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When examining crime patterns over time, particularly gender convergence, it is useful to unpack the data with what other measurement and social, political, economic factors might be affecting any changes found over time or that differ across groups over time (e.g., gender differences, in our case). Step 1 is to examine the gender convergence patterns, even if “only” using official, say UCR, data, to determine whether it is because (1) male rates are decreasing at a faster pace than female rates(are decreasing), or (2) male rates are increasing at a slower rate than female rates (are increasing). Steps 2 and 3 are much more difficult. Step 2 stresses that using police data to assess gender–crime patterns is problematic given how many crimes are unreported and unknown to the police, that reporting to the police varies by the type of crime (e.g., gender-based abuse crimes are some of the most underreported), and arrest data may be a better indication of police disproportionately targeting and privileging individuals (e.g., racial profiling, policing neighborhoods differently, etc.) than the actual offending rates. Most gender gap crime research uses U.S. UCR (arrest) data, although NIBRS is increasingly used. Step 2, then, is to compare arrest rates (and other CLS-generated data, such as court convictions) with self-reported offending and/or victimization data (e.g., NCVS) for the same period. Indeed, most criminologists agree that self-report data are a far more accurate measure of the actual crime rate than arrest data, given the numerous crimes unknown and unreported to the police. Also, people are quite honest and consistent in their self-reports of offending (W. Pollock, Hill, Menard, & Elliott, 2016).

Step 3 is accounting for potential economic, social, and/or policy changes happening that might affect offending patterns and, for the purposes of this chapter, do so in a gendered way. Stated alternatively, when there is gender convergence documented by official statistics, it is important to assess how much is offender-generated (individuals committing more or fewer crimes) and how much is due to economic changes such as the feminization of poverty (increasing women’s “survival” offenses) and to changes in the behavior of social control agents and policy, also referred to as “law in action” (Harmon & O’Brien, 2011; J. Schwartz & Rookey, 2008; J. Schwartz, Steffensmeier, & Feldmeyer, 2009). A key aspect of Step 3 has to do with net widening—CLS policies or practices that define and include more behaviors as offenses. Given that net widening typically involves including more minor behaviors as delinquent or criminal, and women and girls disproportionately commit the more minor offenses, net widening is likely to result in a higher percentage of female than male entanglement in the CLS, indicating gender convergence when actual offending has not changed but responses to it have (Harmon & O’Brien, 2011; Javdani et al., 2011; J. Schwartz & Rookey, 2008).

In contrast with the WLEH, most research addressing gender–crime patterns, until the 1990s, reported a strong tendency toward gender stability, that is, finding women’s crime rates basically stayed the same except in the areas of less serious property crimes and drugs (Boritch & Hagan, 1990; Canter, 1982; Chilton & Datesman, 1987; Giordano, Kerbel, & Dudley, 1981; Kruttschnitt et al., 2008; Leonard, 1982; Naffine, 1987; Steffensmeier, 1993; Steffensmeier & Cobb, 1981; Steffensmeier & Steffensmeier, 1980; Steffensmeier & Streifel, 1992). An exception is a UCR study of youth arrest rates during the 1960s and early 1970s that found the larceny offenses by girls increased by 250%, which the authors attributed to “baby boomers” hitting the high-risk offending ages (Chesney-Lind & Shelden, 1992).

Studies published since the 1990s using arrest data (primarily the UCR) are mostly consistent with gender convergence; that is, evidence showed an increasingly larger percentage of arrests among girls and women (e.g., Brener, Simon, Krug, & Lowy, 1999; Chesney-Lind & Belknap, 2004; Kaufman, 2009; Lauritsen, Heimer, & Lynch, 2009; Marcus, 2009; Rosenfield, Phillips, & White, 2006; J. Schwartz et al., 2009; Steffensmeier & Schwartz, 2004; Steffensmeier, Schwartz, Zhong, & Ackerman, 2005; Steffensmeier, Zhong, Ackerman, Schwartz, & Agha, 2006; T. Stevens, Morash, & Chesney-Lind, 2011). According to UCR data, girls and women were 10% of arrests in 1965, 14% in 1980, 16% in 1990, 20% in 2000 (Steffensmeier & Schwartz, 2004), 26% in 2011, and, as we can see in Table 4.1, 30% in 2018. In other words, between 1965 and 2018, women and girls’ percentage of all arrests increased 270%. However, given that this was only a 1% increase in the last decade, the convergence could be leveling into stability. At the same time that we acknowledge indications of gender convergence, it is also necessary to remember that for the 28 offenses listed in Table 4.1, all were solidly male-gender-related except larceny-theft (approaching-male-gender-related for combined ages), embezzlement (gender-neutral for combined ages), prostitution and commercialized vice (female-gender-related for combined ages, approaching-male-gender-related for youths), and liquor law violations (approaching-male-gender-related, but only for youths).