Examining the Linkages between Street Crime and Selected State Economic Variables in Malaysia: A Panel Data Analysis (original) (raw)

The Dependencies of Economic Indicators towards Violent Crime: A Case Study in Malaysia

Crime has become a major phenomenon across the world. Several studies found that macroeconomic variables were related with the occurrence of violent crime. This study attempts to examine the relationship between economic indicators, which is represented by, unemployment, GDP per capita and population density, towards violent crime. Data from the period of 1985 to 2014 have been collected. SPSS program was used for this study to analyze the model suggested. This study reveals that there is a significant relationship for unemployment and population density towards violent crime in Malaysia. In contrast, GDP per capita has no significant relationship towards violent crime in Malaysia. It was found that the violent crime is expected to increase for every one unit increase in unemployment and population density. While, the violent crime is expected to decrease for every increment in GDP per capita. This study also highlighted recommendation to the policy maker and potential subsequent researcher.

Crime and income inequality: An economic approach

Atlantic Economic Journal, 1992

The criminology and sociology literature are rich with scholarly attempts to identify the variables affecting criminal activity, giving particular emphasis to crimes against property. Naturally, special attention has been devoted to the influence of inequality in the distribution of income, since its importance is so intuitively appealing. An excellent study of this type is that of Carroll and Jackson [1983], who used a sample from 93 non-southern cities with a population of over 50,000. Their regression results demonstrated [p. 186] "that inequality has strong causal effects on crime rates." In their introductory survey of the literature, Caroll and Jackson [p. 179] also point out that "recent research tends to the conclusion that it is inequality rather than poverty that is the crucial variable." As they correctly point out, this argument was very strongly made by Braithwaite [1979, p. 211], who after an exhaustive review of the literature concluded that while the evidence regarding the effect of poverty on crime rate is inconsistent, "the literature shows fairly uniform support for a positive association between inequality and crime .... " This relationship, first noted by Eberts and Schwirian [1968], is generally accepted today. Surprisingly, this positive association seems to hold true only for the U.S. In a cross-national study of 62 countries, Stack [1984, p. 247] concludes that, "the best estimates in the analysis offer no support for the hypotheses on property crime and inequality. The results of research based predominantly on American samples are not replicated for a large set of nations with varied cultural and institutional frameworks." The most significant variable affecting crime rates according to Stack was the total level of wealth of the society or what he termed the degree of economic development [p. 246]. "Again, however, a control variable does most of the explaining of the variance in crime: the level of economic development." The importance of the total level of wealth in explaining property crime within the U.S. itself is also pointed out by Jacobs [1981]. He finds [p. 23] that, "if we look at the effects of particular variables we find that the indicator with the most consistent effects was the measure of economic development." And he shows once again that (in contrast to cross-national data) for the U.

Crime and Income Inequality: The Case of Malaysia

Journal of Politics and Law, 2009

This paper examines the causality between income inequality and crime in Malaysia for the period 1973-2003. Autoregressive Distributed Lag (ARDL) bounds testing procedure is employed to (1) analyze the impact of income inequality on various categories of criminal activities as well as to (2) analyze the impact of various categories of criminal activities on income inequality. Interestingly our results indicate that income inequality has no meaningful relationship with any of the various categories of crime selected, such as total crime, violent crime, property crime, theft and burglary. Crime exhibits neither long-run nor short run relationships with income inequality and they are not cointegrated. It cannot be denied that there is ambiguity in the empirical studies of crime economics regarding various income variables leading to often mixed and contradicting results, which might be a good explanation of this finding.

Theoretical and empirical work on the relationship between unemployment and crime

2001

Part of the ongoing debate between Cantor and Land and Greenberg centers on differing opinions about the question of interest in . We begin this article with our opinion that Cantor and Land's theory relates changes in the business cycle to changes in the aggregate rate of crime. We then question whether year-to-year changes adequately reflect changes in the business cycle, which last on average 4 years, and we refer to an article by which presents an alternative method of measuring business cycle changes. We also discuss how Greenberg's use of cointegration provides an alternative way of addressing the difficult statistical problem of nonstationarity without resorting to first differences. Our final contribution involves noticing that opportunity and motivational theories of crime can be structurally identified by focusing on different types of crime rather than temporal lags. We demonstrate this idea by splitting car theft into joyriding and theft for profit. We show that joyriding appears to be driven by opportunity, while the causal structure of theft for profit is less clear.

Crime and Unemployment in Malaysia: ARDL Evidence

The purpose of the present study is to determine whether there is long-run relationship between crime rates and unemployment rate in Malaysia for the period 1973 to 2003. The autoregressive distributed lag bounds testing procedure was employed as the main tool to infer cointegration or the long-run relationship between unemployment and the crime rates. The results indicate that the unemployment rate, and crime rates: total crime rate, violent crime (murder, robbery, and assault), and property crime (daylight burglary, night burglary, and

Relationship between Crimes and Economic Conditions in Pakistan: A Time Series Approach

Using the time series data from 1990-2011, this paper is an attempt to explore the relationship between economic conditions and criminal activities in Pakistan. Three variables are being used for economic conditions like increasing female employment in labor market, CPI which denotes inflation and income inequality. We check their relationship with total reported crime with reference to Pakistan. The Augmented Dicky Fuller test is used for unit root process which suggested that all the variables are stationary at the 1 st level. For the long run relationship Johanson-Cointegration technique has been applied. After statistical procedure results suggested that female employment, inflation and the Gini index are strongly related with crime. Coefficient of Gini index is high which means that in long-run income inequality affects the crime more than other two because income inequality is a long-run phenomenon so it affect the criminal activities with great magnitude. Vector Correction Model (VCM) has been applied to check the short-run relationship between variables. VCM results suggested that the model we estimate is divergent. Divergent model mean that there is no adjustment from long-run to short-run between variables as they are going away from equilibrium. If we increase the lag length, the model can become divergent but due to crime data unavailability it was difficult to increase the observations and the lags as well. Research paper gives evidence that economic conditions have significant impact on crimes and increasing female employment which is considered as labor market improvement is positively related with crime in Pakistan. This relationship may be the result of market imperfections. Moreover inflation by decreasing purchasing power and income inequality by increasing the gap between social classes is also the cause of increasing crime and they contribute significantly in crime situation of country.

Income Inequality Impact on Crime in Indonesia : Static and Dynamic Analysis During 2007-2011

Indonesia experienced a tremendous surge in crime over the last few years. I tries to explain the evolution of this crime to determine the main economy cause. I use regression analysis with panel data set of 33 provinces in Indonesia in the range of 2007 to 2011. The primary crime categories studied are several kind of property crime and aggregate violent crime. The incidence rates of these crimes are regressed against the gini coefficient and an array of other explanatory variables. First, I describe the relationship between inequality and crime with a pooled ordinary least squares (OLS) model. Secondly a fixed effects model is used to control for possible omitted variable problem in simple OLS regressions. Finally, I employ a generalized method of moments (GMM) model in order to address the issues of criminal dynamics and endogeneity of regressors. My results confirm that every kind of crime in Indonesia have some similarity and difference also in its explanatory variables. Inequality and urbanization (fraction of population live in urban area) is criminogenic factor for almost all kind of crime. Government policy also has a direct impact on the variation of crime. In this model, 2008 set as dummy, there is oil subsidy reduction policy, resulting in enhancing every type of crime rates. Household headed by female its likely push violent that lead to increasing homicide rate. For robbery, which usually related for young’s (male) deliquency behaviour, school participation play important reducing factor (protective effect). I also find another interesting fact that gini's relation on crime rate takes an U invers-shape. It means after reach a given treshold value, impact of the worsening income distribution on crimerate is decreasing. It’s works in every kind of crime with various threshold of gini.

How Economic Indicator Drive Crime? Empirical Study in Developing Country, Indonesia

International Journal of Economics and Financial Issues

More offenses are committed in society as the crime rate rises, which is a sign that the safety of society is deteriorating. The purpose of this research is to examine how the Gini ratio, per capita income, unemployment, poverty, and population density affect the number of crimes committed in Indonesia between 2012 and 2020 in 34 Indonesian provinces, this research used secondary data from the years 2012 until 2020. Multiple linear regression using a panel data approach is the study methodology. The study's findings demonstrate that the fixed effect approach is the most effective. Unemployment had a positive and significant impact on the amount of crimes, according to partial testing, while overcrowding and internet access had a negative and significant impact. In Indonesia between 2012 and 2020, the Gini ratio, per capita income, and poverty did not affect crime, according to partial testing, which also found that unemployment had a positive and significant impact on the number...

The Effects of Unemployment and income on Crime: a Panel Data Analysis on Turkey

Today, the reason of the crime is a subject, that attract much attention from the researchers. It is accepted that the growth of the crime rates damages population by psychologically and economically. The issue of the relation between crime rates and economic variables is a famous hypothesis. Therefore the effect of the economic variables on the crime rates is the subject of many academic researches. But, research made in different countries and with different methodologies shows inconsistent results. Especially in the growing countries like Turkey, which economic parameters change rapidly, the investigation of the relation between crime and economic variables gives a different point of view about this issue. The objective of this paper is to analyze the relation between, various crime types and economic variables, unemployment and gross domestic product per capita. The second purpose of this paper is to determine the direction of the causality. The data set used in the study is the crime rates, unemployment and gross domestic product per capita series between 1990 and 2010.