Criminal Justice System Outcomes for Buyers,Sellers,and Facilitators of Commercial Sex in Houston,Texas

Abstract

This study draws upon the economic model of prostitution to explore the relationship between gender, prostitution role, and criminal justice system outcomes. Official court data for 1,027 prostitution arrestees from Harris County (Houston), Texas, were used to differentiate participants by their role in the commercial sex trade (buyer, facilitator, or seller). Logistic regression results indicate that gender differences persist for case dismissal and plea deal acceptance among prostitution arrestees even after controlling for their criminality and role in the prostitution offense. Although gender does not influence the likelihood of conviction, buyers are significantly less likely to be convicted than sellers. Implications from these findings, as well as limitations and suggestions for further research, are discussed.

Keywords

prostitution disparity gender punishment sentencing

Introduction

Criminal justice policies pertaining to the handling of prostitution are changing throughout the United States (U.S.; Walters, 2011). One reason for this transition is that traditional methods of addressing prostitution, such as arresting sellers of commercial sex, have proven largely ineffective at reducing recidivism among this population (Farmer & Horowitz, 2013). Rising concerns about sex trafficking have also influenced U.S. responses to prostitution. Some officials view prostitution and sex trafficking as fundamentally related issues (Farrell & Cronin, 2015; Hedlin, 2017). Consequently, sellers of commercial sex—once considered offenders—are now considered victims. As a result, U.S. policy makers and law enforcement agencies are actively exploring alternative policies to address the needs of individuals arrested for prostitution offenses (Hayes-Smith & Shekarkhar, 2010).

Scholars who view prostitution and sex trafficking as a single issue use the economic principles of demand, supply, and distribution to explain how the commercial sex trade¹ operates (Hedlin, 2017). These economic principles correspond to the three distinct roles individuals can occupy in the commercial sex trade. Specifically, buyers generate demand for commercial sex that is met by sellers who supply sex for money. In some cases, a facilitator² distributes sellers to buyers in exchange for a portion of the profits (Shively, Kliory, Wheeler, & Hunt, 2012). Buyers are considered the driving force behind the commercial sex trade because they generate demand for both prostitution and sex trafficking (Gutierrez, 2014; Yen, 2008). Consequently, U.S. policy makers have increasingly mobilized the criminal justice system against buyers (Hunt, 2013; Monto & Hotaling, 2001). For example, buyers may be arrested through reverse-sting operations, have their vehicles temporarily or permanently confiscated, or participate in court-mandated educational programs (Shively et al., 2012). In some cases, law enforcement initiatives that target buyers have coincided with efforts to divert sellers away from the criminal justice system (Farrell & Cronin, 2015; Stalans & Finn, 2016a). In other cases, the criminal justice system has simply been retooled to target buyers in addition to sellers (Dodge, Starr-Gimeno, & Williams, 2005).

The prostitution literature reveals that sellers are typically women and buyers are typically men (Amnesty International, 2016; Hunt, 2013). As sellers, women have been disproportionately affected by prostitution criminalization throughout U.S. history (Danna, 2012; Ekberg, 2004). At the same time, men have traditionally paid for sex without legal consequences (Heiges, 2009; E. M. Johnson, 2014). This dynamic is well known, but few studies have examined the relationship between gender and criminal justice system outcomes for individuals charged with prostitution within a specific jurisdiction. A recent study by Pfeffer, Ormachea, and Eagleman (2017) is informative because it relies on prostitution data from Houston, Texas, for the years 1977 to 2010 to demonstrate that women were more likely than men to be arrested and sentenced to jail for prostitution during this time period. In contrast, men were less likely to be arrested and more likely to receive a fine and/or probation instead of a jail sentence. Although instructive, the authors’ study is limited by their inability to account for the role participants occupied in the commercial sex trade. Without this measure, it is impossible to disentangle the relationship between gender and prostitution roles. The present study addresses this gap by calculating logistic regression models separately for buyers and sellers of commercial sex to examine whether gender still influences criminal justice system outcomes after controlling for participants’ role in the prostitution offense. Similar to Pfeffer and colleagues’ (2017) study, the present study’s sample consists of 1,027 prostitution arrestees from Harris County (Houston), Texas.

Economic Model of Prostitution

The commercial sex trade is a multibillion-dollar enterprise (Farley & Kelly, 2000; Giusta, 2010). As such, social scientists have highlighted the economic nature of prostitution. This framework contends that the commercial sex trade operates like any other economic endeavor, and that its uniqueness lies in its illegal status rather than its configuration. As with any market, consumer demand for a product (sex) creates opportunities for individuals to supply the product and for facilitators to connect suppliers with interested consumers (Hedlin, 2017; Shively et al., 2012). Thus, the commercial sex trade consists of buyers (johns) who create a demand for paid sex with sellers, who supply sexual services for money. In some cases, facilitators (pimps, madams, or traffickers) may broker sexual encounters between buyers and sellers in exchange for a percentage of sellers’ profits (Pfeffer et al., 2017). The next section explores the roles buyers, sellers, and facilitators occupy in the commercial sex trade in more depth.

Prostitution Actors

Buyers

Although commercial sex encounters involve buyers and sellers, there is a fundamental power imbalance between the two parties. Buyers typically hold the most power because they have excess money to spend on commercial sex. In contrast, many sellers rely on sex-based incomes to meet their basic needs (E. M. Johnson, 2014; Walters, 2011). Accurate estimates of the number of people who buy sex are hard to come by, in part because this population is understudied (Gutierrez, 2014). The little research that does exist can be summarized to reveal four main observations about buyers. First, a substantial portion of any population can be assumed to have paid for commercial sex at least once in their life (Weitzer, 2009). This finding may be partially explained by social norms that legitimize the purchase of sex for pleasure (Ekberg, 2004; Kotsadam & Jakobsson, 2011). Second, the ratio of buyers to sellers is heavily weighted in favor of buyers (Weitzer, 1999). Thus, the commercial sex trade contains more buyers than sellers. Third, individuals who purchase sex lead otherwise conventional lives (Yen, 2008). Finally, buyers generate the demand for sex that sustains the supply and distribution of commercial sex (Gutierrez, 2014; Hunt, 2013). Consequently, law enforcement efforts to reduce prostitution and sex trafficking are most effective when they address this demand.

Sellers

Sellers of commercial sex are a heterogeneous group, which can be divided into “indoor” and “outdoor” classifications based on where they meet buyers. Indoor sellers inhabit brothels, massage parlors, and strip clubs, whereas outdoor sellers attract buyers on the street (Holt, Blevins, & Kuhns, 2014; Sanders, 2004). Many outdoor sellers live in neighborhoods characterized by high levels of concentrated disadvantage and are unable to meet their basic needs through legitimate means (Hunt, 2013). These outdoor sellers are disproportionately exposed to violence from buyers, facilitators, and disapproving members of the public (Armstrong, 2017; Cepeda & Nowotny, 2014). Furthermore, the trauma they experience is often varied and extensive in nature (Nixon, Tutty, Downe, Gorkoff, & Ursel, 2002; Shannon et al., 2009). Indoor sellers face victimization too, but typically to a lesser degree (Farley et al., 2004; Raphael & Shapiro, 2002). Sellers also report being verbally, physically, or sexually victimized by police officers (Almodovar, 2010; Dewey & Germain, 2014; Sherman et al., 2015). The criminalization of selling sex—in conjunction with police misconduct—discourages sellers from reporting their victimization to law enforcement. This renders sellers vulnerable to additional victimization (E. M. Johnson, 2014).

Facilitators

Facilitators of commercial sex (aka pimps, madams, and traffickers) are first and foremost “business people” (Lord, 2014, p. 601). If facilitators cannot profit from commercial sex, they either distribute a more lucrative product or exit the distribution business altogether (Hunt, 2013). Law enforcement efforts to reduce the demand for commercial sex decrease the profitability of the market, thereby deterring some facilitators from distributing sellers. Many sellers operate without a facilitator, however, and recent research suggests that the number of sellers who do so is larger than previously thought (Cohen, 2018). Sellers who work for a facilitator report experiencing greater victimization than sellers who operate independently (Norton-Hawk, 2004).

Two narratives exist surrounding facilitators. The first narrative argues that facilitators recruit vulnerable underage girls through promises of love and financial stability (Ekberg, 2004). After a brief “honeymoon” phase, these facilitators coerce sellers into providing commercial sex through a combination of threats, drug dependency, and violence (Kristof, 2014; Stalans & Finn, 2016b). The second narrative contends that the relationship between facilitators and sellers is more complex. For example, some facilitators may personally victimize sellers, but protect them from buyer- and seller-initiated violence (Nixon et al., 2002). Other facilitators may promote loyal sellers into leadership positions while still requiring them to provide sex (Morselli & Savoie-Gargiso, 2014). Thus, facilitators’ reliance on violence may be overstated (Morselli & Savoie-Gargiso, 2014).

Gender Roles and Prostitution

Commercial Sex Trade Actors

Buyers

Men are more likely than women to consider it morally acceptable to purchase sex (Jakobsson & Kotsadam, 2011). As a result, buyers of commercial sex are predominantly male (Hunt, 2013; Rissel et al., 2017; Yen, 2008). As one scholar observed, “demand has a gender and this gender is male” (Raymond, 2004, p. 1158). Within patriarchal societies such as the United States, gender norms and male entitlement lead many men to assume they can act however they want without consequence. This is especially true when it comes to the ways many men relate to women. For example, the #MeToo and Time’s Up movements have highlighted widespread, systematic efforts by men in the workplace to coerce their female coworkers and employees into sexual encounters (Buckley, 2018; C. A. Johnson & Hawbaker, 2018).

Many women associated with these movements have recounted feeling unable to reject sexual advances from men without endangering their personal safety or career trajectories (C. A. Johnson & Hawbaker, 2018). Scenarios like these occur regularly throughout the United States because men and women in patriarchal societies do not share equal power. Instead, men often possess social and economic capital far beyond that of their female peers. For example, in 2016, the latest year for which data exist, U.S. women were paid 20% less than their male counterparts (American Association of University Women, 2018). Within the commercial sex trade, male buyers use their privileged position in patriarchal societies—and the greater economic resources this privilege confers—to purchase sex from women (Janson, Durchslag, Mann, Marro, & Matvey, 2013). In some cases, the women who sell sex feel unable to reject buyers’ advances without jeopardizing their personal safety and/or financial security (Gerassi, 2015).

Sellers

Buyers of commercial sex are primarily men, but sellers are predominantly women (Rissel et al., 2017).³ Evidence suggests that the role women occupy in the commercial sex trade is conditioned by gender. Women in patriarchal societies have fewer employment opportunities than men, and those opportunities are often associated with reduced compensation (Draus, Roddy, & Asabigi, 2015). Women also bear the brunt of poverty (Amnesty International, 2016). In 2016, U.S. adult women were 38% more likely to satisfy the federal threshold for poverty than adult men (Patrick, 2017). In addition, many women who sell sex have experienced physical and/or sexual victimization as children (Danna, 2012; Hickle & Roe-Sepowitz, 2017). Consequently, the intersection of poverty, childhood abuse, and limited unskilled labor positions may increase the risk of some women entering the commercial sex trade.

One explanation is that impoverished women require money but have few legal sources of income. These women may capitalize on the fact that men have been socialized into a patriarchal society and feel entitled to sex. Thus, women aware of male demand—perhaps as a result of childhood sexual abuse—may sell sex to meet their basic needs. In this way, women who sell sex to men assume a position within the commercial sex trade that is conditioned by gender roles and economic disadvantage. Some scholars have referred to the gender dynamic between buyers and sellers as “a form of [male] violence against women” (Ekberg, 2004; Raymond, 2004, p. 1177).

Facilitators

Many facilitators of commercial sex are men. Some facilitators pose as older boyfriends to lure underage girls into the commercial sex trade (Ekberg, 2004). Once the “boyfriends” are sure they have control over the girls, they guilt or otherwise coerce the girls into selling sex (Stalans & Finn, 2016b). Alternative arrangements also exist, however. For example, husband–wife or boyfriend–girlfriend couples may cofacilitate the sale of sex in exchange for a percentage of the profits (Siegel & de Blank, 2010). Male facilitators may also entrust daily operations to a loyal female seller who is responsible for introducing new women into the sex trade and retaining high-earners (Morselli & Savoie-Gargiso, 2014).

Criminal Justice Outcomes

Arrest

The United States criminalizes all aspects of the commercial sex trade (Hayes-Smith & Shekarkhar, 2010). This means that individuals who buy, sell, or facilitate sex are all—at least theoretically—at risk for arrest and punishment. Historically, however, law enforcement has arrested sellers while ignoring buyers (Farley & Kelly, 2000; Kann, 2011). Police agencies’ unequal enforcement of prostitution laws has created a “double standard for women and men,” as many sellers are women and many buyers are men (Raymond, 2004, p. 1183; Weitzer, 1999, p. 95). Recent attention to the interrelated nature of prostitution and sex trafficking has shifted law enforcement’s priorities toward arresting buyers rather than sellers, but this transformation is far from complete (Farrell & Cronin, 2015; Stalans & Finn, 2016a).

Many law enforcement agencies do not keep records of the role prostitution arrestees occupy in the commercial sex trade (Heiges, 2009). In some jurisdictions, facilitating sex is considered a separate offense, and the number of facilitators arrested is easily identifiable. Most jurisdictions, however, charge buyers and sellers of commercial sex with the same offense without any way to distinguish between the two roles. As a result, scholars are generally unable to compare arrest rates for buyers and sellers (Hedlin, 2017). One study estimated that sellers are 2 to 10 times more likely to be arrested than buyers (Hunt, 2013). Other sources suggest that sellers account for nine out of every 10 prostitution arrests (The Economist, 2015; Weidner, 2001).⁴ Only two studies appear to have calculated arrest rates for buyers and sellers using real-world data. The Seattle Women’s Commission (1995) found that 84% of prostitution arrestees in 1993 were sellers, and that buyers comprised the remaining 16%. In Connecticut, sellers constituted 72% of individuals arrested for prostitution over a decade (Juliano, 2016). Buyers accounted for the other 28%.

Sentencing

Researchers have yet to study differences in sentencing outcomes between buyers and sellers of commercial sex. The little research that does exist demonstrates that sellers are far more likely than buyers to be convicted, and therefore, receive a sentence of any type. For example, the Seattle Women’s Commission (1995) reported that 97% of those convicted of prostitution in 1993 were sellers. Consequently, buyers only accounted for 3% of those convicted of prostitution that year. Over a decade’s worth of data from Connecticut similarly revealed that 87% of those convicted of prostitution during that period were sellers (Juliano, 2016). In contrast, 13% were buyers. These studies suggest that sellers—and by extension, women—are disproportionately convicted of prostitution. As a result, sellers appear uniquely at risk for receiving a sentence of any type.

Current Study

A recent study by Pfeffer and colleagues (2017) reported that women were more likely than men to be arrested and sentenced to jail for prostitution. In contrast, men were less likely to be arrested, and more likely to receive a fine and/or probation instead of a jail sentence. Although instructive, the authors’ study was hindered by a couple of limitations. First, the authors did not control for participants’ criminality, which may influence criminal justice system outcomes. Second, the authors used data from Houston, Texas, for the years 1977 through 2010. The Texas Legislature, however, did not pass legislation until 2015 that required law enforcement agencies to distinguish between buyers and sellers of commercial sex (S.B. 825; 2015). Thus, Pfeffer and colleagues (2017) were unable to measure the role participants occupied in the commercial sex trade. This limitation is problematic because women primarily sell sex, whereas men overwhelmingly buy and facilitate it (Ekberg, 2004; Rissel et al., 2017). As a result, it is unclear whether the disparities Pfeffer and colleagues (2017) observed are attributable to gender, as the authors argue, or to a tendency for criminal justice actors to arrest and punish sellers instead of buyers (Stalans & Finn, 2016a).

The present study addresses these limitations by examining the influence of prostitution roles (buyer, seller, or facilitator) on various criminal justice system outcomes while controlling for a host of other variables, including participants’ criminality. Logistic regression models are calculated separately for buyers and sellers of commercial sex to control for any influence participants’ role in the prostitution offense may have on the independent and control variables. Similar to Pfeffer and colleagues’ (2017) study, our sample consists of 1,027 prostitution arrestees from Harris County (Houston), Texas. Our sample, however, only includes individuals arrested for prostitution after the Texas Legislature required law enforcement agencies to distinguish between buyers and sellers of commercial sex. Thus, we are able to differentiate between individuals arrested for buying, selling, or facilitating commercial sex.

The present study poses four research questions:

Research Question 1: Do demographic differences exist between buyers, sellers, and facilitators of commercial sex?

Research Question 2: Do women and men experience different criminal justice system outcomes?

Research Question 3: Do buyers, sellers, and facilitators of commercial sex experience different criminal justice system outcomes?

Research Question 4: If women and men do experience different criminal justice system outcomes, do these gender differences persist after controlling for the influence of prostitution role (buyer, seller, or facilitator)?

Method

Data

The Harris County District Attorney’s office provided prostitution arrest data for September 1, 2015 (the day S.B. 825 took effect) through September 23, 2016.⁵ During this period, 1,949 individuals were arrested for prostitution-related offenses in Harris County, Texas (Houston). Of these, 264 observations were deleted due to missing race/ethnicity or state residency data. An additional 658 observations were deleted due to missing data for criminal justice system outcomes. As a result, the final sample consisted of 1,027 prostitution arrestees. Table 1 presents descriptive statistics for the sample.

Table 1.

Descriptive Statistics (N = 1,027).

Variable	M (SD)	Minimum	Maximum	%	n
Dependent variables
Dismissed		0	1	34.8%	357
Pled out		0	1	16.5%	169
Convicted		0	1	48.8%	501
Independent variable
Prostitution role					1,027
Buyer		0	1	36.1%	371
Facilitator		0	1	5.2%	53
Seller		0	1	58.7%	603
Control variables
Male		0	1	52.3%	537
Race					1,027
Non-Latinx White		0	1	21.2%	218
Non-Latinx Black		0	1	41.7%	428
Latinx		0	1	29.1%	299
Non-Latinx other race		0	1	8.0%	82
Felony		0	1	6.4%	66
Age at arrest	35.5 (12.4)	17.4	75.1		1,027
Number of arrests	4.2 (3.8)	2	36		1,027
Non-Texas U.S. resident		0	1	8.2%	84

Dependent Variables

Three prostitution outcomes were measured. Prostitution cases where the district attorney declined to file charges were measured dichotomously (1 = case dismissed, 0 = case not dismissed). Prostitution cases where charges were filed and the defendant accepted a plea deal were measured dichotomously (1 = accepted plea deal, 0 = did not accept plea deal). Finally, prostitution cases that proceeded to trial and resulted in a guilty judgment were also measured dichotomously (1 = convicted, 0 = not convicted).

Independent Variables

Gender was measured dichotomously (1 = male, 0 = female). Because all of participants’ arrests for prostitution occurred after S.B. 825 took effect, the Harris County District Attorney’s office records allowed for identifying the distinct roles participants occupied in the prostitution offense. Participants’ role in the prostitution offense was measured categorically (1 = seller, 2 = buyer, 3 = facilitator). Dichotomous measures for buyers (1 = buyer, 0 = nonbuyer) and facilitators (1 = facilitator, 0 = nonfacilitator) were also created for use in multivariate analyses, with sellers serving as the reference group.

Control Variables

Data obtained from the Harris County District Attorney’s office contained measures of race, but not Latinx ethnicity. A standard practice for overcoming this limitation is to use the Latinx surname list associated with the most recent United States Census Bureau survey (Perkins, 1993). In the present study, the Latinx surname list consisted of surnames from the 2010 U.S. Census Bureau survey where at least 75% of the households bearing that surname self-identified as Latinx (Word & Perkins, 1996). Participants with a surname from the 2010 Latinx surname list were coded as Latinx, whereas participants with a surname from the 2010 U.S. Census Bureau survey where fewer than 75% of households self-identified as Latinx were coded as non-Latinx. Participants with a surname not listed on the U.S. Census Bureau survey were considered to be missing Latinx ethnicity data and excluded from the sample. Race and ethnicity data were then combined to create dummy variables for non-Latinx Blacks (1 = yes, 0 = no), Latinxs (1 = yes, 0 = no), and non-Latinx Asians (1 = yes, 0 = no). Non-Latinx Whites served as the reference group.

The severity of the prostitution offense for which participants had been arrested was measured dichotomously (1 = felony, 0 = misdemeanor). Age at arrest was measured continuously (x– = 35.5; SD = 12.4). Participants’ criminality was measured as the number of arrests they had, not including the current arrest for prostitution (x− = 4.2; SD = 3.8). Non-Texas residency was measured dichotomously (1 = non-Texas resident, 0 = Texas resident).

Analysis

Chi-squared analyses were conducted with prostitution role (buyer, facilitator, or seller) as the independent variable and gender, race, non-Texas residency, and criminal justice system outcomes as the dependent variable, respectively. One-way ANOVAs were also calculated with prostitution role as the independent variable and age at arrest and number of arrests as the dependent variable, respectively. Three logistic regression analyses were estimated with dismissal, plea deal acceptance, and conviction as the dependent variables; gender and prostitution role as the independent variables; and race, non-Texas residency, felony, age at arrest, and number of arrests as control variables.⁶ Following this, split-sample logistic regression models were calculated separately for buyers and sellers of commercial sex.⁷ Dismissal, plea deal acceptance, and conviction served as the dependent variables, gender as the independent variable, and race, non-Texas residency, felony, age at arrest, and number of arrests as control variables.

Results

Several statistically significant demographic differences existed between buyers, facilitators, and sellers of commercial sex at the bivariate level (Table 2). Men accounted for 96.8% of buyers and 77.4% of facilitators, but only 22.7% of sellers. In terms of race and ethnicity, non-Latinx Blacks accounted for 71.7% of facilitators, 46.3% of sellers, and 29.9% of buyers. Latinxs accounted for 48.2% of buyers, 19.1% of sellers, and 9.4% of facilitators. Non-Latinx Asians accounted for 12.4% of sellers, 3.8% of facilitators, and 1.5% of buyers. Non-Latinx Whites accounted for 23.1% of sellers, 19.7% of buyers, and 11.3% of facilitators.

Table 2.

Differences Between Buyers, Facilitators, and Sellers (N = 1,027).

Variable	Buyer(n = 371)	Facilitator(n = 53)	Seller(n = 603)	Test statistic
Male	96.8%	77.4%	22.7%	χ²(2) = 518.86***
Non-Latinx White	19.7%	11.3%	23.1%	χ²(2) = 4.84^†
Non-Latinx Black	29.9%	71.7%	46.3%	χ²(2) = 45.98***
Latinx	48.2%	9.4%	19.1%	χ²(2) = 105.23***
Non-Latinx Asian	2.2%	7.5%	11.6%	χ²(2) = 27.95***
Non-Texas U.S. resident	1.9%	3.8%	12.4%	χ²(2) = 35.49***
Age at arrest	39.62 (12.41)	32.58 (12.46)	33.15 (11.66)	F(2) = 35.14***
Number of arrests	3.32 (2.70)	5.26 (3.42)	4.67 (4.21)	F(2) = 17.63***
Dismissed	30.2%	49.1%	36.3%	χ²(2) = 8.84*
Pled out	21.6%	20.8%	12.9%	χ²(2) = 13.19**
Convicted	48.2%	30.2%	50.7%	χ²(2) = 8.31*

†

p < .10. *p < .05. **p < .01. ***p < .001, two tailed.

Non-Texas residents were more likely to be sellers (12.4%) than buyers (1.9%). On average, buyers were significantly older at the time of arrest (39.6 years) compared with sellers (33.2) and facilitators (32.6). The average buyer also had fewer arrests (3.3) than the average seller (4.7) and facilitator (5.3). Facilitators were significantly more likely to have their case dismissed (49.1%) relative to sellers (36.3%) and buyers (30.2%). Buyers were significantly more likely to accept a plea deal (21.6%) than sellers (12.9%). Finally, facilitators were significantly less likely to be convicted (30.2%) compared with buyers (48.2%) and sellers (50.7%).

Logistic regression results demonstrated that men were 55% less likely to have their case dismissed compared with women (see Model 1, Table 3). Facilitators were 192% more likely to have their case dismissed relative to sellers. Non-Latinx Blacks were 31% less likely to have their case dismissed relative to non-Latinx Whites at p < .10. Non-Latinx Asians were 115% more likely to have their case dismissed relative to non-Latinx Whites. Older participants were less likely to have their case dismissed, as were participants with a greater number of arrests.

Table 3.

Logistic Regression Coefficients for the Influence of Gender and Prostitution Role on Criminal Justice System Outcomes (N = 1,027).

Independent variables	β	Exp (B)	SE
Model 1: Dismissed
Constant	1.03***	2.79***	.27
Male	−.79**	.45**	.23
Buyer	.32	1.38	.23
Facilitator	1.07**	2.92**	.41
Non-Latinx Black	−.37^†	.69^†	.19
Latinx	.04	1.04	.20
Non-Latinx Asian	.77**	2.15**	.29
Non-Texas residency	−.23	.79	.25
Felony	.35	1.42	.41
Age at arrest	−.02***	.98***	.01
Number of arrests	−.14***	.87***	.03
R² = .08; χ²(10) = 110.53, p < .001
Model 2: Pled out
Constant	−1.96***	.14***	.39
Male	1.20***	3.31***	.32
Buyer	.24	.1.27	.27
Facilitator	−5.39***	.004***	.93
Non-Latinx Black	−.17	.84	.26
Latinx	−.53*	.59*	.27
Non-Latinx Asian	.26	1.29	.43
Non-Texas residency	−2.11*	.12*	.84
Felony	6.87***	958.48***	.86
Age at arrest	.00	1.00	.01
Number of arrests	−.17***	.84***	.04
R² = .21; χ²(10) = 192.46; p < .001
Model 3: Conviction
Constant	−1.56***	.21***	.27
Male	.22	1.25	.22
Buyer	−.42*	.66*	.21
Facilitator	.30	1.35	.49
Non-Latinx Black	.44*	1.55*	.19
Latinx	.29	1.33	.20
Non-Latinx Asian	−.86**	.42**	.31
Non-Texas residency	.74**	2.09**	.26
Felony	−6.91***	.001***	1.15
Age at arrest	.02**	1.02**	.01
Number of arrests	.21***	1.24***	.03
R² = .14; χ²(10) = 195.84, p < .001

†

p < .10. *p < .05. **p < .01. ***p < .001.

Men were 231% more likely to accept a plea deal than women (see Model 2, Table 3). Facilitators were .4% more likely to accept a plea deal relative to sellers. Latinxs were 41% less likely to accept a plea deal relative to non-Latinx Whites. Non-Texas residents were 82% less likely than Texas residents to accept a plea deal. Participants facing a felony prostitution charge were 858% more likely to accept a plea deal than participants facing a misdemeanor prostitution charge. Participants with a greater number of arrests were less likely to accept a plea deal.

Men and women did not differ in their likelihood of being convicted for a prostitution offense (see Model 3, Table 3). Buyers, however, were 34% less likely to be convicted relative to sellers. Non-Latinx Blacks were 55% more likely to be convicted compared with non-Latinx Whites. Non-Latinx Asians were 58% less likely to be convicted relative to non-Latinx Whites. Non-Texas residents were 109% more likely to be convicted relative to Texas residents. Participants who had been arrested for a felony prostitution charge were 99.9% less likely to be convicted compared with participants who had been arrested for a misdemeanor prostitution charge. Older participants were more likely to be convicted, as were participants with a greater number of arrests.

Split-sample logistic regression models were estimated to examine the influence of gender on criminal justice system outcomes separately for buyers and sellers of commercial sex (Table 4). Few variables reached statistical significance in the buyer-only sample. Latinx buyers were less likely to have their case dismissed or accept a plea deal, and more likely to be convicted, relative to non-Latinx White buyers. Non-Latinx Black buyers were also more likely to be convicted relative to non-Latinx White buyers. Buyers with a greater number of arrests were less likely to have their case dismissed or accept a plea deal, and more likely to be convicted.

Table 4.

Logistic Regression Coefficients for the Influence of Gender on Criminal Justice System Outcomes by Prostitution Role.

Dismissed
Independent variables	Model 1: Buyers (n = 371)			Model 2: Sellers (n = 603)
Independent variables	β	Exp (B)	SE	β	Exp (B)	SE
Constant	.51	1.67	.78	1.61***	5.03***	.37
Male	−.16	.85	.65	−.67*	.51*	.27
Non-Latinx Black	−.44	.64	.33	−.21	.81	.24
Latinx	−.53^†	.59^†	.30	.64*	1.89*	.28
Non-Latinx Asian	−.24	.79	.78	1.67***	5.30***	.38
Non-Texas residency	−.17	.84	.86	−.35	.70	.28
Felony	—	—	—	−.20	.82	.65
Age at arrest	−.01	.99	.01	−.06***	.94***	.01
Number of arrests	−.16*	.85*	.06	−.10*	.90*	.04
	R² = .03; χ²(7) = 12.89, p = .07			R² = .13; χ²(8) = 105.29, p < .001
Pled out
Independent variables	Model 3: Buyers (n = 364)			Model 4: Sellers (n = 603)
Independent variables	β	Exp (B)	SE	β	Exp (B)	SE
Constant	−1.07	.34	1.19	−2.12***	.12***	.55
Male	1.05	2.86	1.08	1.04**	2.83**	.38
Non-Latinx Black	−.14	.87	.36	−.37	.69	.40
Latinx	−.71*	.49*	.34	−.33	.72	.46
Non-Latinx Asian	−.31	.73	.88	.38	1.46	.56
Non-Texas residency	—	—	—	−1.78*	.17*	.85
Felony	—	—	—	6.42***	611.06***	.98
Age at arrest	.00	1.00	.01	.01	1.01	.01
Number of arrests	−.35**	.71**	.11	−.14*	.87*	.06
	R² = .06; χ²(7) = 24.41, p < .001			R² = .31; χ²(8) = 143.36, p < .001
Conviction
Independent variables	Model 5: Buyers (n = 371)			Model 6: Sellers (n = 567)
Independent variables	β	Exp (B)	SE	β	Exp (B)	SE
Constant	−1.55*	.21*	.79	−2.00***	.14***	.37
Male	−.40	.67	.65	.14	1.15	.25
Conviction
Independent variables	Model 5: Buyers (n = 371)			Model 6: Sellers (n = 567)
Independent variables	β	Exp (B)	SE	β	Exp (B)	SE
Non-Latinx Black	.58^†	1.79^†	.34	.33	1.40	.24
Latinx	1.08**	2.93**	.32	−.47^†	.63^†	.28
Non-Latinx Asian	.51	1.67	.80	−1.65***	.19***	.38
Non-Texas residency	1.24	3.47	.89	.74**	2.10**	.28
Felony	—	—	—	—	—	—
Age at arrest	.00	1.00	.06	.05^***	1.05^***	.01
Number of arrests	.31***	1.36***	.79	.17***	1.19***	.04
	R² = .09; χ²(7) = 47.73, p < .001			R² = .12; χ²(7) = 94.85, p < .001

†

p < .10. *p < .05. **p < .01. ***p < .001.

Male sellers were less likely to have their case dismissed and more likely to accept a plea deal compared with female sellers. Latinx and Non-Latinx Asian sellers were both more likely to have their case dismissed and less likely to be convicted relative to non-Latinx White sellers. Non-Texas resident sellers were less likely to accept a plea deal and more likely to be convicted compared with Texas resident sellers. Sellers facing a felony prostitution charge were more likely to accept a plea deal than sellers facing a misdemeanor prostitution charge. Older sellers were less likely to have their case dismissed, and more likely to be convicted. Sellers with a greater number of arrests were less likely to have their case dismissed or accept a plea deal, and more likely to be convicted.

Discussion

The present research builds on Pfeffer and colleagues’ (2017) study by clarifying the relationship between gender, prostitution role, and criminal justice system outcomes using a sample of 1,027 prostitution arrestees from Harris County (Houston), Texas. A major limitation of Pfeffer and colleagues’ (2017) study is their inability to control for the role participants occupied in the commercial sex trade. The authors also lacked a measure of participants’ criminality, which may influence criminal justice system outcomes. The present study addressed these limitations by examining whether gender influences criminal justice system outcomes when participants’ criminality and role in the prostitution offense (buyer, seller, or facilitator of commercial sex) are controlled for. Split-sample logistic regression analyses were also estimated separately for buyers and sellers of commercial sex to control for the influence of prostitution role. Several notable findings emerged.

First, as anticipated by the prior literature (Ekberg, 2004; Hunt, 2013), buyers and facilitators are primarily men, whereas sellers are predominately women. Second, gender predicted case dismissal and plea deal acceptance even after controlling for participants’ criminality (as measured by the number of arrests) and role in the prostitution offense. Contrary to expectations, however, men were less likely to have their case dismissed. This finding may be explained by the fact that the majority of buyers did not have their cases dismissed, and buyers were overwhelmingly men. Men were also more likely to accept a plea deal.

A third interesting finding is that prostitution role, rather than gender, predicted participants’ likelihood of conviction. Specifically, sellers were more likely to be convicted than buyers. This finding is in line with previous research demonstrating that sellers are more likely to be arrested and convicted than buyers (Juliano, 2016; Seattle Women’s Commission, 1995). Thus, despite growing recognition that many sellers experience violence from buyers and facilitators, they are nevertheless more likely to be convicted than buyers. This finding is troubling because it suggests that the criminal justice system is most likely to punish individuals who may be better understood as victims rather than perpetrators.

In addition to being at risk for violence perpetrated by buyers and facilitators, sellers may also be victims of human trafficking. For example, a defining characteristic of sex trafficking is the constant rotation of trafficked women from one city to the next over short periods of time to avoid detection by law enforcement (Bales & Lize, 2005). As a result, non-Texas residents who sold sex in Harris County (Houston) may be considered potential human trafficking victims. In the present study, non-Texas resident sellers were less likely to accept a plea deal and more likely to be convicted relative to Texas resident sellers. Thus, sellers with an increased likelihood of having been trafficked were punished more harshly than those with a reduced likelihood of having been trafficked. This finding lends additional support to the concern that the criminal justice system punishes victims more harshly for prostitution offenses.

Fourth, participants’ criminality (as measured by the number of arrests other than the current prostitution arrest) significantly predicted criminal justice outcomes. This finding is in line with previous research demonstrating that participants’ criminality is an important predictor of criminal justice system outcomes (Spohn & Welch, 2006). Thus, future research exploring gender disparities in criminal justice outcomes among prostitution arrestees should control for participants’ criminal history when possible.

As with any research effort, the current study has several limitations. First, the limited number of facilitators in the sample prevented estimating a separate split-sample model for this group. Second, data from the present study is not directly comparable with the data used by Pfeffer and colleagues (2017). As a result, we were unable to construct a severity of punishment measure such as the one those authors used. Finally, prior research has demonstrated that participants’ citizenship and legal status can influence sentencing outcomes (Wolfe, Pyrooz, & Spohn, 2011). Data limitations precluded controlling for this variable in the present study.

In conclusion, our findings show that gender differences persist for case dismissal and plea deal acceptance among prostitution arrestees even after controlling for their criminality and role in the prostitution offense. Gender does not influence the likelihood of conviction, but buyers are significantly less likely to be convicted than sellers. As a result, the criminal justice system appears to punish victims more harshly than perpetrators where prostitution is concerned. As long as prostitution continues to be criminalized in Texas, law enforcement and prosecutors would do well to shift their focus from sellers to arresting and prosecuting buyers. The finding that men are less likely than women to have their case dismissed suggests that some efforts may have already been taken to divert women (who are primarily sellers) away from the criminal justice system. In instances where women do not have their case dismissed, prosecutors may wish to consider pretrial prostitution diversion programs such as the Survivors Acquiring Freedom and Empowerment (SAFE) Court in Harris County, Texas (Muftic & Updegrove, 2018).

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Notes

Author Biographies

Alexander H. Updegrove is a doctoral research assistant in the Department of Criminal Justice and Criminology at Sam Houston State University. His recent works have appeared in Crime & Delinquency, the International Journal of Offender Therapy and Comparative Criminology, and the Journal of Offender Rehabilitation.

Lisa R. Muftic is currently an associate professor in the Department of Criminal Justice and Criminology at Sam Houston State University. Her work explores the intersectionality of interpersonal violence perpetration, victimization, sex, and justice responses. Her work has appeared in journals such as Justice Quarterly, Crime and Delinquency, and the Journal of Research in Crime and Delinquency.

Nicole Niebuhr is a doctoral student in the Department of Criminal Justice & Criminology, Sam Houston State University. Her research interests include offender rehabilitation, reentry, life course theory, and program evaluation. Her recent works have appeared in Crime and Delinquency, Journal of Criminal Justice and Corrections: Policy, Practice and Research.

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