TABLE OF CONTENTS 2 ABSTRACT 1 INTRODUCTION 1 SECTION I: LITERATURE REVIEW 3 1.1. Economic Growth (GDP growth) 3 1.2. Inflation Rate 4 1.3. Population 4 1.4. Income Inequality 5 1.5. Urbanization 5 1.6. Governance 6 SECTION II: RESEARCH METHODOLOGY 7 2.1. Methodology 7 2.2. Model specification 7 2.3. Data description 8 2.3.1. Sources of data 8 2.3.2. Descriptive statistics and interpretation for each variable 9 2.3.3. Correlation matrix between variables 10 SECTION III: RESEARCH RESULTS AND IMPLIACATION 12 3.1. Choosing the Estimated Model 12 3.1.1. Breusch Pagan test 12 3.1.2. Hausman test 13 3.2. Diagnosing the problems of the model 14 3.2.1. Testing for multicollinearity 14 3.2.2. Testing for heteroskedasticity 14 3.2.3. Testing for serial correlation 15 3.2.4. Testing for crosssection correlation 15 3.3. Fixing the model 16 3.4. Hypothesis testing 16 3.4.1. Test the overall significance of the observed multiple regression 17 3.4.2. Test the individual significance: 18 3.5. Result analysis and implications 19 CONCLUSION 21 REFERENCES 22 APPENDIX 25
https://tailieuluatkinhte.com/ FOREIGN TRADE OF UNIVERSITY FACULTY OF INTERNATIONAL ECONOMICS - MIDTERM ASSIGNMENT Module: Econometrics II factors affecting crime rates in g20 countries from 2010 – 2020 Group: Class: KTEE.318 Instructor: Prof Dinh Thi Thanh Binh Ha Noi, June 2023 Econometrics II KTEE Group TABLE OF CONTENTS TABLE OF CONTENTS ABSTRACT INTRODUCTION SECTION I: LITERATURE REVIEW 1.1 Economic Growth (GDP growth) 1.2 Inflation Rate 1.3 Population 1.4 Income Inequality 1.5 Urbanization 1.6 Governance SECTION II: RESEARCH METHODOLOGY 2.1 Methodology 2.2 Model specification 2.3 Data description 2.3.1 Sources of data 2.3.2 Descriptive statistics and interpretation for each variable 2.3.3 Correlation matrix between variables 10 SECTION III: RESEARCH RESULTS AND IMPLIACATION 3.1 Choosing the Estimated Model 12 12 3.1.1 Breusch - Pagan test 12 3.1.2 Hausman test 13 3.2 Diagnosing the problems of the model 14 3.2.1 Testing for multicollinearity 14 3.2.2 Testing for heteroskedasticity 14 3.2.3 Testing for serial correlation 15 3.2.4 Testing for cross-section correlation 15 3.3 Fixing the model 16 3.4 Hypothesis testing 16 3.4.1 Test the overall significance of the observed multiple regression 17 3.4.2 Test the individual significance: 18 3.5 Result analysis and implications 19 Econometrics II KTEE Group CONCLUSION 21 REFERENCES 22 APPENDIX 25 ABSTRACT This research paper investigates the factors influencing crime rates in G20 countries from 2010 to 2020 using an econometric model The study examines the relationship between crime rates and independent variables including GDP growth, inflation, population, income inequality, urbanization, and governance Based on a dataset of 363 observations and using the Stata software, the analysis provides valuable insights into these relationships Understanding the determinants of crime is essential for governments and policymakers in developing effective crime prevention strategies The literature review explores the complexities surrounding crime rates and various economic, demographic, and societal factors While economic growth demonstrates a weak correlation with crime rates, inflation is found to have a positive association, potentially incentivizing criminal behavior Moreover, population size, particularly in urban areas, is consistently linked to higher crime rates However, the impact of income inequality and governance on crime rates remains inconclusive The regression analysis results reveal several significant findings GDP growth, population size, urbanization, and inflation exhibit statistically significant relationships with crime rates However, income inequality and governance not demonstrate significant effects These findings contribute to our understanding of the complex relationships between economic and demographic factors and crime rates in G20 countries Keyword: Crime rates, G20, growths Econometrics II KTEE Group INTRODUCTION Crime rates continue to be a pressing concern for governments worldwide, as they pose significant threats to societal well-being, economic development, and public safety Understanding the factors that influence crime rates is crucial for formulating effective policies and strategies to combat crime and maintain social order This research paper delves into the exploration of the factors affecting crime rates in G20 countries from 2010 to 2020, employing an econometric approach to estimate the relationships between various independent variables and the dependent variable, crime rate The independent variables include GDP growth, inflation, population size, income inequality, urbanization, and governance By examining the relationships between these factors and crime rates, this research aims to contribute to the existing body of literature on the topic, shed light on the complexities surrounding crime rates, and provide insights that can inform evidence-based crime prevention strategies The independent variables include GDP growth, inflation, population size, income inequality, urbanization, and governance The literature review reveals that the relationship between economic factors and crime rates is not straightforward Economic growth, as measured by GDP growth, demonstrates a weak correlation with crime rates, with some studies suggesting that periods of rapid economic growth may contribute to an increase in crime rates This phenomenon may be due to technological advancements and easier access to communication methods, which can facilitate criminal activities Furthermore, a materialistic way of life associated with economic prosperity may lead to a decline in moral values, potentially influencing crime rates Inflation, another economic variable, has been found to positively associated crime rates Studies indicate that inflation weakens purchasing power, reducing the quality of life and incentivizing individuals to engage in criminal behavior to supplement their resources Conversely, a decline in inflation during periods of economic growth has been associated with a decrease in crime rates Population size, particularly in urban areas, has consistently been linked to higher crime rates Larger cities tend to experience higher crime rates compared to smaller cities, with the relationship potentially following a linear pattern The growth of urban areas facilitates social contacts, both positive and negative, depending on the formal Econometrics II KTEE Group structure of the population Consequently, urbanization is regarded as a contributing factor to increased crime rates Income inequality has been a subject of debate concerning its impact on crime rates Some studies suggest that economic inequality fosters an environment conducive to criminal behavior, while others argue that the relationship between income inequality and crime rates depends on cultural contexts and perceptions of inequality as unjust Governance plays a crucial role in addressing crime and maintaining social order Strong state institutions and effective governance mechanisms are associated with lower crime rates, while poor governance, corruption, and weak state institutions are linked to higher levels of crime Empirical studies have identified indicators of poor governance, such as the rule of law and legitimacy, as significant independent factors influencing crime rates across different countries To examine the relationships between crime rates and the aforementioned independent variables, an econometric model was constructed using Stata software The analysis utilized a dataset comprising 363 observations from G20 countries over the period from 2010 to 2020 The regression analysis, employing the Driscoll-Kraay standard errors method, generated coefficients and standard errors for each independent variable The statistical significance of these coefficients was assessed using t-statistics and p-values, while 95% confidence intervals provided a range within which the true population coefficients are likely to fall About the structure of the research report: The essay is made with three main sections: Section I: Literature Review Section II: Research Methodology Section III: Research Results and Implication Econometrics II KTEE Group SECTION I: LITERATURE REVIEW As old as humanity, crime has grown to be a significant issue for governments Therefore, stopping illegal activities is a key goal for the government Because a proper understanding of these is required to prevent crime, the causes of crime have also been extensively investigated According to the economic model tradition, the majority of the important variables influencing crime are thought to be economic, including people's level of education, income, income inequality, unemployment, rate of urbanization, age of the population, gender ratio, labor force participation rate, number of laws, security expenditures, and criminal records The impact of socioeconomic and demographic factors on crime rates has been examined in numerous empirical studies Different time periods and nations have been taken into consideration, along with a variety of methodological approaches This could help to explain why there isn't much agreement on the effects of these factors in the literature—there are even some results that are at odds with one another 1.1 Economic Growth (GDP growth) Detotto and Otranto (2010) stated that criminal activity acted as a tax on the entire economy, lowering the competitiveness of businesses, discouraging investments, and reallocating resources, leading to uncertainty and inefficiency These factors all had a negative impact on economic performance Li et al (2018) analysis included using SOM on crime in Japan from 1926 to 2013 to cluster and facilitate practical comparison between different historical periods with GDP growth rate and criminal features They found that there were only weak correlations between GDP growth rate and crime rates, but ultimately, they stated that the relationship between crime and economic development had been considered complicated Fajnzylber et al (2002) suggested that the widespread adoption of technological advancements and easy access to more sophisticated communication methods during periods of rapid economic growth could result in an increase in crime rates Mauro and Carmeci (2007) empirically explore the link between crime, unemployment, and economic growth using Italian regional data Using a standard overlapping exogenous growth model, they found transitional negative effects of unemployment and crime on income growth and permanent income level effects Econometrics II KTEE Group 1.2 Inflation Rate The rate of inflation is also a significant factor in determining crime However, despite its importance, it is frequently left out of studies of crime Studies by Teles (2004), and Tang and Lean (2007) that looked into inflation as a potential factor in crime are among the exceptions As inflation weaken purchasing power, individuals may purchase lesser goods with a given income This could potentially lower their quality of life and encourage them to turn to crime as a means of obtaining additional resources As Teles (2004) noted, it was evident that income encourages criminal involvement because it affected preferences for crime and because inflation reduced real income Similarly, Deadman and MacDonald (2002) discovered in a study conducted in the USA that a decline in inflation was associated with a drop in crime rates during a period of sustained economic growth In Ralston (1999) research in the USA, he provided evidence in favor of the theory by demonstrating a causal link between inflation and crime rates A similar positive relationship between inflation and crime rates as well as a long-term cointegration between them was discovered by Tang and Lean (2007), who also looked at the USA Inflation, according to Seals and Nunley's 2007 study, is a significant contributor to crime In particular, they discovered a correlation between inflation and crime rates in the 1960s, 1970s, and 1990s According to Tang's 2009 study of Malaysia, crime was significantly influenced by inflation from 1970 to 2006 in the country A co-integration test conducted as part of the same study found a long-term connection between inflation and crime Similarly, inflation and poverty have a longterm relationship with crime, according to Gillani et al (2009), who conducted a similar analysis of Pakistan 1.3 Population The population also plays a role in determining the crime rate, several studies had discovered small, favorable correlations between population size and crime rate Blau (1977) also proposed a connection between population size and crime, based on the straightforward premise that social associations required opportunities for social contacts He postulated that there was a good chance that the population size and crime relationship was linear According to Chamlin and Cochran (2004), population size had no impact on any of the equations relating to crime rates But regardless of the functional form looked at, population size had a big impact on how many violent and property crimes there were Population growth facilitates all types of social contacts, Econometrics II KTEE Group from the most beneficial to the most harmful, according to the formal structure of the population, which asserts categorically (Mayhew and Levinger 1976; Blau 1977) Although the relationship between population size and crime rate might seem to be insignificant (Nolan, 2004), it may actually be statistically significant The factors influencing crime rates in the EU-15 countries from 2000 to 2007 were examined by Lauridsen et al (2013) The study concentrated on the rate of inflation, educational attainment, earnings, and employment The findings revealed that while inflation rate, employment potential, and the urban population had positive effects on crime 1.4 Income Inequality Stven Stack (1984) delves into the interaction between inequality and variables that are believed to contribute to a perception of inequality as unjust The core argument posits that the extent to which inequality affects crime rates is contingent upon a contextual element: a culture that fosters radical egalitarianism and condemns inequality The study analyzes data on property crime across 62 nations In a recent study, Eran Itskovich (2023) presented a novel explanation for a positive correlation between economic inequality and criminal activity, grounded in the social resistance framework The hypothesis suggests that economic inequality causes individuals to feel disconnected from societal institutions and values, leading them to resist these structures through criminal activity Through survey data from Israel, they tested this theory on two distinct types of crime and applied structural equation modeling to validate our results Their findings provide preliminary evidence that economic inequality fosters a fertile environment for criminal behavior by promoting resistance to fundamental societal values and institutions 1.5 Urbanization The correlation between crime and city size is a well-established fact that has been recognized by social observers for quite some time Criminologists have extensively discussed the tendency for urban areas to experience higher levels of criminal activity Denis A Ladbrook (1988) conducted a study using cross-sectional Japanese data from 1970 to explore why conventional crime rates are more prevalent in urban areas than in rural areas Through his research, Ladbrook identified three sociological explanations for this phenomenon Firstly, he attributed the higher rates of urban crime to the degree of urbanization and population density Secondly, he noted that urban populations experience greater rates of migration and population growth, Econometrics II KTEE Group which can contribute to the increase in crime Lastly, Ladbrook pointed out that the demographic structures of urban and rural areas differ, with urban areas having a higher proportion of young people who may be more likely to engage in criminal behavior These findings suggest that urban environments may present unique challenges when it comes to reducing crime rates Ajaz Ahmad Malik (2016) stated that urbanization can be advantageous as it allows for economies of scale, leading to the growth and development of industries from an economic perspective However, from a social standpoint, urbanization has been linked to an increase in crime rates in large cities and urban areas While urbanization is not solely responsible for the rise in crime, there are various other factors closely associated with it that contribute to this trend 1.6 Governance Since the 17th century, Thomas Hobbes has long argued that a well-functioning state can prevent criminal and violent behavior Hobbes’s political philosophy is based on the idea that government is primarily a device for ensuring collective security and political authority is justified by a hypothetical social contract among the many that vests in a sovereign person or entity the responsibility for the safety and well-being of all In the modern days, political scientists and economists, particularly from the New Institutionalist School (World Bank 2011; UNDP 2013; Acemoglu et al 2012, 2017), have increasingly emphasized the negative connections between bad governance, corruption, violence, and underdevelopment Poorer nations are said to be stuck in a cycle of weak state institutions, underdevelopment, and high levels of crime and corruption The role of countries' levels of 'governance' in relation to violence and crime has been highlighted in numerous empirical studies on current crime levels Studies on homicide rates across different countries have identified indicators of poor governance, rule of law, and legitimacy as significant independent factors (LappiSeppälä and Lehti 2014; Karstedt 2015; Stamatel 2016; Huebert and Brown 2019) Econometrics II KTEE Group SECTION II: RESEARCH METHODOLOGY 2.1 Methodology In this research, we use the quantitative approach to analyze the factors affecting Crime rates The OLS estimation method is applied to estimate the effects of independent variables on a dependent variable Our group processed the raw data and ran the test using Stata/MP 15.0 software Data Processing The data used is secondary data which is taken from the World Bank and Fragile State Index We choose to use the panel data with a combination between time-series 2010 to 2020 and cross-sectional observations of G20 countries Due to the data available problem, there are 34 countries chosen from G20 Reach unlikeliness for multicollinearity to happen between independent variables This will let the result of estimation of parameters to be more precise The data includes: Gross Domestic Product Growth, Inflation rate, Population size, Income Inequality, Urbanization, and Governance We implement several data researching and collecting steps and make it into a data table in Excel used for analysis, hence the total number of observations is 363 2.2 Model specification We suggest the following population regression model: crimerate = β0 + β1 gdp + β2 pop + β3 urban + β4 inflation + β5 equality + β6 legitimacy + ui Where in: ● β0 : constant coefficient ● βi : correlation coeficient ● ui : residuals The variables list and the expected signs are summarized in the table below Table Explanation of variables