Crime and economic growth in the United States

Crime and Economic Growth in the United States

Bachelor Thesis

February 2015

Faculty: Faculty of Economics and Business Student Name: Karisa Kandouw

Student Number: 10621350 Specialization: Economics Supervisor: Lin Zhao

TABLE OF CONTENT

1. INTRODUCTION 3

2. LITERATURE REVIEW 5

3. DATA AND METHODOLOGY

1. The Data 8 1.1. Economic Growth 9 1.2. Crime 9 1.3. Population Growth 10 1.4. Human Capital 11 1.5. Innovation 11

1.6. Openness to International Market 12

2. The Model 12

4. RESULTS 14

5. CONCLUSION 20

1. INTRODUCTION

Crime is usually referred to any activities that are prohibited by law. Because the law in a country may not be the same in another, what constitutes as a crime in one place may also not be considered as one in another place. The types of crime range widely, including assault, burglary, kidnapping, rape, and many others. Intuitively, because of its against-the-law nature, crime generates negative impact on the society, both at individual-level and national-level. Brand and Price (2000) did a study on the economic costs of crime and stated that England and Wales were estimated to have a loss of around £60 billion in 1999/2000 because of criminal activities. Moreover, the Australian Institute of Criminology (AIC) estimated that Australia loses almost $36 billion on annual basis. It is about 4.1% of the country’s GDP for the year 2005. Beside that, the costs of crime also reach the non-economic part, such as the psychological trauma, pain, and the reduced quality of life of the victims (Cohen, 2007). Since criminal activities seems to bring quite a lot of disadvantages, therefore, it is important to conduct studies about crime and its effect so that government can implement the right policies to avoid it from happening.

As the third largest country by population, United States has one of the highest crime rates in the world (Blau and Blau, 1982). The US Crime Index State (2015) ranks South Carolina as the state in which crime rate occurs the most over the past years. The country itself, like most others, has been trying to reduce crime as it is perceived to have negative effects. According to Maltz (1977), federal attention to crime statistics began on June 22, 1870, when the Congress established the US Department of Justice. Furthermore, the Federal Bureau of Investigation (FBI) began administering the Uniform Crime Reporting (UCR) Program in 1930. The primary objective is to “generate reliable information for use in law enforcement administration,

operation, and management” (Uniform Crime Report, 2011). Up until today, the program is still running to assess and monitor the crime activities in the US. The crime trend in the country is also interesting. The rate was showing a reverse pattern when it was increasing during the 1980s, but then it was falling in the 1990s (Gould et al, 2002). According to Levitt (2004), the decreasing trend of crime might be because of increased incarceration, more police, the decline of crack and legalized abortion. The question about whether crime in the US affects the country’s economic growth then still remains.

This paper is aiming to contribute to the existing research about crime and economic growth by investigating the relationship empirically, using a state-level annual data from 50 states in the US. The period observed is between 2000-2011. By employing OLS regressions, crime is regressed on economic growth along with several control variables. At the end, a conclusion is drawn in which the effect of crime on growth is statistically significant. It appears that the impact of crime is also quite substantial as the results show that a single crime can lower GDP growth. The research started off with three regressions of each total crime, violent crime, and property crime individually on economic growth, along with several control variables. The control variables included are population growth, human capital, innovation, openness to international market, and economic growth lagged 1 year. The results of those regressions changed when a dummy variable, indicating the financial crisis started in 2008, was added into the regressions. Some variables become significant after that, including the variables of interest, total crime and property crime.

This paper is organized as follows: in the following section, Section 2, some literature and existing research related to the topic will be reviewed. Section 3 will then proceed to describe the

data and methodology used in order to answer the research question of how crime affects economic growth in the US. Lastly, Section 4 and 5 will finally show the results and conclusion along with the limitations of this research.

2. LITERATURE REVIEW

The study of crime had not been much of an interest for economists until the influential work of Becker (1968), Crime and Punishment: An Economic Approach, appeared. In his paper, Becker tried to explain the rationality of criminal behavior in economic sense. He suggested that a person will commit such illegitimate action if the benefits of doing it exceeds the risks. Consequently, the number of research about crime and its relation to the economic world has been growing and its importance towards economic growth has become increasingly recognised. Several studies, such as those of Freeman (1999), Hemley and McPheters (1975), Burnham et al (2004), and Detotto and Otranto (2010), had been conducted since to explore the relationship. Though more empirical research had also been done, the conclusion of how crime affects economic growth has still not been unanimous.

Logically, the more criminal activities, the lower the growth should be. There are many channels in which it can eventually have an effect on growth. In their paper, Brand and Price (2000) stated that crime may bring wide economic distortion. For example, there will be fewer shops and services in a high-crime area, which also means fewer job opportunities. Moreover, the need of government to prevent crime activities, fund the criminal justice system, or work on the victim support services may eventually lead to higher tax. As a consequence, it discourages people on savings and investments. In addition, Josten (2003) also suggested that the increase in criminal

activities may cause a slowdown in investment by making property rights less secure. From the theoretical perspective, these things are expected to slow the process of economic growth. Some empirical research supported this theory of negative association between crime and economic growth. Peri (2004), for example, found a strong evidence that organised crime affects economic development, measured in income per capita growth, negatively. In the research about social variables and economic success, he used the data of 95 Italian provinces within the 1951-1991 period. His conclusion is in line with World Bank Report (2006) in which it examined the costs of crime and violence on development. The report estimated that the direct costs amount to 3-5% of GDP according to several state-level and city-level data of Brazil with the use of accounting methodology. Furthermore, it also estimated that Brazil’s per capita income might have been a few percent higher in the coming years if the rate of homicide had been smaller within 1991-1995. Drawing a comparable result, Kumar’s (2003) work showed that the effect if crime rates (with intentional homicides, robbery rates, and violent crime as proxies) on growth are statistically significant. These findings are consistent with the general intuition of negative associationship between criminal activities and economic performance. Having most of the research agreed with the general intuition, this paper is constructed with a hypothesis of “total crime has an adverse impact on economic growth”.

As mentioned earlier, the relationship between crime and economic growth has not been agreed upon and is still a discussion regardless of the growing number of empirical studies. Though some research have found significant negative correlation between the two, few studies did not really find a conclusive results. For instance, Burnham et al (2006) did a research about the central city crime and suburban economic growth. After analysing the county-level data on 32 selected US states during 1982-1997, they concluded that violent crime does have a strong

adverse impact on income growth. However, property crime seemed to only have a weak impact. Therefore, the overall impact of crime on growth remains unclear. In agreement with Burnham et al (2006), another study conducted by Dettoto & Pulina (2012) derived a result in which various type of crime influences GDP growth differently by applying an Autoregressive Distributed Lags (ARDL) approach. They discovered that only homicides and robberies that deterred growth in Italy, making use of Italy’s 1970-2004 data. Meanwhile, other types of crime, such as thefts and property crime, did not present any effect. Similar ambiguous results are also found in a more recent work by Enamorado et al (2013) where they studied the crime and growth convergence in Mexico. The result of employing the country’s data at municipal level from 2005-2010 showed that such negative effect of crime on growth is only occurred with drug-related crimes. On the other hand, crimes that are unrelated to drugs did not seem to have an effect. Besides, Chatterjee and Ray (2009), using the data of many countries between 1989-2005, found that there is no evidence of a strong and negative impact of either crime on a country’s economic growth rate. From the above-mentioned studies, it is suggested that only certain types of crime that have significant impact on economic growth. As a result, this paper will do a regression on 3 types of crime, which are violent crime, property crime, and total crime in order to see a clear outcome for each type on growth. This strategy also helps to avoid an aggregation bias that usually happens when estimating the total crime as warned by Cherry and List (2002).

Goulas and Zervoyianni (2012) finally brought a light by responding on the different results found by the researchers earlier. According to their paper, the significance of the effect of crime on economic growth depends on macroeconomic uncertainty. Many of the previous studies did not take into account the potential link between crime and the uncertainty (Goulas and Zervoyianni, 2012). By analysing a cross-country panel data of 25 countries over the period

1991-2007, the results suggested that under low macroeconomic uncertainty, higher crime does not necessarily cause any severe impact on growth. On the contrary, increasing crime rate is detrimental to growth of the coming years when the uncertainty of macroeconomic situation is high. Another suggestion on the possible cause of unclear relationship between crime and growth comes from Nair et al (2010). While investigating the complex interconnection between crime, corruption, and economic growth, and found little evidence on the effect of the former on the latter, they also indicated that the problem of the existing literature is that “there is not a universal and comprehensive definition of crime that is used consistently used for all countries”. This might be one of the limitations of cross-country analysis because what is considered as crime in one country could be not considered as one in another. As a consequence, the research would not produce convincing results. For this point, this paper will analyse one country by using state-level data because the definition of crime in every state should be more similar than with other countries.

Based on the aforementioned literature, this paper will investigate the effect of crime on economic growth using a state-level data of the US and will do regressions on different types of crime. The underlying hypothesis of this paper is that total crime has a negative effect on economic growth.

3. DATA AND METHODOLOGY

1. The Data

This paper investigates the link between crime rates and economic growth in the US by making use of state-level data. There are 50 states included, and the annual data used are in the period of

2000-2011. While aiming to do a research based on recent data, the period range was chosen due to the availability of the data itself. Having a twelve-year period data constructed in a panel dataset, the number of observations should be 600. However, there are 3 missing data for the variable Innovation, measured by R&D expenditure, and the number of observation becomes 597 accordingly. Based on several studies about crime and also the determinants of economic growth, the following variables are chosen: population growth, human capital, innovation, openness to international market, and a dummy variable for crisis as control variables. Some variables, such as investment and savings, are not included in the regression due to the data availability. The next part of this section will define all the used variables in details.

1.1. Economic Growth

Economic growth acts as the dependent variable and will be measured by the growth rate of Gross Domestic Product (GDP) per capita. GDP per capita itself is GDP divided by midyear population, in which GDP is calculated as the sum of what consumers, businesses, and government spend on final goods and services, plus investment and net foreign trade (US Bureau of Economic Analysis, 2015). The data for GDP per capita growth was obtained from the US Bureau of Economic Analysis (2015) for each state. The term “growth” suggests that the value of economic growth in year t represents a change of GDP per capita growth between year t and year t-1. The values for this variable for each state are the annual percentage change, while the GDP is measured in the 2009 dollar rate.

1.2. Crime

Crime is this paper’s variable of interest and thus becomes the independent variable. The Federal Bureau of Investigation (FBI) provides a regularly-updated crime statistics and reports of the US.

For the statistics, they divide crime into two big categories, which are violent crime and property crime. Violent crime includes murder, forcible rape, robbery and aggravated assault. Meanwhile, burglary, larceny-theft, arson, and motor vehicle theft are comprised into property crime. As previously mentioned, this paper will implement a solution of aggregation bias and the different effect of different type of crime by running a regression individually on 3 categories of crime, which are violent crime, property crime, and total crime. They will be measured in a rate of population of 100,000 inhabitants. It means that the values of crime used equals to the number of crimes occurred divided by total population of each year and each state, then multiply it by 100,000. A one-year lag of this variable will also be included in the regression as control variable. It means that the value of crime at year t will be replaced by the value of crime at year t-1 for this control variable. The possible limitation for using the data from FBI database is the underreporting of number of crime as pointed out by Nair et al (2010) as it only records the the number of reported cases by law enforcement. There is actually a solution for this since the US has other measure of crime, National Crime Victimization Survey (NCVS), in which it documents the reported and unreported crime. However, it does not provide the data on a state-level. So, this paper stays with the data from FBI.

1.3. Population Growth

Several studies have proven that the increasing size of population has a negative impact on economic growth (Romer et al, 1990; Barro, 1996). High population growth may suggest lower quality of human capital, thus lower economic growth. The Solow growth model also considers population growth as one of the main determinants of long-run improvement of living standards. Therefore, the reduction of population growth has been a concern for neoclassical growth

models. The data was taken from FBI database (2015), which is available alongside the data for crime rates. The value of each state’s year t measures the change in population between year t and year t-1.

1.4. Human Capital

One of the most important determinants of growth according to the fundamental theories is human capital (Barro, 1996; Mankiw et al, 1992). Human capital refers to labour productivity which is affected by their skill and knowledge. For this reason, human capital is usually measured through educational attainment, the number of secondary or tertiary enrollment. It is expected that the higher human capital measured, the higher the economic growth will be. Kwabena et al (2006) indicated the role of tertiary education enrollment on growth and therefore this paper uses the data of each state’s total fall enrollment in degree-granting institutions as a proxy of education. National Center for Education Statistics (2015) provides the necessary data for the enrollment level. The values available are in thousands of people. In the model, the data used is the number of enrollment divided by the population in each state each year.

1.5. Innovation

Innovation has been acknowledged as a major determinant of a country’s economic performance (Griliches, 1992; Ulku, 2004). Innovation and Research & Development (R&D) activities are tightly linked with one another as R&D activities enable the possibility of creating new products or finding new ways of processing that are useful. This paper will use the data of R&D expenditures as a proxy for innovation and the data itself was obtained from the US National Science Foundation (2015). The values are in thousands of US dollar. Unfortunately, there are three missing data for this variable. They are the data for the state of Missouri (for 2009 and

2011) and New Hampshire (2009). In the model, the data used is the expenditure divided by the GDP in each state each year.

1.6. Openness to International Market

Many studies have cited that the openness to international market of a country plays a significant role in stimulating economic growth. It is believed that openness to international market is positively related to economic growth (Sachs and Warner, 1995) as it enables a country to get exposure of new technologies, international education, and new resources. Openness is often measured by the ratio of the sum of imports and exports to GDP. However, since the state-level data for imports in the desired period are not available, only the data of exports are used. The data itself was obtained from the US Department of Commerce (2015). The values are in US dollars. In the model, the data used is then exports divided by GDP in each state each year.

2. The Model

As mentioned earlier, this paper is aiming to find out about the significance of the effect of crime on economic growth, if there is any. This paper uses a panel data set based on a total of 597 observations for every 50 state in the US for several variables from 2000-2011. Note that there are 3 missing data for the variable Innovation. An Ordinary Least Square (OLS) regression is then performed on the data. By employing the control variables described in the previous part, the regression model in this paper follows:

where Y is the GDP growth rate as the dependent variable and CRIME as the independent variable of interest. Added into the regressions are the control variables, which are POP for population growth, HUM for human capital, INNV for innovation, OPEN for openness to international market, and a one year lagged Y. The error term is represented by ε, while i and t indicate which state and the year observed respectively.

Based on existing empirical evidences stated in previous section, the effect of crime on economic growth differs for each type of crime. This paper is then also going to do a regression with two types of crime as categorized by FBI, namely violent and property crime. The following regression models are performed:

(2) Yit= α + β1 . VIOLENTit+ β2 . POPit+ β3 . HUMit+ β4 . INNVit + β5 . OPENit+ β6 . Yit-1+ εt

(3) Yit= α + β1 . PROPERTYit+ β2 . POPit+ β3 . HUMit+ β4 . INNVit + β5 . OPENit+ β6 . Yit-1+ εt

where VIOLENT and PROPERTY indicate the number of cases of each type of crime. All other variables are the same as explained before for regression (1).

The US experienced a financial crisis in 2008 which led to a global financial crisis from that year through to 2012. Therefore, it is suitable to put a dummy variable, CRISIS, which indicates the time when the country was in crisis. The dummy variable is then added into the previous regressions becoming:

(5) Yit= α + β1 . VIOLENTit+ β2 . POPit+ β3 . HUMit+ β4 . INNVit + β5 . OPENit+ β6 . Yit-1 + β7CRISIS + εt

(6) Yit= α + β1 . PROPERTYit+ β2 . POPit+ β3 . HUMit+ β4 . INNVit + β5 . OPENit+ β6 . Yit-1 + β7CRISIS + εt

The results of the regressions mentioned in this section will then be discussed in the next section.

4. RESULTS

This paper employs an OLS regression in order to investigate the effect of crime on economic growth. As mentioned in previous section, three regressions are performed based on the type of crime, which are violent crime, property crime, and total crime. After performing the regressions on those types of crime along with several control variables, the following results are presented:

Table 1

(1) (2) (3)

VARIABLES Economic Growth Economic Growth Economic Growth

CRIME -0.000173

(0.000125)

(0.0942) (0.0902) (0.0943) HUM 0.0229 -0.0333 0.0337 (0.0899) (0.0915) (0.0895) OPEN -0.0146 -0.0242 -0.0154 (0.0190) (0.0187) (0.0192) INNV -0.108 -0.0687 -0.111 (0.0681) (0.0690) (0.0681) Yt-1 0.237*** 0.221*** 0.239*** (0.0389) (0.0390) (0.0389) VIOLENT -0.00200*** (0.000649) PROPERTY -0.000129 (0.000142) Constant 1.445* 1.941*** 1.196 (0.771) (0.701) (0.762) R-squared (overall) Prob > Chi2 0.0732 0.000 0.0848 0.000 0.0715 0.000 Observations 597 597 597

Number of STATES 50 50 50 Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

Note that the statistics already shows that the models are statistically significant regardless of the small value of R-squared. In model (1), total crime is regressed along with other control variables. The results show that the coefficient for total crime is negative, as expected. It can be interpreted that an increase of 1 unit of total crime will lead to a decrease of 0.000173 on economic growth. This is, however, statistically insignificant. The results in model (2), when violent crime is regressed, its coefficient is statistically significant at 1% level, indicating that an increase of 1 unit will lower economic growth by 0.00200 unit. Moreover, model (3) reveals that the effect of property crime on economic growth is smaller than violent crime and insignificant, similar to total crime. This result is actually quite in line with the research done by Dettoto and Pulina (2009) and Burnham et al (2006) where they suggested that property crime only have weak impact on economic growth. So, in general, the results from model (1)-(3) are not really able to support this paper’s hypothesis, which is crime rates affect economic growth negatively. While the signs of directions of the variables are negative, the effects are not statistically significant for total crime and property crime. On the other hand, the effect of violent crime is significant at 1% level. Interesting results, though, are shown by the coefficient values of population growth, openness to international market, and innovation. While the coefficient of human capital is positive as expected, the other three variables mentioned before show otherwise. Population growth is expected to be inversely related with economic growth, however the results present a positive value in all three regressions model. Furthermore, innovation and

openness to international market are supposed to support growth, but the values shown are negative. The sources of this problem might be causality bias and omitted variable bias which will be discussed further later in this section.

As pointed out in the methodology section, the US experienced a financial crisis in 2008 which led to a global financial crisis from that year through to 2012. In order to take this crisis into account, a dummy variable, CRISIS, is included in the regressions and the results can be seen in Table 2 below:

Table 2

(4) (5) (6)

VARIABLES Economic Growth Economic Growth Economic Growth

CRIME -0.000481*** (0.000123) POP 0.141 0.0880 0.135 (0.0891) (0.0859) (0.0894) HUM 0.229*** 0.175* 0.246*** (0.0883) (0.0911) (0.0881) OPEN 0.0197 -0.00159 0.0222 (0.0184) (0.0180) (0.0187)

INNV -0.116* -0.0749 -0.126** (0.0643) (0.0657) (0.0644) Yt-1 0.0764* 0.0824** 0.0797* (0.0412) (0.0411) (0.0412) CRISIS -2.207*** -1.954*** -2.221*** (0.259) (0.248) (0.262) VIOLENT -0.00222*** (0.000619) PROPERTY -0.000519*** (0.000142) Constant 1.946*** 1.431** 1.785** (0.731) (0.670) (0.724) R-squared (overall) Prob > Chi2 0.1752 0.000 0.1721 0.000 0.1727 0.000 Observations 597 597 597 Number of STATES 50 50 50

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

From the results above, it can be seen that some changes occur. First of all, the value of R-squared increased, indicating that the model fits the data better. Furthermore, the addition of the dummy variable increases the significance of the effect of total crime, even at 1% level in model (4). The estimation for property crime also becomes significant. Not only that, human capital and innovation becomes significant as well at 1% and 10% level respectively. However, the value of the coefficients of innovation is still negative in all models, contrasting the economic theory and existing empirical evidences. This might be due to the limitations faced by this research that will be discussed in the next paragraph combined with the unavailability of a few data for this variable. The value of openness to international market also changes, becoming positive, while remains insignificant. Identical changes also seem to be present from the results of model (6). Moreover, from model (5), it can be noticed that violent crime remains a significant variable, influencing economic growth negatively. These changes that appear after controlling for the crisis can be related to the study of Goulas and Zervoyianni (2012). They stated that the effect of crime may be more substantial when the macroeconomic uncertainty is high. Besides the effect of this paper’s variable of interest, which is total crime, violent crime, and property crime, some conclusions can also be drawn from the regressions performed related to the other variables. For instance, population growth and openness to international market are not significant enough to be able to affect economic growth. One possible cause is that the proxy for openness to international market that is used is not really complete. Usually, it is defined as the sum of exports and imports in terms of GDP. Despite that, the value of imports is not included as a proxy in the regressions due to the unavailability of the data within the desired period.

Even though the results of the regressions can be interpreted and investigated, it is also important to note that they suffer from several limitations. As suggested by many studies, it is essential to

be aware of the possible causality bias that may occur within the regressions. Causality bias occurs when the dependent variable may affect the independent variable, while the investigation run is actually to find the effect of the independent variable on the dependent variable. In this case, the dependent variable is economic growth and the independent variable is total crime, violent crime, and property crime. Based on many studies and empirical evidences, low/negative economic growth may also cause crime. They are assumed to be negatively related, as the higher the economic growth, meaning higher income and standard of living, the lower the crime rate will be. Kelly (2000) did a study on inequality and crime and concluded that inequality has a strong impact on violent crime. Inequality itself may be a result of a deteriorating economic growth. This similar result is also supported by Burdett et al. (2003) and Verdier and Zenou (2004). Another thing to be considered is the possibility of omitted variable bias. Omitted variable bias appears if one or more variables are not included in the regressions, with two conditions that the omitted variable: 1) is a determinant of the dependent variable and 2) is correlated with the included independent variable (Stock and Watson, 2003). Due to the limited availability of data on state-level, there are some control variables that were intended to be included but eventually put out of the regressions. Those variables include investment and savings.

5. CONCLUSION

This paper intends to examine the effect of crime on economic growth, using the data of the US. After performing several regressions with control variables, such as population growth, human capital, openness to international market, and innovation, the results suggest that, being statistically significant, the effects of total crime, violent crime, and property crime on economic

growth are also quite substantial. In addition, a dummy variable to control the crisis that occurred in the last few years of the observation is also included. The estimations itself are obtained using OLS method with the data observed within the period of 2000-2011. This result is in line with Kumar (2003) and Peri (2004) that found a strong and significant link between crime and economic growth.

Other findings that can be drawn from the results of the regressions are that population growth and openness to international market are not significant in determining economic growth. This is actually opposite of what Romer et al (1990) and Barro (1997) have stated. They suggested that the increase of population growth is an important variable in affecting growth. Similarly, Sachs and Warner (1995) also stressed the significance of openness to international market on growth. However, contradictory results are present in this paper. While interpreting the results from the regressions performed in this paper, it is also important to be aware of several limitations, such as the possibility of causality bias and omitted variable bias as discussed in the previous section.

Further research is definitely encouraged in order to understand the relationship between crime and economic growth fully. Since this paper is using a simple method to determine the relationship, some improvements may include using an Instrumental Variable (IV) regression to reduce the causality bias. Choosing the right instruments for crime is needed to be done carefully nevertheless. Another improvement may be done by including more variables that affect economic growth, such as investment and savings, as many have stated the importance of those variables on economic growth.

All in all, the research done in this paper suggests that the effect of crime is statistically significant in deterring economic growth. The conclusion goes the same way with both violent crime and property crime.

6. BIBLIOGRAPHY

Barro, R. (1996). Determinants Of Economic Growth: A Cross-Country Empirical Study. NBER WORKING PAPER

SERIES, 822-824. Retrieved January 5, 2015, from http://www.nber.org/papers/w5698.pdf

Becker, G. (1974). Crime And Punishment: An Economic Approach. Journal of Political Economy, 169-169. Retrieved January 13, 2015, from http://www.nber.org/chapters/c3625.pdf

Blau, J., & Blau, P. (1982). The Cost of Inequality: Metropolitan Structure and Violent Crime. American

Sociological Review, 47(1), 114-129. Retrieved February 2, 2015.

Brand, S., & Price, R. (2000). The economic and social costs of crime. Home Office Research Study 217. Retrieved January 16, 2015, from

https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/191497/Green_book_supplementary_ guidance_economic_social_costs_of_crime.pdf

Burdett, K., Lagos, R., & Wright, R. (2003). Crime, Inequality, And Unemployment.American Economic

Review, 93(5), 1764-1777. Retrieved January 16, 2015, from http://www.jstor.org/stable/3132151

Burnham, R., Feinberg, R., & Husted, T. (2006). Central City Crime And Suburban Economic Growth. Applied

Economics,36(9), 917-922. Retrieved January 21, 2015, from

http://www-tandfonline-com.proxy.uba.uva.nl:2048/doi/pdf/10.1080/0003684042000233131

Chatterjee, I., & Ray, R. (2009). Crime, corruption and the role of institutions.Indian Growth and Development

Review,73-95. Retrieved January 15, 2015, from

http://www.buseco.monash.edu.au/eco/research/papers/2009/2009crimechatterjeeray.pdf

Cherry, T., & List, J. (2002). Aggregation bias in the economic model of crime.Economics Letters, 75(1), 81-86. Retrieved January 17, 2015, from http://www.sciencedirect.com/science/article/pii/S0165176501005973

Cohen, M. (2007). Valuing crime control benefits using stated preference approaches. Working Paper Number 08 -09, Vanderbilt University Law School, Nashville, TN. Retrieved from

http://ssrn.com/abstract=1091456

Costs of crime. (n.d.). Retrieved January 9, 2015, from

http://www.aic.gov.au/crime_community/communitycrime/costs.html

Crime, Violence and Economic Development in Brazil. (2006). World Bank Report No. 36525-BR. Retrieved January 12, 2015, from

http://www-wds.worldbank.org/external/default/WDSContentServer/WDSP/IB/2007/06/20/000090341_20070620103207/Rend ered/PDF/365250BR.pdf

Detotto, C., & Otranto, E. (2010). Does Crime Affect Economic Growth? Kyklos,63(3), 330-345. Retrieved January 9, 2015, from http://onlinelibrary.wiley.com/doi/10.1111/j.1467-6435.2010.00477.x/pdf

Detotto, C., & Pulina, M. (2012). Does more crime mean fewer jobs and less economic growth? European Journal

of Law and Economics, 36(1), 183-207. Retrieved January 19, 2015, from

http://link.springer.com/article/10.1007/s10657-012-9334-3

Enamorado, T., López-Calva, L., & Rodríguez-Castelán, C. (2013). Crime and growth convergence: Evidence from Mexico.World Bank Policy Research Working Paper 6730, 9-13. Retrieved January 4, 2015.

Federal Bureau of Investigation. Retrieved January 8, 2015, from http://www.fbi.gov/about-us/cjis/ucr/crime-in-the-u.s/2011/crime-in-the-u.s.-2011/fbi-releases-2011-crime-statistics

Freeman, R. (1999). The Economics of Crime. In Handbook of Labor Economics,3c, Chapter 52-Chapter 52. Retrieved January 17, 2015, from http://webspace.qmul.ac.uk/fcornaglia/economics od crime.pdf

Goulas, E., & Zervoyianni, A. (2012). Economic growth and crime: Does uncertainty matter? Applied Economics

Letters, 420-427. Retrieved January 2, 2015, from

http://www.francescobilli.it/rcfea.org.1.0/RePEc/pdf/wp51_12.pdf

Gould, E., Weinberg, B., & Mustard, D. (n.d.). Crime Rates And Local Labor Market Opportunities In The United States: 1979–1997. Review of Economics and Statistics, 45-61. Retrieved January 8, 2015, from

http://people.terry.uga.edu/mustard/labor.pdf

Griliches, Z. (1992), ‘The Search for R&D Spillovers,’ Scandinavian Journal of Economics, 94, pp. s29-s47. Gyimah-Brempong, K., Paddison, O., & Mitiku, W. (2006). Higher Education And Economic Growth In Africa. The

Journal of Development Studies, 42(3), 509-529. Retrieved January 12, 2015, from

http://economics.usf.edu/PDF/higher.education.growth.africa.jds06.pdf

Hemley, D., & McPheters, L. (1975). Crime as an Externality of Economic Growth: An Empirical Analysis. The

American Economist, 19(1), 45-47. Retrieved January 16, 2015, from http://www.jstor.org/stable/25602992

Josten, S. (2003). Inequality, Crime and Economic Growth. A Classical Argument for Distributional Equality. International Tax and Public Finance, 10(4), 435-452. Retrieved January 17, 2015, from http://link.springer.com/article/10.1023/A:1024683431555

Kelly, M. (2000). Inequality And Crime.Review of Economics and Statistics, 82(4), 530-539. Retrieved January 12, 2015, from http://irserver.ucd.ie/bitstream/handle/10197/523/kellym_article_pub_004.pdf

Kumar, S. (2013). Crime and Economic Growth: Evidence from India. MPRA Paper No. 48794. Retrieved January 10, 2015, from http://mpra.ub.uni-muenchen.de/48794/1/MPRA_paper_48794.pdf

Levitt, S. (2004). Understanding Why Crime Fell In The 1990s: Four Factors That Explain The Decline And Six That Do Not. Journal of Economic Perspectives, 18(1), 163-190. Retrieved January 6, 2015, from http://melissa-morabito.wiki.uml.edu/file/view/[Levitt]2004.pdf

Maltz, M. (1977). Crime Statistics: A Historical Perspective. Crime & Delinquency,32-40. Retrieved February 2, 2015, from http://www.academia.edu/1106101/Crime_Statistics_A_Historical_Perspective

Mankiw, N., Romer, D., & Weil, D. (1992). A Contribution to the Empirics of Economic Growth. The Quarterly

Journal of Economics, 107(2), 407-437. Retrieved January 18, 2015, from http://qje.oxfordjournals.org/content/107/2/407.full.pdf

National Center for Education StatisticsU.S. Department of Education, Institute of Education Sciences. Retrieved from http://nces.ed.gov/

Peri, Giovanni (2004). Socio-Cultural Variables and Economic. Topics in Macroeconomics Volume 4, Issue 1, 1- 34. Retrieved from

http://www.degruyter.com/view/j/bejm.2004.4.1/bejm.2004.4.1.1218/bejm.2004.4.1.1218.xml?format=INT

Powell, B., Manish G.P. & Nair, M. (2010). Corruption, crime and economic growth. Handbook on the Economics

of Crime, Edward Elgar Publishing, Massachusetts.

Romer, P. M. (1990). Endogeneous technological change. Journal of Political Economy, Vol. 98, No. 5, 71-102. Sachs, Jeffery D., & Warner, Andrew. (1995). Economic Reform and the Process of Global

Integration. Brookings Papers on Economic Activity, 1-118.

Stock, J. H., and Mark Watson. (2003). Introduction to Econometrics. Boston, MA: Pearson Education.

The National Science Foundation. Retrieved from http://www.nsf.gov/

U.S. Crime Index State Rank. (n.d.). Retrieved January 10, 2015, from http://www.usa.com/rank/us--crime-index--state-rank.htm

Ulku, H. (2004). R&D, Innovation, and Economic Growth: An Empirical Analysis.IMF Working Paper. Retrieved January 12, 2015, from https://www.imf.org/external/pubs/ft/wp/2004/wp04185.pdf

US Department of Commerce Bureaus of Economic Analysis. Retrieved from http://www.bea.gov/

Verdier T., & Zenou, Y. (2004). Racial Beliefs, Location, And The Causes Of Crime. International Economic

Review, 45(3), 731-760. Retrieved January 5, 2015, from