveillance, the definition of criminal models under the criteria of criminal records, history of incidents in
different regions of the city, location of the police force, established businesses, etc., that is, they make
predictions in the urban context of public security and justice. Finally, the ethical considerations and
principles related to predictive developments based on artificial intelligence are presented, which seek to
guarantee aspects such as privacy, privacy and the impartiality of the algorithms, as well as avoid the
processing of data under biases or distinctions. Therefore, it is concluded that the scenario for the
development, research, and operation of predictive crime solutions with neural networks and artificial
intelligence in urban contexts, is viable and necessary in Mexico, representing an innovative and effective
alternative that contributes to the attention of insecurity, since according to the indices of intentional
homicides, the crime rates of organized crime and violence with firearms, according to statistics from
INEGI, the Global Peace Index and the Government of Mexico, remain in increase.
Artificial Intelligence Models for Crime Prediction in Urban Spacesmlaij
This work presents research based on evidence with neural networks for the development of predictive crime models, finding the data sets used are focused on historical crime data, crime classification, types of theft at different scales of space and time, counting crime and conflict points in urban areas. Among some results, 81% precision is observed in the prediction of the Neural Network algorithm and ranges in the prediction of crime occurrence at a space-time point between 75% and 90% using LSTM (Long-ShortSpace-Time). It is also observed in this review, that in the field of justice, systems based on intelligent technologies have been incorporated, to carry out activities such as legal advice, prediction and decisionmaking, national and international cooperation in the fight against crime, police and intelligence services, control systems with facial recognition, search and processing of legal information, predictive surveillance, the definition of criminal models under the criteria of criminal records, history of incidents in different regions of the city, location of the police force, established businesses, etc., that is, they make predictions in the urban context of public security and justice. Finally, the ethical considerations and principles related to predictive developments based on artificial intelligence are presented, which seek to guarantee aspects such as privacy, privacy and the impartiality of the algorithms, as well as avoid the processing of data under biases or distinctions. Therefore, it is concluded that the scenario for the development, research, and operation of predictive crime solutions with neural networks and artificial intelligence in urban contexts, is viable and necessary in Mexico, representing an innovative and effective alternative that contributes to the attention of insecurity, since according to the indices of intentional homicides, the crime rates of organized crime and violence with firearms, according to statistics from INEGI, the Global Peace Index and the Government of Mexico, remain in increase.
The Relationship Rural Development and CrimesAI Publications
This document summarizes research on the relationship between rural development and crimes. It discusses several theories from criminology that explore this relationship, such as social disorganization theory, strain theory, and subcultural theory. The document also presents eight hypotheses about the positive relationships between various factors like law enforcement, urbanization, demographic characteristics, and industries/enterprises with rural development and crimes. Finally, it reviews literature on this topic and discusses methods used in the research study, including a survey of 118 people to analyze the relationship between rural development and crimes.
This paper focuses on a cross-cultural comparative analysis between the characteristics of immigration policy in France and the United States. It shows how culture, religion and history influence immigration policy in both countries. It looks at current major issues and shifts in immigration policy in both countries and what measures governments in both countries are taking to address them.
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
Vanessa Schoening February 13, 2014Module 2 .docxMARRY7
Vanessa Schoening February 13, 2014
Module 2 Revised
Annotated Bibliography Revised
1. Bureau of Justice Statistics (1999). Number of homicides and population for cities with estimated population 100,000 or more, from 1985-1997.
This paper begins by acknowledging that most studies carried out into crimes have focused on the features of the populations such as age, employment and average earnings. The writers hold that the fact that population density has not been studied much represents a knowledge gap. The paper tries to find out whether population density has a negative or positive correlation with crime. Through sifting through previous studies, the paper tries to find out the nature of crimes that are rampant in varying populations.
2. Christens, B., & Speer, P. W. (2006). Predicting violent crime using urban and suburban densities. Behavior and Social Issues, 14(2), 113-127.
Christens and Speer go through past research and statistics and try to show the varying nature of crimes in different settlements areas. Their main focus is on violent and crime and they try to show that densely populated areas are more likely to see violent crime due to increased chances of conflict. Their other argument is that low-income areas are likely to see more violent crime.
3. Duany, A., Plater-Zyberk, E., & Speck, J. (2000). Suburban nation: The rise of sprawl and thedecline of the American dream. New York: North Point Press.
The authors of this book are founders of a movement that calls for an improved planning of settlements to avoid further spread of sprawls or informal settlements. The book highlights the effects of these settlements including economic and social ills. The book gives real life examples of how unplanned and congested settlements coupled with low income can cause crime. The book also offers solutions on how to improve the planning of cities and suburbs
4. Fulton, W., Pendall, R., Nguyen, M. & Harrison, A. (2001). Who sprawls most? How growth patterns differ across the U.S. Washington, D.C.: The Brookings Institution.
This book takes a look at population densities in various cities across the US between 1982 and 1997. The book also acknowledges that as cities grow, they continue to add previously unused land into their metropolitan territory. The book tries to compare the rate at which this land is being urbanized against the population growth. The book holds that cities in the west of the US have highly populated metropolitans and goes ahead to outline some of the challenges that may pose.
5. LaFree, G., Bursik, R.J., Short, J. & Taylor, R.B. (2000). The nature of crime: Continuity and change. Criminal Justice 2000, 1, 261-308.
The authors of this article begin by pointing out that the nature of crimes and reaction of societies towards various crimes do not remain constant. The writers hold that changing legislations and people’s attitudes can affect the nature of crimes. The availability of IT for instance creates a new ...
The document discusses the crime rate in Los Angeles and how it has fallen for the 10th straight year, making LA the safest big city in America. Violent crime decreased by 8.3% and murders remained low, with just 298 murders in 2012 compared to 1,092 murders 20 years ago. The decrease is attributed to innovative policing strategies focused on dismantling gangs, which were a major factor in LA's high crime rates in previous decades but have decreased significantly due to these policing efforts.
Citizenship Status and Arrest Patterns for Violentand NarcotVinaOconner450
Citizenship Status and Arrest Patterns for Violent
and Narcotic-Related Offenses in Federal Judicial
Districts along the U.S./Mexico Border
Deborah Sibila1 & Wendi Pollock2 & Scott Menard3
Received: 8 September 2016 /Accepted: 30 October 2016 /
Published online: 10 November 2016
# Southern Criminal Justice Association 2016
Abstract Media reports routinely reference the drug-related violence in Mexico,
linking crime in communities along the Southwest U.S. Border to illegal immigrants.
The primary purpose of the current research is to examine whether the media assertions
can be supported. Logistic regression models were run to determine the impact of
citizenship on the likelihood of disproportionate arrest for federal drug and violent
crimes, along the U.S./Mexico border. In arrests for homicide, assault, robbery, and
weapons offenses, U.S. citizens were disproportionately more likely than non-citizens
to be arrested. The only federal crime where non-citizens were disproportionately more
likely to be arrested than were U.S. citizens was for marijuana offenses. Results of the
current study challenge the myth of the criminal immigrant.
Keywords Citizenship . Arrest . Criminal immigrant . Gender
The myth of the criminal immigrant is perhaps one of the single most controversial
factors contributing to America’s present day anti-immigrant fervor. In their book, The
Am J Crim Just (2017) 42:469–488
DOI 10.1007/s12103-016-9375-1
* Wendi Pollock
[email protected]
Deborah Sibila
[email protected]
Scott Menard
[email protected]
1 Department of Government, Stephen F. Austin State University, Box 13045 SFA Station,
Nacogdoches, TX 75962, USA
2 Department of Social Sciences, Texas A&M University, 6300 Ocean Drive, Corpus Christi,
TX 78412, USA
3 Institute of Behavioral Science, University of Colorado, Boulder, USA
http://crossmark.crossref.org/dialog/?doi=10.1007/s12103-016-9375-1&domain=pdf
Immigration Time Bomb, authors Richard D. Lamm and Gary Imhoff contend that the
issue of immigration and crime is a critically divisive topic easily subject to misinter-
pretation (1985, p. 21). The belief that immigrants are more crime-prone than native-
born is not a twentieth century development. Debates on this controversy date back
more than 100 years (Hagan & Palloni, 1998; Martinez & Lee, 2000). Hagan and
Pallon believed that the nexus between immigration and crime is so misleading that it
constitutes a mythology (1999, p. 630). In a special report for the Immigration Policy
Center, professors Ruben Rumbaut and Walter Ewing wrote B[The] misperception that
the foreign-born, especially illegal, immigrants are responsible for higher crime rates is
deeply rooted in American public opinion and sustained by media anecdote and
popular myth^ (2007, p. 3). Lee (2013) similarly argues that immigrants have a long
history of serving as scapegoats for a vast array of America’s societal problems
including crime.
Public opinion surveys suggest that a significant nu ...
Violent Crimes Report for Continental U.S. (1980 - 2009)Afroxl87
The document discusses violent crime rates in the continental United States from 1980 to 2009 based on FBI Uniform Crime Reporting data. It finds that violent crime peaked in the early 1990s, with 1992 having the most reports of violent crime for the decade. The passage of the 1994 Violent Crime Control and Law Enforcement Act, which provided billions for law enforcement and crime prevention, corresponded with declining violent crime rates throughout the remainder of the 1990s. Rates fluctuated by state and decade, influenced by factors like the introduction of crack cocaine in the 1980s and economic conditions. Overall, reports of violent crime increased dramatically from the 1960s through the early 1990s before declining for the rest of the analyzed period.
This document analyzes the impact of organized crime infiltration of local governments in Italy. Using a national law that allowed dissolution of infiltrated municipal governments, the authors conduct a differences-in-differences analysis comparing public spending, taxes, and elections before and after infiltration. They find that infiltrated governments spend more on construction and waste but less on transportation, police, and collect fewer waste taxes. Regression discontinuity analysis also links right-wing election victories to higher infiltration rates. The document aims to shed light on the less violent but still impactful influence of organized crime.
Residential differentials in incidence and fear of crime perception in ibadanAlexander Decker
This document summarizes research on differences in crime incidence and fear of crime across residential areas in Ibadan, Nigeria. It finds that households in high and medium density residential areas experience higher average crime rates than those in low density areas. However, residents in low density areas report the highest levels of fear of crime, followed by medium and then high density areas. The pattern of fear does not match the actual pattern of crime rates. The study uses surveys of residents across different neighborhoods to examine both experienced crime and fear of crime. It aims to establish a link between the two and understand how to better address residents' concerns.
Do you think that Canada’s perceived liberal approach to both immigr.pdfforladies
Do you think that Canada’s perceived liberal approach to both immigration and refugees poses a
security threat to the United States?
Solution
Ans;- Yes, Canada’s perceived liberal approach to both immigration and refugees poses a
security threat to the United States,
mmigration has been connected with terrorism, immigration has also been related to increased
criminality, resulting in the perception that immigration is a threat to public security. The issue
of whether or not immigration actually results in increased crime rates is, again, an issue of
perception versus reality. While the public has become increasingly concerned about high crime
rates intensified by immigration and the threat that immigrants pose to public order, these
concerns are empirically unsound (Wang 2012:743). Contrary to popular opinion, several studies
on a number of states have found no strong correlation between immigration and criminality.
It cannot be denied that in some states, there has been a connection between increased
immigration flows and increased crime rates. There is, indeed, a trend showing that cities and
countries that have high crime rates tend to have a higher immigrant population. For instance, a
study found that in 2001, “the proportion of the prison population born abroad in Spain was
twenty five times higher than the proportion of immigrants in the population” (Westbrook
2010:101). However, as Westbrook (2010) insightfully argues, this has much more to do with
demographic factors than it does with simply having an immigrant status (101). In the case of
Spain, the majority of immigrants are those who have the highest incidence of criminal
behaviour: single men aged 18 to 35 (Westbrook 2010:101). Thus, in examining the relationship
between immigration and criminality, demographic variables must be taken into account.
There is an abundance of evidence which demonstrates that the correlation between immigration
and criminality is very weak or non-existent. A study of three American neighbourhoods
concludes that in general, immigration does not lead to increased levels of homicide among
Latinos and African Americans (Lee et al. 2001:559). Similarly, in another study, Butcher and
Piehl (1998) conclude that the flow of migration has no effect on a city’s crime rate (457). Bell et
al. (2010) investigate the relationship between immigration and crime during two particular
periods of large migration flows in the United Kingdom: during the wave of asylum seekers in
the 1990s and early 2000s, and the inflow of economic migrants from EU accession countries
beginning in 2004 (1). The study reports that neither wave impacted rates of violent crime, and
that immigrant arrest rates were no higher than native arrest rates (Bell et al. 2010:17). Evidently,
while widespread public opinion holds that immigration is a threat to public security, it is a
constructed threat, not founded upon empirical facts.
The terrorist attacks of September 2001 (9/11) in the United St.
Spatial analysis of Mexican Homicides and Proximity to the US BorderKezia Dinelt
This document describes a spatial analysis of homicides related to drug trafficking organizations (DTOs) in Mexico in 2010 in relation to proximity to the U.S. border. The author hypothesizes that DTO homicides will be more prevalent closer to border crossings due to drug trafficking between Mexico and the U.S. The analysis uses GIS shapefiles including Mexican municipalities, DTO homicides in 2010, U.S. border cities, and population/inequality data. Regression is performed to analyze the relationship between DTO homicides and distance from border points while controlling for population density and inequality.
Crime is considered as a serious issue. It makes people worried on their own safety especially when the crime rate is increasing. The study generated the trend analysis and forecasted data of the recorded crimes in Ozamiz City using autoregressive integrated moving average through the GRTEL software. Results showed that majority of the crimes recorded decreased from 2018 to 2021. These include crimes against person, crimes against property, crimes against personal liberty and security and violations against special laws particularly the comprehensive dangerous drugs Act of 2002. However, the violators who resisted persons in authority drastically increased. On the other hand, forecasted data revealed that most of the crimes will most likely increase in occurrence in the year 2022 and will be stabilized in the remaining years. The data gathered from this study can be used by the different law enforcement agencies in planning and creating intervention strategies towards mitigating the occurrence of crimes in Ozamiz City.
Since coming into office two years ago, Chinese President Xi Jinping has carried out a sweeping, highly publicized anticorruption campaign. Skeptics are debating whether the campaign is biased towards Mr. Xi’s rivals, and even possibly related to the current economic slowdown. What is less debated is the next stage of Mr. Xi’s anti-corruption strategy, which is going to alter the legal statutes. Amendment IX, proposed in October 2014, includes heavier penalties, but two important tools in the fight of corruption – one-sided leniency and asymmetric punishment – became more limited and discretional. We argue that studying a 1997 reform and its effects can shed some light onto why the Chinese leadership seems dissatisfied with the current legislation and the likely effects of the proposed changes.
Chapter ONE INTRODUCTIONThe Pervasiveness (and Limitations) of Measure.docxnoel23456789
Chapter ONE
INTRODUCTION
The Pervasiveness (and Limitations) of Measurement
[Numbers] can bamboozle and not enlighten, terrorize not guide, and all too easily end up abused and distrusted. Potent but shifty, the role of numbers is frighteningly ambiguous.
—Blastland & Dilnot (2009, p. xi)
On September 23, 1999, NASA fired rockets that were intended to put its Mars Climate spacecraft into a stable, low-altitude orbit over the planet. But after the rockets were fired, the spacecraft disappeared—scientists speculated that it had either crashed on the Martian surface or had escaped the planet completely. This disaster was a result of confusion over measurement units—the manufacturer of the spacecraft had specified the rocket thrust in pounds, whereas NASA assumed that the thrust had been specified in metric system newtons (Browne, 2001).
Measurement is obviously very important in the physical sciences; it is equally important in the social sciences, including the discipline of criminology. Criminologists, policy makers, and the general public are concerned about the levels of crime in society, and the media frequently report on the extent and nature of crime. These media reports typically rely on official data and victimization studies and often focus on whether crime is increasing or decreasing.
Both official crime and victimization data indicated that property and violent crime in the United States were in a state of relatively steady decline from the early 1990s to 2000. But in late May 2001, the release of Federal Bureau of Investigation (FBI) official crime data, widely publicized in the media, indicated that crime was no longer declining. This prompted newspaper headlines such as “Decade-Long Crime Drop Ends†(Lichtblau, 2001a) and led commentators such as James Alan Fox, dean of the College of Criminal Justice at Northeastern University, to assert, “It seems that the crime drop is officially over. … We have finally squeezed all the air out of the balloon†(as quoted in Butterfield, 2001a). However, some two weeks after the release of these official data, a report based on victimization data indicated that violent crime had decreased by 15% between 1999 and 2000, the largest one-year decrease since the federal government began collecting national victimization data in 1973 (Rennison, 2001). The release of these data prompted headlines such as “Crime Is: Up? Down? Who Knows?†(Lichtblau, 2001b) and led James Alan Fox to declare, “This is good news, but it’s not great news†(as quoted in Bendavid, 2001).
How do we reconcile the conflicting messages regarding crime trends from these two data sources? First, although most media sources commenting on the FBI data failed to mention this caveat, the official data report was in fact based on preliminary data: “[The report] does not contain official figures for crime rates in 2000†(Butterfield, 2001a). Second, and more important, the underlying reason for these differe.
This document provides a summary of predictive policing technologies and their implications. It discusses how policing has evolved from political to community-based models using broken windows theory. Two predictive policing software programs, PredPol and HunchLab, use data analysis to predict future crime locations. While these programs aim to efficiently allocate police resources, concerns include privacy violations, implementation costs, and potential biases. The document examines debates around predictive policing and big data collection in law enforcement.
Human rights oriented article that examines whether the transitional justice measures implemented in Serbia have had the desired effect of contributing to reconciliation after the Yugoslav conflict in the 1990s.
This study examines factors that impact crime rates across major U.S. cities using data from 251 cities. Ordinary least squares regression is used to analyze the relationship between crime rates and variables like ethnicity, income, gender, age, population density, and political affiliation. Preliminary results found higher crime rates were associated with higher percentages of the population aged 25-34, higher population density, and higher percentages of divorced individuals. Lower crime rates were linked to more vacant housing units, higher median household incomes, and cities that lean Democratic politically. The study aims to help understand why crime rates vary significantly between cities with seemingly similar populations.
Literature Review for third party crime reporting.pdfsdfghj21
This document summarizes literature on third-party crime reporting across cultures. It discusses how perceived social cohesion and trust in police affects crime reporting rates in individualistic vs. collectivistic cultures. In cultures with high social cohesion, third parties may be less likely to report crimes to avoid social consequences, while those in individualistic cultures may feel obligated to notify authorities. The document also examines how factors like gender, number of cosocialized siblings, and crime type influence willingness to report in different societies.
The criminal justice system aims to punish and prevent crime through departments like the police, Crown Prosecution Service, and judiciary. However, the system is criticized for being biased in how it defines crime and deviance as well as in how it disproportionately targets some ethnic groups. While necessary to maintain order, the system excludes many minority groups and solidifies distrust between these groups and institutions through its labeling and lack of addressing socioeconomic inequalities. Reforms have been introduced but fail to overcome issues of potential politicization or institutional discrimination.
ARTIFICIAL INTELLIGENCE MODELS FOR CRIME PREDICTION IN URBAN SPACESmlaij
This work presents research based on evidence with neural networks for the development of predictive
crime models, finding the data sets used are focused on historical crime data, crime classification, types of
theft at different scales of space and time, counting crime and conflict points in urban areas. Among some
results, 81% precision is observed in the prediction of the Neural Network algorithm and ranges in the
prediction of crime occurrence at a space-time point between 75% and 90% using LSTM (Long-ShortSpace-Time). It is also observed in this review, that in the field of justice, systems based on intelligent
technologies have been incorporated, to carry out activities such as legal advice, prediction and decisionmaking, national and international cooperation in the fight against crime, police and intelligence services,
control systems with facial recognition, search and processing of legal information, predictive
surveillance, the definition of criminal models under the criteria of criminal records, history of incidents in
different regions of the city, location of the police force, established businesses, etc., that is, they make
predictions in the urban context of public security and justice. Finally, the ethical considerations and
principles related to predictive developments based on artificial intelligence are presented, which seek to
guarantee aspects such as privacy, privacy and the impartiality of the algorithms, as well as avoid the
processing of data under biases or distinctions. Therefore, it is concluded that the scenario for the
development, research, and operation of predictive crime solutions with neural networks and artificial
intelligence in urban contexts, is viable and necessary in Mexico, representing an innovative and effective
alternative that contributes to the attention of insecurity, since according to the indices of intentional
homicides, the crime rates of organized crime and violence with firearms, according to statistics from
INEGI, the Global Peace Index and the Government of Mexico, remain in increase.
Artificial Intelligence Models for Crime Prediction in Urban Spacesmlaij
This work presents research based on evidence with neural networks for the development of predictive crime models, finding the data sets used are focused on historical crime data, crime classification, types of theft at different scales of space and time, counting crime and conflict points in urban areas. Among some results, 81% precision is observed in the prediction of the Neural Network algorithm and ranges in the prediction of crime occurrence at a space-time point between 75% and 90% using LSTM (Long-ShortSpace-Time). It is also observed in this review, that in the field of justice, systems based on intelligent technologies have been incorporated, to carry out activities such as legal advice, prediction and decisionmaking, national and international cooperation in the fight against crime, police and intelligence services, control systems with facial recognition, search and processing of legal information, predictive surveillance, the definition of criminal models under the criteria of criminal records, history of incidents in different regions of the city, location of the police force, established businesses, etc., that is, they make predictions in the urban context of public security and justice. Finally, the ethical considerations and principles related to predictive developments based on artificial intelligence are presented, which seek to guarantee aspects such as privacy, privacy and the impartiality of the algorithms, as well as avoid the processing of data under biases or distinctions. Therefore, it is concluded that the scenario for the development, research, and operation of predictive crime solutions with neural networks and artificial intelligence in urban contexts, is viable and necessary in Mexico, representing an innovative and effective alternative that contributes to the attention of insecurity, since according to the indices of intentional homicides, the crime rates of organized crime and violence with firearms, according to statistics from INEGI, the Global Peace Index and the Government of Mexico, remain in increase.
The Relationship Rural Development and CrimesAI Publications
This document summarizes research on the relationship between rural development and crimes. It discusses several theories from criminology that explore this relationship, such as social disorganization theory, strain theory, and subcultural theory. The document also presents eight hypotheses about the positive relationships between various factors like law enforcement, urbanization, demographic characteristics, and industries/enterprises with rural development and crimes. Finally, it reviews literature on this topic and discusses methods used in the research study, including a survey of 118 people to analyze the relationship between rural development and crimes.
This paper focuses on a cross-cultural comparative analysis between the characteristics of immigration policy in France and the United States. It shows how culture, religion and history influence immigration policy in both countries. It looks at current major issues and shifts in immigration policy in both countries and what measures governments in both countries are taking to address them.
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EC831 Project "Does More Immigration Lead to More Crime?"
1. “Does More Immigration Lead to More
Crime?”
EC831 Project Martin Hewing 1504565
9993 words
I. Introduction
As immigration has increased over the past twenty years, there has been a rise
in the number of economic studies of the impact of migration on crime, in
countries that have been destinations for migrants. This issue is at the
forefront of current events; with some arguing that the general public voting in
the recent referendum, to leave the EU termed "Brexit" was partly due to fear
of immigration, and its effect on crime. Nigel Farage (2013) [1] speaking at the
UKIP party conference in September of 2013 claimed that London was in the
midst of a “Romanian crime wave” also to accusing the coalition government
of welcoming “foreign criminal gangs” from the newly joined EU member
states. Therefore, if the general public share these viewpoints on a possible
link between immigration and crime, they could vote in so called “right-wing”
politicians, leading potentially to serious economic consequences.
II. Literature Review
Mastrobuoni and Pinotti (2011) [2] used an empirical model, to examine
whether there is a causal effect between the legal status of a migrant and
propensity for criminality. Using data from the wave of immigration that took
place in 2007, when Romania and Bulgaria joined the EU, the authors took
advantage of the differences in travel policies between countries, to
implement a difference-in-difference approach for their estimation strategy.
They found that legalization of migrants from Bulgaria and Romania reduced
the chance of rearrests from 5.8% to 2.3% for both groups. The explanation of
their result relies on the fact that legalisation means greater labour market
opportunities for migrants. The findings are consistent with the Becker (1968)
model of crime. When rationally behaving immigrants have access to a legal
income through labour market opportunities, they decide to commit less
crime, as the opportunity cost of crime has increased. However, Freedman et
2. al. (2014) [3] state the authors estimate reoffending rates only from
immigrants returned to prison in Italy. This limits the author's ability to be able
to differentiate between the impact of real reductions in criminality from the
impact of greater mobility and resettlement of the migrants from Romania and
Bulgaria. Freedman et al. (2014) concluded in an alternative study that
increased mobility of immigrants might cause one to underestimate, not
overestimate the effects of increased labour market access. A similar approach
to Mastrobuoni and Pinotti (2011) was later taken by Pinotti (2014) [4] in an
empirical study on the immigration status of migrants and their propensity for
crime in Italy. He used a regression discontinuity as a regression model to test
using a sample of the application data for legalization which was provided by
recent immigrants and data on major crimes that were carried out by non-
natives. He found that when an immigrant gains legalization, the probability of
committing crime falls, however, the effect is stronger in locations which
presented immigrants with greater labour market access and less strict
immigration enforcement. Similarly, to Mastrobuoni and Pinotti (2011),
Alonso-Borrego et al. (2012) [5] also used an empirical model to test if crime
and immigration are linked. They consider the wave of mass immigration into
Spain from various locations that took place in a ten-year period beginning in
1999. In their estimation strategy the authors control for different individual
characteristics of migrants such as their level of education, language skills and
age Moreover they also considering economic characteristics such per capita
GDP and the levels of unemployment in the different Spanish provinces.
However, as noted by the authors, the use of data at the provincial can cause
endogeneity. This is due to differences in economic characteristics of different
locations and to the fact that immigrants were seen to cluster in locations that
offer them the greatest economic benefits such as access to labour markets.
The Spanish Foundation For Science and Technology (2012) [6] in an interview
with the authors noted that, to overcome this endogeneity problem, Alonso-
Borrego et al. (2012) used longitudinal data, allowing them to accurately
estimate the effect of immigration on crime, stripping this away from a
positive correlation between higher levels of crime in locations with more
economic opportunities and the fact that migrants cluster in these areas. They
find that there is a positive correlation between immigration and crime;
however, this is not a causal effect. This result can be explained by the
individual characteristics of the immigrants especially with respect to language
skills and educational achievement, since migrants with better language skills
and education commit less crime. The Spanish Foundation for Science and
Technology (2012) Also point out that the migrant influx consisted of a large
proportion of young males, the group that tend to commit most crimes, this
3. being a factor in the positive correlation between crime and immigration, but
not being causal. These findings are in line with the famous 'Latino Paradox'
that found a decrease in criminality in the population of Mexican immigrants in
the USA in the 1970's and 1980's Sampson et al. (1997) [7]. Buonanno et al.
(2012) [8] also, empirically analyse immigration to Italian provinces from 1999
to 2003 and its impact on crime using Italian law enforcement records. They
first show that by using Ordinary Least Squares (OLS) that there is a positive
correlation between migrant population size, the number of property crimes
and total crime, in particular they find that, a 1% increase in the population of
immigrants could lead to a 0.1% rise in the number of total crimes with the
impact on property crime being very significant. However, the authors
acknowledge that their results could be biased given that differences in
geographic locations where the migrants settled can lead to a problem of
endogeneity. Bracco and Onnis (2015) [9] explain that the approach taken by
the authors with regards to OLS has the potential to suffer from measurement
error from the presence of undocumented migrants (non-legalized migrants
can't be observed). In order to decrease the possible bias caused by omitted
variables Bracco and Onnis (2015) have extended the analysis of Buonanno et
al. (2012) by including dummies to account for geographic locations and
periods of time. They find that legalizations of migrants have helped to
improve OLS coefficient accuracy and this increases substantially the fitting
power of their empirical model. Secondly in an attempt to overcome the
endogeneity issues faced when taking an OLS approach the authors used
instrumental variables, exploiting the difference between immigration to
various locations. Specifically, they instrument immigrant flows to Italian
provinces versus migrant flows to the rest of the EU. Their subsequent findings
show that the impact on total and property crime is not statistically significant
but the impact on robberies is significant. In another empirical study, on the
effects of immigration and crime, Spenkuch (2013) [10] used fixed effects
panel data techniques on data from the county level in USA; this differed to
the differences in differences panel data method used by Mastrobuoni and
Pinotti (2011). He found that immigration causes a statistically significant
increase in property crime. Furthermore Spenkuch (2013) finds that a rise in
the population of immigrants of10% leads to a 1.2% rise in property crime, and
that immigrants, on average, commit 2.5 times more property crimes as
natives. Moreover, he found that when separating immigrants into their
respective racial groups, Mexicans were the only group that was found to have
a significant impact on property crime, and this can be explained due to
Mexicans having poor labour market outcomes. Additionally, Piopiunik and
Ruhose (2017) [11] found that in Germany where immigrants were
4. exogenously allocated to various provinces in Germany, a highly statistically
significant impact of immigration on all types of crime. However, this result
was much stronger in locations that suffered from higher levels of existing
crime and unemployment. This can lead to the conclusion that their estimated
results may be affected by an endogeneity problem. Bell et al. (2013) [12]
carried out an empirical study which examined two different waves of
migration into the UK and the impact on crime. The first group consisted of
Asylum seekers who came to the UK at the end of the 1990's and start of the
2000's, and the second group consisted of economic migrants (A8) that arrived
in the UK in search of employment following the enlargement of the EU after
2004. The authors found that the influx of asylum seekers did lead to a small
increase in property crime, whereas, for the A8 group they found there was a
small negative impact on property crime. Their findings suggest that access to
labour market opportunities are the driving factor behind the small increase in
criminal activity from the first group. Miles and Cox (2014) [13] examine
legalization status of immigrants and its effect on crime by analysing the
impact of a US government scheme called “secure communities” on rates of
crime. The scheme was implemented in 2008 on a staggered basis across 3000
US counties. The scheme records data on immigrants, arrested at the local
level, who have violated immigration laws and may be subsequently deported
by federal authorities. Miles and Cox firstly used a difference in difference
approach similarly to Mastrobuoni and Pinotti (2011) to estimate the
implementation of “Secure Communities” across the US, and the effect on
rates of crime. They found that the scheme did not have a statistically
significant impact on reducing the rate of crime as measured by the crime rate
index of the FBI, or any reduction on crimes of a violent nature. In an interview
where they discuss their research [14] they also discovered that the scheme at
first focused strongly in Latino districts, questioning whether there might be
some bias in the results due to the different economic characteristics of mainly
Hispanic neighbourhoods and the fact that one racial group seems to be the
focus of the scheme. Legalization status is also analysed by Fasani (2016) [15]
which follows on from the work of Mastrobuoni & Pinotti (2011), Pinotti (2014)
and Miles and Cox (2014). He looks at the legal status of immigrants and the
impact on crime by analysing migrant amnesty data collected over fifteen
years, beginning in 1990 in Italy. Fasani's approach takes into account the
potential problems due to endogenous factors by using instrumental variables
similarly to Buonanno et al. (2012). Fasani instruments the real number of
migrants with legal status compared to predicted numbers legalized migrants
which is based on previous amnesty data and the geographic locations that
legal and illegal migrants have settled. He comes to the conclusion that
5. locations with larger shares of immigrants experience a fall in crime the year
after an amnesty takes place. However, Fasani notes that the impact is small,
and that Italian natives and other EU migrants do not create a substitution in
the market for crime. Furthermore, a German study conducted by Pfeiffer et al
(2017) [16] found that crimes committed by immigrants were twice as likely to
be reported to the police as those committed by Germans. Additionally the
research carried at Zurich University of Applied Sciences which analysed
asylum seeker data from 2015 and 2016 from Lower Saxony in Germany and
found that migrants from Syria, Iraq and Afghanistan, which made up 54% of
the migrants in the study committed 34% of the violent crime attributed to
asylum seekers, however, they also found that even though migrants from
Algeria, Tunisia and Morocco only make up 0.9% of the asylum seekers in their
study they are responsible for 17.1% of the share of violent crime, postulate
that these groups often have to share cramped living conditions, due to
legalization restrictions which could potentially lead to inter-group violence, as
they found that 91% of murders and 75% of assault cases were between
migrants, moreover, many of the migrants were young-men- the group which
commits the most, violent crime, and interestingly they postulate that the
male to female ratio of the migrants was a major contributing factor to the
situation. In conclusion the current research points towards the fact that
immigration may increase some types of property crime, however, this positive
correlation is not a causal effect. Therefore, further research is needed to
analyse this important question so that the economic effects of increased
immigration and a potential link between rising crime levels are fully
understood.
III. Theoretical Model of Crime
In his seminal model of the economics of crime Becker (1968) [17] states that
rational economic agents decide whether or not to commit a criminal act based
on the expected utility that they could expect to gain by carrying out the crime,
weighted against the probability of being apprehended. Rohling (2010) [18] in a
technical analysis of Becker’s (1968) Crime and Punishment an Economic
Approach states that;
Euj = pjUj(Yj – fj) + (1 – pj)Uj(Yj)
Where Yj is the financial proceeds of crime, Uj is the utility function; pj is the
likelihood of being arrested and convicted and fj is the financial equivalent of the
6. punishment if an individual is indeed convicted. Following this logic an individual
j carries out a crime if the expected utility from the criminal act is greater than
the expected utility that the individual would gain from using their human
capital in a legal manner.
The supply of crimes, that individual j commits can be represented by a crime
function.
Oj = Oj(pj, fj, uj)
Where uj represents all other factors that individual j takes into account when
making a decision whether or not to carry out a crime. Furthermore, the market
supply of criminal acts is the summation of all the Oj and therefore
O = O(p, f , u)
Where p, f and u take average values. Thus, an increase in p or f decreases
expected utility and thus the supply of offences.
Op > 0 and Of > 0
On the other hand, while criminals gain from carrying out criminal acts G(O),
society in general is harmed H(O) by crime, and net damage D(O) to society from
crime expresses the loss to society from crime L(O).
L(O) = D(O) = H(O) = G(O)
where Η’ > 0, Η’’(Ο) > 0, G’ > 0, and G’’ < 0
The minimization of loss to society from crime is the stated aim of this social
policy. The relevant variables are the likelihood of arrest and conviction p and
the financial punishment f, as both are directed linked to the quantity of crimes
O.
Furthermore, to minimise the loss to society with respect to p and f:
D’(O) = 0 and thus G’(O) = H’(O)
Thus, to set the conditions for optimal policy, p must be equal to 1 and a fine f =
H’(O), and this leads to a high deterrence effect, in so forth that the fine f
7. compensates fully the marginal victim. However, when taking into account the
costs of arrest and conviction, a p value of 1 cannot be optimal.
Factoring the fore mentioned analysis, the total costs of arrest and conviction
C(p, O) then are determined by p and O.
where Cp > 0 and CO > 0
Therefore, a rise in the chance of arrest and conviction or the total number of
crimes, leads to an increase in the total cost of arrest and conviction.
When total costs of arrest and conviction are factored in to the constrained
optimisation problem of minimising social loss and p is set to 1, the loss
function becomes
L(O) = D(O) + C(1,O)
Thus, minimising the social loss function from crimes O with respect to fines f
leads to
D’(O) + C’(1, O) = 0 and thus H’(O) + C’(1,O) = G’(O) = f
So that when costs of arrest and conviction are taken into account, convicted
criminals compensate the marginal victim optimally, in addition to the
marginal cost of their arrest and conviction.
On the other hand, if f is held fixed, while p is allowed to vary, the optimality
condition changes to
D’(O) + C’(p, O) + Cp/Op = 0 and thus H’(O) + C’(p, O) > G’(O)
This leads to the conclusion that optimisation with respect to p and f is
impossible, f or p must be arbitrarily set and then social loss is minimised by
the other variable. The ideal situation is that p ≅ 0 and f → ∞ (if possible). In this
case a deterrent effect is created and total costs to society are low.
Other costs imposed on society are caused by the cost of punishing criminals;
these costs can be modelled as total social cost of punishment bpfO, which is
the summation of costs to criminals, in addition to costs to society, this can be
split into the following categories pO: the quantity of convicted criminals, f: the
8. punishment per crime and b: the coefficient, which transforms the punishment
to criminals into costs for society. The value of the coefficient b: is linked to the
type of punishment. Firstly: fines. These impose no costs to society as they are
a transfer payment from criminal to victim so b≅ 0, and secondly: a prison
term. This does impose costs to the criminal in terms of loss of earnings and on
society in terms of the cost of housing the prisoner so b > 1 in this case.
Consequently, Becker’s general social loss function from committed crimes
L(O) can be given by
L(O) = D(O) + C(p, O) + bpfO
Where D(O) is societal damages from O crimes, C(p, O) are total costs from
arrests and convictions, and pbfO are societal total losses arising from
punishment, where b > 0 is the coefficient that transforms the punishment to
criminals into costs for society.
From the preceding analysis we can put forward a model for optimal social
policy as follows. Social loss L(O) minimised with respect to p and f leads to
DO + CO = -bpf(a-1/εf) (1)
Where εf = -(p/O)(Op) > 0
DO + CO + Cp/Op = -bfp(1-1/εp) (2)
Where εp = -(p/O)(Op) > 0
These two equations determine the optimal values of p and f. The left-hand
side of the two equations represents the marginal costs from an increase in O
or conversely a fall in f or p. whereas; the right-hand side represents the
marginal revenue of crime.
In previous empirical studies into a possible link between immigration and
crime, the theoretical model first put forward by Becker (1968) has been
widely used to try and ascertain why immigrants may commit crimes at
different rates to natives. This paper will also make use of this theoretical
framework to examine the impact of migration on crime.
When making use of this theoretical framework, this empirical study will take
into account that the levels of human capital that migrants have in terms of
9. their language skills, years of education and work experience may be
significantly less than natives in the countries that they migrate to, therefore
the ability to earn a wage in the employment market could potentially be
severely curtailed and so the utility migrants gain from committing crime could
be much higher than the average native because the proceeds of crime are far
greater than the marginal wage they are able to earn legally, or in some cases
they might not be entitled to participate in the employment market at all.
With regards to the costs of committing crime for migrants versus natives,
another factor to consider is that immigrants run the risk of being deported to
their country of origin for breaking immigration laws not just the punishment a
native would suffer, thus the expected costs of committing a criminal act are
likely higher for non-natives.
III. Data Sources and Summary Statistics
In the paper I use an annual panel dataset at the country level, featuring
fifteen European countries (Belgium, Denmark, Germany, Ireland, Greece,
Spain, France, Italy, Netherlands, Austria, Portugal, Finland, Sweden, United
Kingdom, Norway and Switzerland) over the period 1993 - 2007.
The data for total population in the respective countries has been sourced
from the World Bank total population database, which is assembled from
various different sources including the UN population division, Census reports
and other data from statistical offices, Eurostat - (Demographic statistics and
the UN Statistical Division). Limitations of these data are that total population
is defined as all residents of a country, disregarding their citizenship or legal
status, thus, there may-be some overlap between immigration statistics and
population data, in addition the respective statistical agencies in individual
countries use different techniques to collect and define population data,
therefore, there is likely no homogeneity across various geographical areas.
The total population data is a midyear estimate. The female fraction of a
population is a demographic indictor because it gives information about the
age of a countries population; for example, Institut National D’Etudes
Demographiques (2015) [19] using information from the UN, World Population
Prospects (2017) states that in 2015 the gender balance in France was 50.4%
male and 49.6% female. Explaining that one hundred and seven males are born
for every-one hundred females, however, males suffer from higher rates of
mortality. But at a certain age the totals for males and females converge. In
France this is at age twenty five, and after this age, females begin to
outnumber males, therefore populations with a larger share of females are
10. older, moreover, Ulmer and Steffensmeier (2014) [20] state that the age group
that commits the most crime is those 25 and younger according to the FBI, so
the age of a population can give information on the stability of a nation. The
data for female fraction of the total population is sourced from the World Bank
database of population indicators and is based on the World Bank staff
estimates, which have been calculated via age/sex distributions from the UN
population division.
The data on criminal activity was sourced from the Eurostat Crime and Criminal
Justice Database, where police statistics are the number of criminal acts
recorded by the police and the number of suspected offenders and offenders
brought into formal contact with law enforcement agencies.
The crime data is measured as offence per one hundred thousand inhabitants.
In this paper crime has been divided into two distinct categories; firstly, violent
crime which includes intentional homicide, acts causing harm such as assault
and sexual offences, and secondly property crimes which includes robbery and
burglary. Total crime; the summation of all crimes is also given.
Threats to the accuracy of the crime data, include the fact that not all criminal
acts committed are reported to or detected by law enforcement agencies, and
furthermore the way that police record the details of crimes maybe different
across countries in the dataset. Missing data has been an issue particularly
affecting the data on Belgium, Ireland, France, Austria and Finland.
The United Nations Department of Economic and Social Affairs provided data
on immigration to destination countries within Europe and the source
countries that were migrated from. This information was collated from various
sources including the United Nations Statistical Department Demographic
Yearbook. The data has the potential for inaccuracies, especially because the
database is compiled from various sources, therefore discrepancies between
inputs may occur. Statistics on economic indicators, including average wages in
each country in the dataset has been collected from the database at the OECD,
who calculated their results by dividing the total salary data from the national
accounts in the respective countries by the total number of workers in those
locations, which they weighted depending on whether the employee was full
or part time. Average wages are measured in US dollars with 2016 = 100 being
the base year. An additional economic indicator is GDP per capita measured in
current US dollars and is sourced from the World Bank Economic Indicators
database, which made use of statistics from the World Bank national accounts
and OECD National Accounts data. This figure is the summation of gross value
created by all economic agents in a country minus any taxes or subsidies; they
have calculated this data without making any reductions for depreciation. Data
11. on unemployment has been collected from the World Bank database of
economic indicators and is defined as the share of the work force that is not in
employment but available for work and actively searching for a job. The World
Bank has compiled the database from the International Labour Organisation
and the ILOSTAT database. A limitation of the data is that it does not give any
indication of age or underemployment.
Data relating to government spending on public order and safety as a
percentage of GDP is sourced from the Eurostat Annual government finance
statistics data and is an all-encompassing figure of spending on public
protection, the measure contains the following categories ‘police services’,
‘fire protection services’, ‘law courts’, ‘prisons’, ‘R&D related to public order
and safety’ as well as expenditure not elsewhere classified. Due to the
limitation of the data, information solely on police spending was not available.
Table 1 shows summary statistics for the variables described in this section,
these values are unweighted and raw.
IV. Econometric Strategy
OLS and Fixed Effects Regression
In an attempt to provide an answer to the question “Does more immigration
lead to more crime?” I will start analysing whether there is a positive
relationship between levels of inwards migration. Then I will try and isolate a
causal relationship of immigration on crime.
In the empirical analysis I will focus, separately on violent and property crimes.
I will use a panel dataset constructed from data with the following countries:
Belgium, Denmark, Germany, Ireland, Greece, Spain, France, Italy,
Netherlands, Austria, Portugal, Finland, Sweden, the United Kingdom, Norway
and Switzerland over a fourteen-year period (1993 to 2007). The worldwide
source countries that the migrants originally came from have been grouped by
continent. (Africa, Asia, Europe, Oceania, North America and those from
unknown locations)
I will use fixed effects estimation strategy as in Spenkuch (2012). In the
regression model below, η, the parameter of interest represents the elasticity
of the crime rate, with respect to the immigrant population share from Africa,
Asia, Oceania, Europe, North America and unknown locations. The model will
12. be regressed using weighted least squares, with the weights being represented
by the destination country population.
In(crimec,t) = η ln(immigrantsc,t) + β ln(populationc,t) + X’c,t γ + μc + τt + εc,t,
Where the subscripts c and t are, respectively, the country of destination and
year. The variable crime c,t is the crime rate. Additionally immigrantsc,t and
populationc,t are the total numbers of immigrants and the population
respectively. In this paper a set of country level covariates represented by X’c,t.
This vector of covariates controls for various factors. Demographics of a
country are controlled for by log fraction female, whereas, economic
conditions are controlled for via the log of average wages, unemployment and
GDP per capita respectively, additionally log of law enforcement spending as a
percentage of GDP attempts to control for changes in police expenditure.
, μc are country level fixed effects that control for country unobserved
heterogeneity that does not change over time. τt are year fixed effects that
capture the influence of aggregate (time series) trends. Finally εc,t, is the error
term.
Spenkuch (2012) notes that endogenous factors such as age race and income
should not be controlled for, as natives and migrants do differ, and if these
factors were fully controlled for then, our estimate η would not give any
information on the impact of migration on levels of criminal activity. To
conclude, the fixed effect strategy, by making use of the country and year fixed
effects, holds constant unobserved characteristics, which would normally vary
over time, leading to an econometric model that potentially gives an unbiased
estimate of η.
Results from OLS and Fixed Effects Regressions
Property Crime
Table 2 shows the findings for OLS and fixed effects regressions, property
crime rates are the dependent variable. (This log, log model can be interpreted
as %Δy=βi%Δx). The OLS results in column (1) and (2) shows there is a
statistically significant positive correlation between African migration and
increasing levels of the property crime rate. There is a negative correlation for
European immigration and the property crime rate. In both cases when year
and country fixed effects are controlled, the results are no longer significant.
Bell et al. (2013) [21] found that Asylum seekers, including migrants from
13. Somalia, who came to the UK at the end of the 1990’s, caused a significant
increase in the property crime rate. A8 EU enlargement immigrants conversely
caused a small significant fall in the property crime rate, but their significant
findings did control for fixed effects, whereas the significant results in this
paper only occur with OLS and heteroscedasticity robust standard errors. Bell
et al. (2013) [22] noted that the Asylum seeker group in the UK had severely
restricted access to official labour markets, with long periods of time before
Asylum seekers could legally work. Asylum seekers waiting for their cases to
be heard received substantially lower benefits which made them more likely to
turn to crime. This effect may explain the findings of this paper, because this
paper, and Bell et al. (2013) cover the same time period, as they both include
the A8 EU enlargement in 2004.
The fraction female of a population also provides a significant correlational
result in column (1) using OLS and is negatively correlated. However, the
impact is no longer significant in column (2) when heteroskedastic standard
errors are applied. This is also the case in columns (3) to (6) once fixed effects
for year and country are applied. Campeniello et al. (2017) [23] in their
research into the gender crime gap, found that only 30% of property crimes in
the United States were carried out by females. It is likely the same effect is
present in the European findings of this paper and may explain why the
fraction female of a population is significant and has a large negative
coefficient. Once again small dataset size could be an issue.
Economic indicators, which have a statistically significant effect on crime rates
are average wage, and GDP per capita, although these results are only
significant in columns (1) and (2) for both indicators. Buonanno et al. (2010)
[24] found a correlation between increasing income, immigration and property
crimes in the North of Italy. In their research, property crime made-up for 83%
of all the crimes in their sample. But, correlation is not causation and, there
could be other unseen factors driving the result in their findings, and the
results of this research paper in terms of the positive correlation between
average wage, GDP per capita and property crime rates. Furthermore, they
discovered when they controlled for GDP at the provincial level, their
estimates of the impact of immigration on property crime fell. They surmised
this effect could be due to an increase in GDP, which may be a driver of rising
crime and immigration, especially property offences. On the other hand,
Piopiunik and Ruhose (2017) [25] did not find evidence to suggest that the
effects of crime were different in German counties with unequal levels of GDP
per capita. Therefore, the impact of economic factors such as increasing GDP
14. and its effect on immigration and property crime is somewhat ambiguous in
the literature, and thus this paper. As mentioned previously the major
limitation of these findings is that we do not have enough variation in the data,
due to the small sample size. Ideally, if more information was available, we
might see a different outcome. It might be possible with more data; the
results would be similar to those found in the literature (a positive correlation
for immigrants without access to labour markets) with respect to immigration
and property crime when fixed effects are implemented.
Violent Crime
Table 3 shows the findings for OLS and fixed effects regressions with property
crime rates as the dependent variable. (This log, log model can be interpreted
as %Δy=βi%Δx). The OLS regressions in columns (1) and (2) of this paper show a
negative correlation between increasing immigration from Africa and violent
crime rates. These results hold in columns (3) and (4) when controlling for year
and year fixed effects. However, in column (6) this effect becomes positive, but
not significant, once year and country fixed effects are applied. While, in the
second row of table 3, the results of increasing Asian immigration on violent
crime rates shows a significant positive correlation in columns (1) and (2) with
OLS and heteroscedasticity robust standard errors. These results hold in
columns (3) and (4), however the sign changes in columns (5) and (6).
Moreover, the results for increasing immigration from the USA, and its effect
on violent crime rates, shows that there is a negative significant correlation,
which holds even when controlling for year and country fixed effects. In
common with these findings, the majority of the existing literature fails to find
a significant relationship between rising immigration and increasing violent
crime rates, once year and country fixed effects are applied. However, an
exception is research which has been quoted by the BBC (2018) [26] of an
empirical paper by Pfeiffer et al. (2017). These factors could potentially explain
the findings of this paper in column (6) when year and country fixed effects are
applied i.e. those with little or no hope of asylum or work have no incentive to
obey the law and may turn to crime be it property or violent. For the African
group we see a positive coefficient, whereas a negative coefficient for the
Asian group which fits with the findings of Pfeiffer et al. (2017). However,
possibility due to a lack of variation caused by small sample size the
coefficients on both groups are not significant in column (6) with both year and
country fixed effects, so the findings are ambiguous.
15. In this paper fraction female has a strong negative correlational effect on
violent crime rates. This effect possibly could be explained by the findings of
Campeniello et al. (2017). Their research into the gender crime gap found that
only 30% of property crimes in the United States were carried out by females,
however, it is not certain whether, this effect holds for violent crime. In the
regressions for law and order spending, in columns (5) and (6) when country
fixed effects, and year and country fixed effects are controlled for, then this
variable becomes positive and significant. A potential economic explanation is
the likelihood of an increase in spending on law enforcement results in greater
crime detection. A limitation of the data is that we don’t have police spending
data, but rather a total figure for law and order spending.
V. Instrumental Variables Approach
Methodology
The OLS strategy with fixed effects provides an estimation of the relationship
between migration and crime rates. A requirement of a causal relationship
between immigration and crime is that the residual not be correlated with the
log (real) number of migrants. There are three principal reasons why this may
occur (i) there may be an error measuring the quantity of migrants (ii) omitted
variable bias (iii) endogeneity in the location patterns of migrants (rational
migrants would locate to low crime areas). In my analysis I will replicate the
approach taken by Spenkuch (2012) to overcome these issues. He uses a
supply-push (theses are events in the immigrant’s home country that induce
outwards migration) instrumental variables approach which was developed by
Card (2001). This strategy instrumented the findings of Bartel (1989) who
found that migrants tend to locate to certain locations. Spenkuch (2012)
recognises that for the first differences estimator to be consistent the
instrument must have a correlation with the variation in the numbers of
migrants in location c at time t, but, has no effect on the period t change in
rates of crime in country c apart from changes in the quantity of migrants.
Formally: an instrument Zc,t will be valid if:
Cov[Z*c,t, ∆log(immigrantsc,t )*] ≠ 0 and Cov[z*c,t, ∆εc,t] = 0,
Where * represents the variability of the residual, in the respective variable.
16. Spenkuch (2012) uses the observation that migrants will settle in the same
location as previous immigrants of the same nationality to calculate the
predicted change in the log number of migrants from year t -10 to t which is
then used to instrument for the real change in migration patterns.
The following equation represents location c's estimated total quantity of
migrants in year t.
^
immigrantsc,t = ∑g [(∑c immigrantsc,g,t )(immigrantsc,g,t-10/∑cimmigrantsc,g,t-10)]
The instrument that will be used in my paper is as follows:
^ ^ ^
∆log(immigrantsc,t) = log(immigrantsc,t)-log(immigrantsc,t-10)
Which is the real change in the log of the quantity of migrants. From year t –
10 to t.
Where, t denotes year, g is the country that was migrated from.
However, due to limitations caused by missing data, this paper will only carry
out an instrumental variables approach on immigration and crime for migrants
from Africa and Europe respectively.
Testing for Exogeneity and Relevance
The conditions needed for a variable, z to be valid instrument for x, is that,
firstly the instrument z must be relevant i.e. be correlated with x: Cov(z, x) ≠ 0
and secondly the instrument z must be exogenous i.e. uncorrelated with u
Cov(z, u) = 0.
The first assumption, that the instrument is relevant i.e. Cov(z, x) ≠ 0, means
that our model contains instruments that are not weak. The consequences of
weak instruments are that they do not have much ability to explain the
variation of the predicted value of the exogenous variable we are trying to
estimate. Additionally, weak instruments can be biased and not normally
distributed, thus inference testing will not be possible.
17. Testing Excluded Instruments Separately
To check whether, the proposed model satisfies the above criteria for both the
instruments separately we can examine the F-test results from the first stage
regression for the coefficients on the excluded instruments to ascertain
whether F > 10. However, simply analysing the F-statistics in panel 1 of table
5.A and 6.A is not sufficient, because the excluded instruments do not give
enough information to simultaneously identify the coefficients on the
endogenous variables. Spenkuch (2012) [27] notes some of the issues that
might arise from including weak instruments are that firstly the instrumental
variables estimates can be asymptotically inefficient, which may lead to biased
results and secondly the issue of finite-sample bias. Bloom el al. [28], state that
finite sample bias can firstly bias point estimates and secondly lead to incorrect
statistical inferences. Therefore, to ascertain whether both the coefficients on
are identified we need to examine the weakly identified Sanderson-Wind -
Meijer (2015) [29] F-statistic: which are shown in panel 2 of 5.A and 6.A and
compare it to the Stock-Yogo (2005) [30] critical values below in 5.A and 6.A
respectively. As in both 5.A and 6.A the Sanderson-Wind Meijer F-statistics are
greater than the Stock-Yogo critical values, we can claim that both the
coefficients are separately relevant.
Testing Excluded Instruments Jointly
Furthermore, we can also test the instruments together for the presence of
weak instruments, to do this we need to analyse Xi: an exogenous regressor.
Xi = π0 + π1Z1i + … + πmZmi + πm+1W1i + … + π m+rWmi + vi
And carry-out an F-test on the coefficients of the set of instruments Z1i,…,Zmi to
determine whether, they are jointly equal to zero, this will show if the set of
instruments are weak. The results of this test are shown in 4.B and 5.B
respectively. When the errors are assumed to be i.i.d then the Cragg-Donald
Wald F statistic (1993) [31] is compared to the Stock-Yogo (2005) critical
values. As the Cragg-Donald Wald F Statistic F-statistic is greater than the
Stock-Yogo critical values, we can reject the null hypothesis that the equation
is weakly identified and conclude that the set of instruments are relevant.
Instrumental Variables Results
18. Due to the constraints imposed by the presence of missing data, the
instrumental variables strategy can only be applied to empirically testing if a
link between immigration and crime exists, when considering migrants from
European and Asian sources.
Property Crime
The left-hand column of table 4 displays the results for the Instrumental
variables strategy, with the log of property crime rates as the dependent
variable. (This log, log model can be interpreted as %Δy=βi%Δx). Spenkuch
(2012) [32] notes that an estimate of the causal relationship is valid provided,
there is no heterogeneity in effects, and the observational error is classical,
and these conditions are not violated in this paper. The first row of table 4
shows the estimate of the impact of Asian immigration on property crime
rates; however, this is not significant. Whereas, the estimate for European
immigrants and their effect on property crime rates does show a significant
negative causal relationship exists. Bell et al. (2010) [33] also discovered that
the A8 European immigrants in their study caused a negative causal decrease
in property crimes, although in their study they found that Asylum seekers did
cause a statistically significant causal increase in property crime, but only
amongst those Asylum seekers who were prohibited from legal employment.
Furthermore, as the European immigrants in this paper were also part of the
A8 wave, and had full employment opportunities, and thus little reason to
commit incentive driven crime, this provides an economic explanation to why
they have a negative significant causal relationship with the property crime
rate. A limitation of this paper is that due to dataset size, many of the various
immigrant source groups such as Africa, were dropped to make the instrument
relevant. Therefore, we need remember this when comparing the IV to the OLS
findings, as we are not comparing the same set of independent variables. This
may explain differences due to interactional effects in the different immigrant
source groups. As in the instrumental variables case, there is only a subset of
the original variables in the OLS, and fixed effects regressions. However, for
the impact of increased Asian and European immigration and its effect on
property crime rates, the OLS and IV do display the same signs, with the later
source group being statistically significant in both OLS and IV, with the
instrumental variables coefficient being smaller than the OLS.
Spenkuch (2012) [34] found that Mexicans (a group with poor labour market
opportunities) were the only group who had a statistically significant causal
effect on property crime rates in his US study, and it would be interesting to
19. examine whether the same effect holds in this empirical research with respect
to immigrants with poor labour market outcomes. Ideally, we would like to
carry out a sensitivity analysis, to try and ascertain whether, sub-setting
migrants from Afghanistan, Syria and Iraq from the rest of the Asian group
would change the results with respect to the property crime rate, as the
majority of these immigrants were asylum seekers with poor employment
prospects and an incentive to commit property crime, it is possible we would
see a positive significant causal relationship. Whereas the majority of Asians
from locations such as China or Hong-Kong might have good labour market
access, and this could be the reason the result is insignificant.
Buonanno et al. (2010) [35] only found a significant causal effect between
immigration and robberies, and no causal relationship between other types of
property crimes. Ideally it would be beneficial to examine the various types of
property crime separately, to examine whether either burglaries or robberies
provided any different results. Additionally, law and order spending is
statistically significant. The intuition is that because this measure is total
spending on law and order, thus increased spending is likely to lead to higher
detection rates.
Violent Crime
The right-hand column of table 4 gives the results for the instrumental
variables estimation with violent crime rates as the dependent variable. (This
log, log model can be interpreted as %Δy=βi%Δx). There is an insignificant
positive causal effect between Asian immigration and violent crime, unlike the
OLS and fixed effects results which are significant. There is an insignificant
positive causal effect between Asian immigration and violent crime, unlike the
OLS and fixed effects results which are significant. The BBC [26] has highlighted
that asylum seekers from Afghanistan, Syria and Iraq, may have a propensity
for violent crime. A sensitivity analysis, which could subset these migrants,
from other Asian immigrants, would be beneficial to ascertain whether there is
a positive significant causal effect from increasing migration from Afghanistan,
Syria and Iraq and increasing violent crime rates. The findings show there is a
significant negative causal effect between European immigration and violent
crime rates.
In terms of the instrumental variables results, the results for European
immigration are contrary to the findings of Bell et al. (2010) [36] who found no
significant causal link between European economic migrants, and violent crime
20. in their UK study, once instrumental variables had been used. An explanation
could be that the study focused on one country at a particular time period,
whereas this paper, examines many countries over a longer time frame and
thus captures more of the variation in the data. Moreover, as the period
covered by the study saw an influx of economic migrants from the EU
enlargement in 2004, these migrants enjoyed on the whole full labour market
access. They risked deportation if they committed violent crime, so had an
incentive to obey the law in the countries they migrated to. This could explain
why European migrants have a negative causal impact on violent crime rates.
Buonanno et al. (2010) [37] also confirm no significant causal relationship
between violent crime and immigration, however, their results only rely on
data from the year 2001, while this paper studies a longer time frame, which
could make comparisons difficult. Furthermore Spenkuch (2012) [38] in his IV
results found inconclusive evidence when examining immigration and its
impact on violent crime in the USA, although whether findings from North
America can be compared to those in Europe is debatable, therefore the
findings of this paper, with respect to European immigration having a negative
causal effect on violent crime goes against the majority of the findings of the
literature. There is a statistically significant causal link between GDP per capita
and the rate of violent crime but could possibly be explained by the fact that
the majority of migrants are young men, and this age group commit the most
violent crime. Furthermore, they tend to locate to countries with more
economic opportunities. Additionally, law and order spending provides a
positive significant causal effect. The intuition is that because this measure is
total spending on law and order, increased spending leads to higher detection
rates. However, on the last two points the literature is ambiguous.
Conclusion
After examining the various findings of the empirical project, the conclusion is
that immigrants with good employment prospects, such as European
immigrants, who came to the European Union in 2004 and 2007 respectively,
have a negative causal impact on both property and violent crime rates. These
findings agree with the majority of the literature for property crime, but
conversely are different in terms of violent crime. However, this paper did not
find that immigrants cause crime, conversely the results suggest that in the
right conditions immigration actually reduces crime, although only if the
migrants have good job prospects.
21. See data appendix for descriptions of variables and methods of how the variables have been calculated.
22. See data appendix for descriptions of variables and methods of how the variables have been calculated.
23. See data appendix for descriptions of variables and methods of how the variables have been calculated.
24. See data appendix for descriptions of variables and methods of how the variables have been calculated.
25.
26.
27. Data Appendix
% Africa is the logarithm of total immigrants from Africa divided by the total
population in the respective destination countries.
% Asia is the logarithm of total immigrants from Asia divided by the total
population in the respective destination countries.
% Europe is the logarithm of total immigrants from Europe divided by the total
population in the respective destination countries.
% Oceania is the logarithm of total immigrants from Oceania divided by the total
population in the respective destination countries.
% USA is the logarithm of total immigrants from North America divided by the
total population in the respective destination countries.
% Unknown is the logarithm of total immigrants from unknown source countries
divided by the total population in the respective destination countries.
% Female is the logarithm of the fraction of a total population that is female in
a destination country.
Average wage is the logarithm of average wage as per the description in the data
and summary statistics section.
GDP per cap. is the logarithm of GDP per capita as per the description in the data
and summary statistics section.
Unemp. Rate is the logarithm of the unemployment rate as per the description
in the data and summary statistics section.
Law/order $ is the logarithm of law and order spending as per the description in
the data and summary statistics section.
Austrian migration data is collated from population registers. Immigration being
defined as those persons who register to stay in the country. The data the
statistics on migration in Belgium is collected via the population register.
Immigration is defined as those foreigners who legally enter the country and
stay for at least three months. Immigration data on Denmark is derived from the
28. central population register, with immigration being defined as EEA/Swiss
nationals who stay for six months or more, and those persons from outside the
Nordic region, EEA and Switzerland if they plan to stay three or more months.
Data collated on migration in Finland is collected through the population
register and includes all people that change their permanent residence country
to Finnish. Immigration in Finland is defined as those foreigners who have
gained a residency permit and Fins who have returned from residing abroad. In
France migration data is calculated via the quantity of residence permits that
are issued for at least a year. In Germany data on migration is sourced from the
population register. Immigration is defined as those arriving from abroad who
register their home abode in Germany as being their main place of living. Greek
migration statistics are derived from information on residence permits.
Immigration statistics are calculated from the quantity of residence permits that
have been issued to foreigners. The data collected on migration in Ireland is
collated from a quarterly national household survey and various other sources.
Immigration is defined as people who are usually residents of Ireland and that
did not reside in there a year ago. In Italy the data on migration is calculated via
information from the population register, immigration is seen as those Italians
who reside domestically after residing abroad, those from the EEA if they stay
in Italy for twelve months or more, and those from non-EEA countries holding a
residence permit intending to stay for twelve months or greater.In the
Netherlands migration data is sourced from the municipal population register.
Immigration is seen as people who stay in the country for four months or more.
Norwegian migration statistics are collated through the population register,
where immigration refers to all people that intend to stay in Norway for six or
more months. Data on immigration in Portugal is collected from permit data,
and immigration data is measured as foreigners who have made an application
of a residence permit in a given year. The statistics on migration to Spain is
derived from the municipal population register. Immigration is defined as
foreigners and citizens intending to remain in Spain. In Sweden migration
statistics are collated from the population register. Immigration is seen as
foreigners and citizens coming from abroad and intending to remain in the
country for twelve months or more. The data for migration in Switzerland is
collated via the population and foreigner register. Immigration is defined as all
persons coming to the country from abroad to permanently or temporarily
reside in this country.In the UK flows of migrants are measured via the
International Passenger Survey (IPS) and immigration includes all persons who
have remained abroad for twelve months or more and now intend to remain in
the UK for a year or more.
29. Statistics on economic indicators, including average wages in individual
countries has been collected from the database at the OECD, who calculated
their results by dividing the total salary data from the national accounts in the
respective countries by the total number of workers in those locations, which
they then proceeded to multiply this figure by the ratio of the usual hours
worked by an average full-time worker to the average hours worked per week
by all workers (including part-time). Average wages are measured in US dollars
and are an index with 2016 being the base year. An additional economic
indicator is GDP per capita measured in current US dollars and is sourced from
the World Bank Economic Indicators database, which made use of statistics from
the World Bank national accounts and OECD National Accounts data. This figure
is the summation of gross value created by all economic agents within a
respective country minus any taxes or subsidies; they have calculated this data
without making any reductions for depreciation. Data on unemployment has
been collected from the World Bank database of economic indicators and is
defined as the share of the work force that is not in employment but available
for work and actively searching for a job. The World Bank has compiled the
database from the international labour organisation and the ILOSTAT database.
Bibliography
[1] Nigel Farage comments made while speaking at the UKIP party conference
in September of 2013.
[2] Mastrobuoni, Giovanni and Pinotti, Paolo, Migration Restrictions and
Criminal Behaviour: Evidence from a Natural Experiment (July 21, 2011). FEEM
Working Paper No. 53.2011. Available at SSRN:
https://ssrn.com/abstract=1891689 or
http://dx.doi.org/10.2139/ssrn.1891689
[3] Immigration, Employment Opportunities, and Criminal Behaviour*
Matthew Freedman, Cornell University Emily Owens, University of
Pennsylvania †Sarah Bohn, PPIC page 12A working paper that has been
accepted for publication in the American Economic Journal: Economic policy
https://www.aeaweb.org/articles?id=10.1257/pol.20150165&&from=f
[4] Pinotti, Paolo, Clicking on Heaven's Door: The Effect of Immigrant
Legalization on Crime (October 2014). Baffi Centre Research Paper No. 2014-
154. Available at SSRN: https://ssrn.com/abstract=2426502 or
http://dx.doi.org/10.2139/ssrn.2426502
30. [5] Does Immigration Cause Crime? Evidence from Spain César Alonso-Borrego
Nuno Garoupa Pablo Vázquez American Law and Economics Review, Volume
14, Issue 1, 1 April 2012, Pages 165–191, https://doi.org/10.1093/aler/ahr019
Published: 04 January 2012
[6] Immigration growth in Spain has not caused more crime
FECYT - SPANISH FOUNDATION FOR SCIENCE AND TECHNOLOGY
https://www.eurekalert.org/pub_releases/2012-06/f-sf-igi062512.php
[7] Neighbourhoods and Violent Crime: A Multilevel Study of Collective Efficacy
Robert J. Sampson, Stephen W. Raudenbush, and Felton Earls
Science
New Series, Vol. 277, No. 5328 (Aug. 15, 1997), pp. 918-924
Published by: American Association for the Advancement of Science
Stable URL: http://www.jstor.org/stable/2892902
Page Count : 7
[8] Do Immigrants Cause Crime ? Paolo Buonanno Paolo Pinotti Milo Bianchi
Journal of the European Economic Association, Volume 10, Issue 6, 1
December 2012, Pages 1318–1347, https://doi.org/10.1111/j.1542-
4774.2012.01085.x Published: 01 December 2012
[9] http://carloalberto.it/assets/events/bracco20apr2015.pdf page 8, 9 , L
Onnis - 2015 E Bracco
[10] Understanding the Impact of Immigration on Crime
Jörg L. Spenkuch American Law and Economics Review, Volume 16, Issue 1, 1
March 2014, Pages 177–219, https://doi.org/10.1093/aler/aht017 Published:
13 September 2013
[11] Immigration, regional conditions, and crime: Evidence from an allocation
policy in Germany
Marc Piopiunik and Jens Ruhose February 2017
https://doi.org/10.1016/j.euroecorev.2016.12.004
http://www.sciencedirect.com/science/journal/00142921
[12] Crime and Immigration: Evidence from Large Immigrant Waves Review of
Economics and Statistics Volume 95 | Issue 4 | October 2013 p.1278-1290
Brian Bell, Stephen Machin and Francesco Fasani.
31. [13] Does Immigration Enforcement Reduce Crime ? Evidence from Secure
Communities Thomas J. Miles and Adam B. Cox the Journal of Law &
Economics Vol. 57, No. 4 (November 2014), pp. 937-973
[14] An interview with Miles and Cox (2014) where they discuss their research
http://www.law.nyu.edu/news/ideas/cox-miles-secure-communities
[15] Fasani, Francesco, Immigrant Crime and Legal Status: Evidence from
Repeated Amnesty Programs (November 2016). CEPR Discussion Paper No.
DP11603. Available at SSRN: https://ssrn.com/abstract=2865871
[16] Pffeifer et al 2018 Zur Entwicklung der Gewalt in Deutschland (In
German) https://www.zhaw.ch/storage/shared/sozialearbeit/News/gutachten-
entwicklung-gewalt-deutschland.pdf
[17] Becker, G. (1968) ‘Crime and Punishment: An Economic Approach,' Journal
of Political
Economy, 76, 175-209.
[18] Moritz Rohling ETH Zurich Institute for Environmental Decisions IED for
Values & Regulation in Environmental Economics.
http://webarchiv.ethz.ch/vwl/down/BeningOhndorf/VaRIEE/CandP.pdf
[19] https://www.ined.fr/en/everything_about_population/demographic-facts-
sheets/faq/more-men-or-more-women-in-the-world/#r244
[20] Chapter 23: The Age and Crime Relationship: Social Variation, Social
Explanations Jeffery T. Ulmer & Darrell Steffensmeier In: The Nurture Versus
Biosocial Debate in Criminology: On the Origins of Criminal Behavior and
Criminality DOI: http://dx.doi.org/10.4135/9781483349114.n24
[21] Bell, Brian, Fasani, Francesco and Machin, Stephen (2013) Crime and
immigration: evidence from large immigrant waves. The Review of Economics
and Statistics, 95 (4). pp. 1278-1290. Page 1282 line 15 ISSN 0034-6535 DOI:
10.1162/REST_a_00337
[22] Bell, Brian, Fasani, Francesco and Machin, Stephen (2013) Crime and
immigration: evidence from large immigrant waves. The Review of Economics
and Statistics, 95 (4). pp. 1278-1290. Page 1280 line 19 ISSN 0034-6535 DOI:
10.1162/REST_a_00337
32. [23] Gavrilova, Evelina and Campaniello, Nadia, Uncovering the Gender
Participation Gap in the Crime Market. IZA Discussion Paper No. 8982.
Available at SSRN: https://ssrn.com/abstract=2598922
[24] Do Immigrants Cause Crime ? Paolo Buonanno Paolo Pinotti Milo Bianchi
Journal of the European Economic Association, Volume 10, Issue 6, 1
December 2012, Pages 1318–1347, https://doi.org/10.1111/j.1542-
4774.2012.01085.x Published: 01 December 2012 page 6 line 16.
[25] Immigration, regional conditions, and crime: Evidence from an allocation
policy in Germany Marc Piopiunik and Jens Ruhose February 2017
https://doi.org/10.1016/j.euroecorev.2016.12.004
http://www.sciencedirect.com/science/journal/00142921 page 18 line 36.
[26] http://www.bbc.co.uk/news/world-europe-42557828
[27] Understanding the Impact of Immigration on Crime
Jörg L. Spenkuch American Law and Economics Review, Volume 16, Issue 1, 1
March 2014, Pages 177–219, https://doi.org/10.1093/aler/aht017 Published:
13 September 2013 page 12 line 15.
[28] Finite Sample Bias from Instrumental Variables Analysis in Randomized
Trials Howard S. Bloom Pei Zhu (MDRC) Fatih Unlu (Abt Associates, Inc.)
https://www.mdrc.org/sites/default/files/Finite%20Sample%20Bias%20from%
20Instumental%20Variables%20full%20report.pdf
[29] Sanderson, E. and F. Windmeijer, 2015. A Weak Instrument F-Test in
Linear IV Models with Multiple Endogenous Variables. Journal of
Econometrics (forthcoming). Working paper version: University of Bristol
Discussion Paper 14/644. http://ideas.repec.org/p/bri/uobdis/14-644.html.
[30] Stock, J.H. and Yogo, M. 2005. Testing for Weak Instruments in Linear
IV Regression. In D.W.K. Andrews and J.H. Stock, eds. Identification and
Inference for Econometric Models: Essays in Honor of Thomas
Rothenberg.Cambridge: Cambridge University Press, 2005, pp. 80Ò108.
Working paper version: NBER Technical Working Paper 284.
http://www.nber.org/papers/T0284.
[31] Cragg, J.G. and Donald, S.G. 1993. Testing Identfiability and Specification
in Instrumental Variables Models. Econometric Theory, Vol. 9, pp. 222-240.
33. [32] Understanding the Impact of Immigration on Crime
Jörg L. Spenkuch American Law and Economics Review, Volume 16, Issue 1, 1
March 2014, Pages 177–219, https://doi.org/10.1093/aler/aht017 Published:
13 September 2013 page 14 line 11.
[33] Bell, Brian, Fasani, Francesco and Machin, Stephen (2013) Crime and
immigration: evidence from large immigrant waves. The Review of Economics
and Statistics, 95 (4). pp. 1278-1290. Page 1280 line 19 ISSN 0034-6535 DOI:
10.1162/REST_a_00337 Page 1285 line 16
[34] Understanding the Impact of Immigration on Crime
Jörg L. Spenkuch American Law and Economics Review, Volume 16, Issue 1, 1
March 2014, Pages 177–219, https://doi.org/10.1093/aler/aht017 Published:
13 September 2013 page 22 line 15.
[35] Do Immigrants Cause Crime ? Paolo Buonanno Paolo Pinotti Milo Bianchi
Journal of the European Economic Association, Volume 10, Issue 6, 1
December 2012, Pages 1318–1347, https://doi.org/10.1111/j.1542-
4774.2012.01085.x Published: 01 December 2012 page 14 line 22.
[36] Bell, Brian, Fasani, Francesco and Machin, Stephen (2013) Crime and
immigration: evidence from large immigrant waves. The Review of Economics
and Statistics, 95 (4). pp. 1278-1290. Page 1280 line 19 ISSN 0034-6535 DOI:
10.1162/REST_a_00337 Page 1285 line 26.
[37] Do Immigrants Cause Crime ? Paolo Buonanno Paolo Pinotti Milo Bianchi
Journal of the European Economic Association, Volume 10, Issue 6, 1
December 2012, Pages 1318–1347, https://doi.org/10.1111/j.1542-
4774.2012.01085.x Published: 01 December 2012 page 3 line 5
[38] Understanding the Impact of Immigration on Crime
Jörg L. Spenkuch American Law and Economics Review, Volume 16, Issue 1, 1
March 2014, Pages 177–219, https://doi.org/10.1093/aler/aht017 Published:
13 September 2013 page 16 line 18.