The socioeconomic determinants of crimes rate a country panel evidence

UNIVERSITY OF ECONOMICS HO CHI MINH CITY VIETNAM INSTITUTE OF SOCIAL STUDIES THE HAGUE THE NETHERLANDS VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS THE SOCIOECONOMIC DETERMINANTS OF CRIME RATE: A COUNTRY PANEL EVIDENCE BY NGUYEN DUY MINH MASTER OF ARTS IN DEVELOPMENT ECONOMICS HO CHI MINH CITY, 2013 UNIVERSITY OF ECONOMICS HO CHI MINH CITY VIETNAM INSTITUTE OF SOCIAL STUDIES THE HAGUE THE NETHERLANDS VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS THE SOCIOECONOMIC DETERMINANTS OF CRIME RATE: A COUNTRY PANEL EVIDENCE A thesis submitted in partial fulfilment of the requirements for the degree of MASTER OF ARTS IN DEVELOPMENT ECONOMICS By NGUYEN DUY MINH Academic Supervisor: Dr NGUYEN LUU BAO DOAN HO CHI MINH CITY, 2013 CERTIFICATION "I certificate that the content of the thesis has written by my own knowledge and my own words, not other authors I certify that to best of my knowledge and help received in writing the thesis and all sources used have been acknowledged in the thesis" Signature Nguyen Duy Minh Date: December 2013 i ACKNOWLEDGEMENTS First, I am truly grateful to my supervisor, Dr Nguyen Luu Bao Doan, for his support and his inspiration during the period of my thesis Without his instructions, comments and discussion, I would not have well done this master thesis Second, I would like to thank so much Assoc.Prof Nguyen Trong Hoai, Dr Pham Khanh Nam, Dr Truong Dang Thuy and other lecturers for their professional teaching and encouragement; many thanks Ms Hong, Mr Tam, Mr Huy, Mr Quy for all their support and help Third, my colleagues at MDE 18, they are great They bring to me not just professional learning environment, but also unforgettable memory ii ABSTRACT The study considers the socioeconomic factors that impact on general crime rate and specific crimes rates Since Becker (1968), many researchers have surveyed this topic; however most of them using domestic data source and the findings are discussing This thesis aims to find the social and economic determinants by using global 56 selected countries data set and updated 2003-2010 period While crime variables are collected from the United Nations Office on Drugs and Crimes (UNODC) in 2010, socioeconomic variables are collected from World Development Indicators (WDI), World Bank (WB) in 2011 In order to get the goals of the study, several econometric models are used including Pooled Ordinary Least Squared (Pooled OLS), Fixed Effects Model (FEM) and Random Effects Model (REM) The study has found that (i) GDP per capita growth, urbanization, ratio of labor participation and the ratio of military expenditure are the socioeconomic factors affecting crime rate; (ii) the socioeconomic determinants can explain 46.64% of changing of crime rate; (iii) property crimes, violent crimes and drugs related crimes are explained well by socioeconomic components; but not with sexual crimes In general, the research suggests that the socioeconomic factors are important determinants of crime rate Keywords: Crime Rate, Socioeconomic Determinants TABLE OF CONTENTS Certification Acknowledgements Abstract Table of contents List of main abbreviations List of tables List of figures CHAPTER INTRODUCTION……………………………………………01 CHAPTER LITERATURE REVIEW…………………………………… 07 CHAPTER DATA AND RESEARCH METHODOLOGY……………… 21 CHAPTER RESULTS…………………………………………………… 37 CHAPTER CONCLUSIONS AND POLICY IMPLICATIONS.………….55 Appendices……………………………………………………………………71 References……………………………………………………………………116 LIST OF MAIN ABBREVIATIONS FE Fixed Effects FEM Fixed Effects Model HET Heteroskedasticity LM Lagrange Multiplier Pooled OLS Pooled Ordinary Least Square RE Random Effects REM Random Effects Model STATA STATA software UNODC United Nation on Drug and Crimes WB World Bank WDI World Development Indicators LIST OF TABLES Table 1.1 The percentage of sexual assaulted women (16 years and older)……1 Table 3.2 Expected sign of explanatory variables…………………………….25 Table 3.3 Descriptive statistics……………………………………………… 26 Table 3.4 Correlation matrix of explanatory variables for crime rate……… 28 Table 3.5 Selected specific models from empirical studies………………… 31 Table 4.1 Regression Analysis of crime rate………………………………….37 Table 4.2 Statistical tests among Pooled OLS, FEM and REM………………40 Table 4.3 The regressions of several specific crimes………………………….41 Table 5.1 the ratio of imprisonment of offenders in criminal cases ………….64 Table 5.2 The percentage of several specific crimes………………………….65 Table 5.3 The number of criminal prosecuted offenders in the period 1986-2008…………………………………………………………………… 67 Table A-1: List of 56 selected countries in data sample………………………71 Table B-1: Variables definitions and Data sources…………………………… 72 Table F-1 Chow test (Pooled OLS vs FEM) ………………………………….98 Table F-2 Hausman test (FEM vs REM) …………………………………… 98 Table F-3 Lagrange-Multiplier (LM) test (Pooled OLS vs REM)………….100 Table G-1 Robbery………………………………………………………… 101 Table G-2 Sexual violence………………………………………………… 103 Table G3 Homicide…………………………………………………………105 Table G-4 Drug-related crimes………………………………………………107 Table G-5 Burglary………………………………………………………… 109 Table G-6 Theft………………………………………………………………111 Table G-7 Assault…………………………………………………………….113 LIST OF FIGURES Figure 1.1 The number of offenders per 100,000 habitants in selected countries……………………………………………………………………… Figure 2.1 Analytical framework…………………………………………… 20 Figure 3.1 The crime-age curve……………………………………………….24 Figure 5.1 the level of several specific crimes in Vietnam during period 2006-2012…………………………………………………………………… 63 Figure 5.2 the trend of criminal prosecuted crimes in Vietnam during period 1986-2003…………………………………………………………………… 63 Figure C-1 Histogram of crime rate, convicted……………………………….73 Figure C-2 Kernel density distribution of Theft………………………………74 Figure C-3 Kernel density distribution of Robbery………………………… 74 Figure C-4 Kernel density distribution of Burglary………………………… 75 Figure C-5 Kernel density distribution of Assault……………………………75 Figure C-6 Kernel density distribution of Homicide…………………………76 Figure C-7 Kernel density distribution of Drug-related crimes………………76 Figure C-8 Kernel density distribution of Sexual violence………………… 77 Figure C-9 Scatter diagram of Theft………………………………………….77 Figure C-10 Histogram of unemployment……………………………………78 Figure C-11 Convicted and unemployment rate………………………………78 Figure C-12 Histogram of GDP per capita growth rate……………………….79 Figure C-13 Convicted and GDP per capita growth rate…………………… 79 Figure C-14 Histogram of inflation………………………………………… 80 Figure C-15 Convicted and inflation………………………………………….80 Figure C-16 Histogram subsidies and transfers……………………………….81 Figure C-17 Convicted, and subsidies and transfers rate…………………… 81 Figure C-18 Histogram of income tax……………………………………… 82 Figure C-19 Convicted and income tax……………………………………….82 Figure C-20 Histogram of military expenditure………………………………83 Figure C-21Convicted and military expenditure…………………………… 83 vii Figure C-22 Histogram of internet user ratio…………………………………84 Figure C-23 Convicted and internet user ratio……………………………… 84 Figure C-24 Histogram of school enrollment (secondary)……………………85 Figure C-25 Convicted and school enrollment (secondary)………………… 85 Figure C-26 Convicted and labor participation……………………………….86 Figure C-27 Convicted and labor participation……………………………….86 Figure C-28 Histogram of population growth……………………………… 87 Figure C-29 Convicted and population growth……………………………….87 Figure C-30 Histogram of population density……………………………… 88 Figure C-31 Convicted and population density……………………………….88 Figure C-32 Histogram of urban population………………………………….89 Figure C-33 Convicted and urban population……………………………… 89 Figure C-34 Histogram of fertility rate……………………………………….90 Figure 3.35 Convicted and fertility rate………………………………………90 viii REFERENCES Avakame, E F (1999) Sex ratios, Female Labor Force Participation, and Lethal Violence against Women, Journal of Violence Against Women, Vol 5, No 11, 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Albania, Argentina, Armenia, Australia, Austria, Azerbaijan, Belarus, Bulgaria, Canada, Chile, Colombia, Costa Rica, Croatia, Cyprus, Czech, Denmark, Estonia, Finland, France, Georgia, Germany, Guatemala,... Guatemala, Hong Kong, Hungary, India, Israel, Italy, Japan, Kazakhstan, Kyrgyzstan, Latvia, Lithuania, Mauritius, Mexico, Moldova, Mongolia, Netherlands, New Zealand, Norway, Panama, Poland, Portugal,... International Monetary Fund (IMF), and many Free Trade Area (FTA) In addition, in the case of civil law and criminal law, many countries share the same rules throughout international treaties and agreements,