The number and intensity of crimes in our society is increasing at such a rate that many small non serious crimes are considered normal. Our minds -individually and collectively- are occupied by two types of thought process at each point in time. First, every one of us thinks and plans (legally or illegally) about how to get rich. The second thought that occupies our minds is how to safeguard ourselves and our belongings from being targeted by the criminals. The current research endeavor is an effort to understand the increasing trends of both of our concerns that have deep rooted connections with our personal, social and economic life. The study of crime in economics is not new. The principles of economics have been utilized to understand crime even before Adam Smith, but it was Becker (1968) who popularized the economist’s tool-kit for explaining crime. The economist’s tool-kit that developed after Becker considers the cost-benefit calculus of individuals contemplating to commit a criminal act as the most important element that can be manipulated -through the criminal justice system- to control crime and criminality in the society. The economists’ tool-kit however, by focusing on the criminal justice system variables, ignores other important influences such as individual differences, social structures and the structure of opportunities and setbacks faced by the individuals. While being within the scope of economics, this study argues that besides the criminal justice system variables, individual differences, social influences and the structure of opportunities and setbacks all are relevant factors for understanding crime and criminality. The theoretical arguments developed in this study are empirically tested by using three different and purposively collected data sets from Khyber Pakhtunkhwa, Pakistan. The first data set is collected from university level students (n = 457) and is utilized for understanding crimes such as academic dishonesty, campus crimes and other relevant crimes. The second data set is collected from ex-prisoners (n = 325) of the Khyber Pakhtunkhwa jails and is utilized for evaluating crimes such as violent crimes, property crimes and other crimes prevailing in this sub-sample. The third and final data set consists of university employees (n = 405) where relevant information on whitecollar crimes, academic dishonesty and other related crimes are collected. The three data sets are analyzed using principle component and regression analysis most suited to the estimation of a count dependent variable. Besides Poisson and Negative Binomial regression models, the study also estimate and report results of ordinary least square regressions which serves iv to check robustness of the results. The results obtained from regression analyses are generally supportive of the theoretical implications of the model.More precisely, the results obtained from the regression analysis of the students’ sample reveals that personal morality, human and social capital are important predictors of academic dishonesty and other campus related crimes. Deterrence variables (both social and legal) are however found to have no influence on the crimes considered for the students’ sample. In contrast to the students’ sample, results of the ex-inmate’s sample reveal that human and social capital and deterrence variables all are important predictors of crimes committed by this sub group, but personal morality has no influence. Similarly, in the university employees’ sample, it is found that legal sanctions, morality, human capital and social capital are important predictors of illegal behavior.Results obtained from empirical analysis of the three sub-samples reveals that different crimes are sensitive to different sets of variables. Hence a one-size-fits-all approach for controlling crime in the country is not an appropriate policy. Rather controlling diverse crimes requires - besides a generic approach based on the criminal justice system - that policy needs to be tailored in accordance with the characteristics of crimes and the criminals.