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    • For theft robbery of persons the

    2018-11-15

    For theft/robbery of persons, the results are that the risk of victimization is 2.9 pp higher for individuals engaged in work outside their home as compared to those who are not. The estimates indicate that each additional year of age reduces the risk of becoming a victim of crimes of this nature by about 0.2 pp. All these results and also the non-significant coefficients observed for the other variables in the model are consistent, including in terms of the marginal effect\'s magnitude, with the results observed in Scorzafave et al. (2011), even though no quadratic term for spending was included. It should be noted that, regardless of the type of theft/robbery, no statistically significant effects were detected for the gender, color, and time variables. Such evidence of insignificance corroborates the little or almost no difference observed in the percentage of individuals victimized conditional on the categories of these variables. However, with regard to the gender of an individual, our expectations, based on common sense, that women are easier victims of theft/robbery of persons were not corroborated. For theft/robbery of persons also, the abacavir sulfate of null effect of the variable for the interaction between gender and age that was not expected initially either cannot be rejected (Table 8). Regarding the household theft/robbery, the non-significance of the coefficients of the dummy variables for gender, color, and time had already been observed in the estimations made by Madalozzo and Furtado (2011). Peixoto et al. (2011) also found a non-significant effect for color and time, but he observed a significant effect for gender. In the various models estimated by Gomes (2011), the coefficient of the dummy variable for time was statistically significant in two only, in which an effort was made to control for an assumed spatial correlation between the households. In Scorzafave et al. (2011), the coefficient estimated for the dummy variable for time was significant at conventional levels of significance, but its magnitude is negligible. In this study, it was seen that gender is not a determinant of victimization risk and that the coefficient estimated for the dummy variable for Race was only statistically significant at 10% level.
    Concluding remarks According to the rational choice theory, there is a clear positive relationship between gains from crime and the number of criminal occurrences, a hypothesis that has been tested empirically using the per capita household income as proxy variable (Mendonça et al., 2002; Santos and Kassouf, 2007; Santos, 2009, and many others). It has often been argued that this variable is associated with both expected gains from and the opportunity cost of crime and, in addition, according to Sjoquist (1973), income incorporates the opportunity cost if the criminal is punished with imprisonment. However, the first effect has prevailed in empirical analyses to such an extent that, in most cases, a positive effect of income on crime has been diagnosed. But the evidence found in this study suggests that it might be more appropriate to control for a possible nonlinear effect of income on crime rates. In other words, based on the findings of this study, we suggest that a quadratic term for the variable that measures household income should be included in the calculations. We believe that this should be done because it determines both expected gains from crime and spending contributing to prevent crime. Due to the lack of appropriate data, private investment to deter crime is not controlled for in the estimations of models in which the crime rate is used as dependent variable. Therefore, including a quadratic term for the variable intended to control for expected gains from crime can be a good alternative to indirectly control for private efforts to deter criminal behavior.
    Introduction The implementation of yield curve forecasting models for the Brazilian case is recent (Lima et al., 2006; Vereda et al., 2008; Vicente and Tabak, 2008; Almeida et al., 2009; Cajueiro et al., 2009). The results show that there is no single forecast model that dominates all competitors. This is due to the fact that different models outperform the others, depending on time horizon ahead, maturity and forecast period.