Examining Sentencing Patterns and Outcomes for White-Collar and Property Crime Offenders

ABSTRACT

Guided by the focal concerns perspective, the authors examine effects associated with sentencing predictors on incarceration and sentence length decisions for offenders convicted of white-collar and different forms of property crime. Using seven years of data (2004–2010) obtained from the Pennsylvania Commission on Sentencing, the authors compare direct and interaction effects of legal and extralegal covariates for white-collar, property economic, and property noneconomic offenders to assess similarities and differences in sentencing outcomes across these crime types. Results indicate more variation exists between white-collar and property noneconomic offenders, particularly in terms of age, race, and gender interaction effects on sentence length decisions. Substantive and theoretical implications are discussed.

KEYWORDS:

• sentencing
• focal concerns
• white-collar crime

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. Some studies have included fraud and forgery (e.g., Steffensmeier & Demuth, 2001; Steffensmeier et al., 1998), but do not distinguish between these low-level white-collar offenses and street crimes.

2. Examples include antitrust offenses and securities crimes (e.g., Weisburd et al., 1990).

3. Federal white-collar crimes tend to be more complex than state-level offenses, but still vary in complexity and organizational connections (Weisburd et al., 2001).

4. As previously stated, the Federal Bureau of Investigation defines white-collar crimes as “those illegal acts which are characterized by deceit, concealment, or violation of trust and which are not dependent upon the application or threat of physical force or violence. Individuals and organizations commit these acts to obtain money, property, or services; to avoid the payment or loss of money or services; or to secure personal or business advantage” (U.S. Department of Justice, Federal Bureau of Investigation, 1989, p. 3). This definition was selected because it focuses on the offense, rather than specific offender characteristics (i.e., high social status).

5. We excluded burglary that occurs in a home with a person present, which is considered a violent crime in Pennsylvania.

6. Missing values were minimal for offense severity, prior record score, guideline edition, and offender age, race, and gender (0.2–3%). However, 14% of cases were missing information on mode of conviction. Because removal of these cases would result in a large amount of information lost, we utilized the “Amelia” package in R (Honaker, King, & Blackwell, 2011) to implement an iterative expectation-maximization algorithm with bootstrapping to substitute plausible values for mode of conviction.

7. Recent work suggests more age categories may be needed to account for heterogeneity in age effects, yet also recognizes the impact on sample sizes (Steffensmeier et al., 2017). Examining additional age groups would limit sample sizes across crime types and offender subgroups. Other work has employed two age categories (Kramer & Ulmer, 2009), but prior research on white-collar crime sentencing suggests different treatment for younger, middle age, and older offenders (Weisburd et al., 1991). Thus, we include three age categories in our analyses.

8. Research suggests that utilizing Z-tests for comparing coefficients across logit models may be inappropriate when residual variation differs across groups (Allison, 1999; Williams, 2009). Subanalysis (not shown) employing heterogeneous choice models (Williams, 2009) indicated no differences in residual variation across crime types. Thus, tests for equality of coefficients were conducted using Z-tests (Brame et al., 1998).

9. As noted in the Results section, we also found many similarities when comparing white-collar offenders and all property offenders, yet these findings are likely influenced by the much large number of property economic offenders included in the sample in the all property model.

Additional information

Funding

This research was supported by an internal grant awarded by the Niagara University Research Council. Arguments presented in this manuscript reflect those of the authors and do not necessarily represent those of Niagara University.