Prevailing explanations for ethnic and racial disparities in drug sentencing have used as their testbed the crack and cocaine divide of the 1980s and 1990s. In this study, we investigate whether illegal prescription opioids and heroin have been racialized in the same way. The rise of opioid epidemic provides an opportunity for us to revisit the relationship between racial attitudes and drug policy preferences. In a nationally representative survey conducted on 850 respondents, we find that racial resentment is associated with harsher sentences for first-time drug offenders, regardless of the specific drug. We also investigate whether this relationship is stronger for individuals whose racial schemas for a given drug are more non-White than White. While we originally thought that the perceived racial composition for a drug might act as an effect modifier on the strength of the relationship between racial resentment and sentencing severity, the study did not support this because respondents perceived both heroin and illegal prescription opioid users to be White, consistent with the reality of usage patterns of these drugs. In other words, even if one imagines the user composition for a drug to be predominately non-White, their attitudes toward sentencing severity are no stronger than if they imagine the users to be predominately White. Indeed, our findings suggest that illegal prescription opioids and heroin are equally racialized at the current time. We attribute this lack of racialization to be due to opioid pills being used as gateway drugs to injectable heroin, that is, the drug substance changes, but the user does not.

Keywords: Opiods, race


Prescription painkiller abuse has become an especially salient issue for the public and policymakers. Common prescription opioids—drugs such as OxyContin, Vicodin, Percocet, morphine, and Codeine—are all highly addictive and easily abused substances. According to the Centers for Disease Control and Prevention (CDC), opioid overdoses accounted for 47,600 deaths in the United States in 2017. The 2016 National Survey on Drug Use and Health (NSDUH) found about 11.8 million American adults misused prescription pain relievers at least once in the previous year (Ahrnsbrak, Bose, Hedden, Lipari, & Park-Lee, 2017). Prescription painkillers may also act as a stepping stone to more dangerous and illegal drugs such as heroin. Indeed, about 80% of heroin users reported misusing prescription opioids first (Jones, 2013). Among opioid-involved deaths in 2015, prescription opioids accounted for 17,087 deaths and heroin accounted for 15,469 deaths (CDC, 2018). Despite decades-long efforts to address the use of opioids and overall illicit drug use, addiction rates continue to skyrocket, threatening the professional lives, intimate relationships, and mental health of more than 28.6 million Americans as of 2016 (Ahrnsbrak et al., 2017).

Compared to previous drug epidemics, such as the crack cocaine epidemic in the 1980s and 1990s, the opioid epidemic has been framed in a less criminalized and more medicalized way. The so-called “war on drugs” led to the mass incarceration of African Americans and Latinos for owning even small amounts of crack cocaine (Netherland & Hansen, 2016). Although crack and powder cocaine have nearly the same chemical composition, crack was penalized at a 100:1 ratio to its powdered form, disproportionately targeting communities of color (Fellner, 2009; Netherland & Hansen, 2016). This sentencing disparity reflected how racial discrimination becomes “woven into the very fabric of American anti-drug efforts” (Fellner, 2009, p. 257). Indeed, a report conducted by the U.S. Sentencing Commission found

79 percent of 5,669 sentenced crack offenders in 2009 were black, versus 10 percent who were white and 10 percent who were Hispanic. The figures for the 6,020 powder cocaine cases are far less skewed: 17 percent of these offenders were white, 28 percent were black, and 53 percent were Hispanic. (Kurtzleben, 2010)

The opioid epidemic we now face is still often racialized in the media, although it may also be less criminalized. As a result, users may be viewed more sympathetically by the American public (Netherland & Hansen, 2016). Some suspect this more sympathetic reaction can be attributed to the “whiteness” of the opioid epidemic: The media frames opioid addiction as a problem in predominantly White communities. Netherland and Hansen (2016) found drug use in White communities is considered “surprising and novel” (p. 672) by the media, although epidemiological evidence indicates Blacks and Whites use drugs at the same rate (Substance Abuse and Mental Health Services Administration, 2014). At the same time, the media humanizes drug use in White communities by including compelling background stories of addicts’ lives (Netherland and Hansen, 2016).

Media rhetoric and imagery distort societal perceptions of drugs and their users (Dixon, 2017). A recurring trope in the media paints distinctive pictures of White and Black users wherein Whites are rendered as victims and Blacks are portrayed as addicts. Media narratives often frame drug pushers as predatory “ghetto dwellers” while portraying drug users as innocent White, middle-class victims (Lassiter, 2015). Indeed, there is a stark juxtaposition between the criminalization of non-Whites and the decriminalization of Whites in the news media. Along with the media, the source of prescription painkillers via the medical community may also contribute to the public’s sympathetic views toward opioid addicts today.

The racialization of drug epidemics has become entrenched in American society. The most highly scrutinized example is the disparity in the government’s response toward crack and cocaine users, wherein Blacks are disproportionately penalized for drug use and trafficking (Vagins & McCurdy, 2006). The media fueled this narrative, homing in on the inner-city as a hotbed for crack use and trafficking (Hartman & Golub, 1999). Intertwined in this narrative was the trope that minorities and the urban poor were the predominant users of crack cocaine (Hartman & Golub, 1999). So began the decades-long war on drugs, a federal campaign to eradicate drug use and distribution in the United States, with devastating consequences for non-Whites. The Reagan administration, and particularly First Lady Nancy Reagan’s “Just Say No” campaign, encouraged mass incarcerations for nonviolent drug offenders. There was a demonstrable racialized element to the campaign. The disproportionate media scrutiny on non-Whites combined with legislation like the Anti-Drug Abuse Act of 1986, which established mandatory minimum prison sentences for controlled substances, contributed to longer prison sentences for Black Americans caught with crack cocaine versus White Americans caught with the same amount of powder cocaine. A self-perpetuating cycle began where non-Whites were targeted for drug crimes at higher rates than Whites, and the media eagerly fanned the racial flames, advancing the narrative of crack being an insidious “non-White” drug while neglecting to highlight the more “White” powder cocaine’s role in the drug war.

Most citizens are relatively uninformed when it comes to public policy and current events, so they naturally use “heuristics” or information shortcuts to determine their attitudes on certain policies (Lau & Redlawsk, 2001; Lupia, 1994). An overemphasis of Black people as drug users and criminals in the media reinforced an existing national bias against non-Whites (Entman, 1990). This phenomenon cultivated the oversimplified heuristic that drug abusers were principally non-Whites. Studies show that racial attitudes drive tough on crime sentencing preferences because the drug epidemic—specifically the war on crack cocaine—is so highly racialized (Bobo & Johnson, 2004; Unnever & Cullen, 2010; Valentino, 1999). In other words, racially resentful people believe Blacks are criminals, in general, and thus tougher crime policies will be more punitive toward Blacks even if Whites are caught for the same crime.

We posit a person’s level of racial resentment affects their punitiveness on drug sentencing. Namely, if someone is more racially conservative, we hypothesize they will favor stricter drug sentences. Criminologists James Unnever and Francis Cullen (2010) explain this phenomenon with their Racial-Animus model. The model outlines how Whites and racists imagine the perpetrator of a crime to be “a young, angry, Black, inner-city male who offends with little remorse” (p. 106). Accordingly, individuals harboring pervasive negative views about minorities should express more punitive attitudes when it comes to criminal justice. We also hypothesize this relationship will be the strongest if one’s schema for the typical user of a drug is non-White.

Due to the severity of the opioid epidemic, we want to revisit the degree to which this prominent drug epidemic in the United States is as racialized as popular drugs of the past, namely, crack and cocaine. With this study, we seek to connect the literature on racial attitudes with drug attitudes and how these two intertwined frameworks shape political attitudes.

Literature Review

The War on Drugs has racialized drug use in the United States. In the late 1990s, health care providers began to prescribe opioid painkillers at greater rates after pharmaceutical companies reassured the medical community that patients would not become addicted (Department of Health and Human Services, 2019). Despite this reassurance, the abuse of prescription opioids, particularly OxyContin, rose dramatically among White people (Netherland & Hansen, 2017). Meanwhile, the four “technologies of whiteness”—addiction neuroscience, pharmaceutical technology, legislation innovation, and marketing—have further categorized prescription opioid overdose as a “White problem” (Hansen & Netherland, 2016; Netherland & Hansen, 2017). In other words, these four “technologies of whiteness” are separate systems that have each contributed to illegal opioids’ perception as a White drug without explicitly naming race (Hansen & Netherland, 2016; Netherland & Hansen, 2017). This has allowed predominantly White populations to benefit from drug decriminalization where their drug abuse is treated as a medical illness and where medical need is invoked to secure drugs (Netherland & Hansen, 2017). The media foments this narrative of White victimhood, emphasizing the vulnerability and plights of White, middle-class individuals in rural and postindustrialist areas (McLean, 2017). In addition, the racial stratification of health insurance coverage and access to physicians has disproportionately allowed for opioid prescriptions to go to White patients compared with non-White patients, reinforcing the racial disparities in opioid abuse (Herzberg, 2013; Netherland & Hansen, 2017).

As of 2010, the National Institute on Drug Abuse (NIDA) found that an increasing number of individuals using illegal prescription opioids were turning to heroin as prescription opioids became harder to procure (Cicero, Ellis, Surratt, & Kurtz, 2014; Netherland & Hansen, 2017; Volkow, 2014). Interviews with young, urban injection drug users in 2008 and 2009 revealed that 86% had used opioids for nonmedical reasons prior to using heroin, indicating three main sources of where they obtained opioids: family, friends, and personal prescriptions (Lankenau et al., 2012). Although more recent data have found prescription opioids to be a gateway to heroin, it used to be the other way around. Of those who abused opioids in the 1960s, more than 80% began with heroin (Cicero et al., 2014). This shifted in the 2000s, where 75% reported their first exposure to heroin to be through prescription drug use (Cicero et al., 2014).

While drug use among White populations has generally been treated as a medical illness, non-White populations have experienced a punitive, carceral public response. The racialized differences in the control of prescription opioids and heroin resemble 1980s laws that distinguished crack cocaine from powder cocaine (Hansen & Netherland, 2016; Travis, Western, & Redburn, 2014). These laws prompted the United States to have the highest incarceration rates in the world with Black men 6 times and Hispanic men 3 times more likely to be incarcerated than White men (Hansen & Netherland, 2016; Travis et al., 2014). Today, more than half of young Black men in large cities are currently incarcerated (Alexander, 2012; Netherland & Hansen, 2017). Bobo and Johnson (2004) examined the relationship between racial attitudes and drug views and found that White respondents who had less education and maintained more racial resentment were more likely to support a difference in sentencing for crack and cocaine (i.e., harsher sentences for non-White-associated crack). Thus, drug use in the United States is not only highly racialized but also institutionalized.

In the same fashion as the crack cocaine epidemic of the late 20th century, the media has also racialized heroin and opioid as Black and White drugs, respectively. While the news media does not always explicitly mention race in drug stories, it operates in coded terms. Media imagery and descriptions of heroin users as “urban dwellers” (code for Black and Latino) stand in stark contrast to the portrayal of opioids as a “suburban” and “rural” drug (code for White) (Steiner & Argothy, 2001). Moreover, class and geography discrimination also play instrumental roles in the media’s portrayal of drug users. Representations of heroin users as poor, Black, inner-city residents dominated the news media, while opioid users were portrayed more sympathetically as struggling working-class Whites turning to “hillbilly heroin” (opioids) to self-medicate from their arduous manual labor jobs in rural Appalachia, for example (Inciardi & Cicero, 2009).

Minorities are frequently portrayed as addicts in print and television (Netherland & Hansen, 2016; Taylor, 2008). In news stories involving drug use, Blacks are portrayed as criminals more than Whites (Dixon, 2017; Netherland & Hansen, 2016; Peffley, Shields, & Williams, 1996; Reinarman & Levine, 2004). Netherland and Hansen (2016) found the media humanizes White drug use, portraying White people as innocent victims suffering from a condition out of their control. Moreover, the new emphasis on Whites as drug users and victims finally brought national attention to America’s long-standing substance abuse problem. A sobering 2001 Denver news story on the growing heroin epidemic echoed this new scrutiny on the issue: “It’s a white problem now,” an African American woman notes, adding that “if it weren’t, the camera crew wouldn’t be there” (Ostrow, 2001).

In this article, we examine how racial attitudes influence policy preferences toward drug sentencing for heroin and illegal prescription opioids. We hypothesize that the relationship between racial resentment and toughness on drug sentencing is moderated by the racial schema for that particular drug. We add to the existing literature on the racialization of drug use and begin to uncover how systemic and institutionalized racism have allowed for disproportionate drug sentencing policies in the United States. Moreover, we also evaluate the veracity of White imagery vis-à-vis heroin and illegal prescription opioid users.


In this study, our overarching question asks whether racial resentment predicts more punitive drug sentencing preferences, and if so, to what extent? With this in mind, we formulate two hypotheses.

Hypothesis 1: Racial resentment on drug sentencing: Racial resentment will be positively associated with a preference for tough drug sentencing policies.

Hypothesis 2: Effect modification: The more non-White a drug’s typical users are perceived to be, the stronger the relationship will be between racial resentment and drug sentencing severity.

We suspect the imagined racial schema of the drug users will act as an effect modifier (moderating variable) in the relationship between racial resentment and sentencing toughness. In the context of our study, racial schema refers to the user composition of a drug, whether it is viewed as a “White drug” or a “non-White drug.” More specifically, as the drug user racial schema becomes less White, the impact of racial resentment on punitiveness should increase.

Figure 1 presents a diagram of our causal model with controls for party identification, ideology, race, education, and experience with drugs.

Figure 1. Proposed causal model.
Figure 1. Proposed causal model.

Our logic in controlling for these variables is as follows: Political affiliation is a compelling identity that shapes one’s beliefs and votes (Campbell, Converse, Miller, & Stokes, 1960). Democrats and Republicans typically hold different preferences on crime perpetrators, with the former usually taking more liberal positions in favor of more forgiving sentences and the latter taking more conservative positions in favor of harsher sentences. Due to the racially fraught nature of U.S. drug policy, we also controlled for the respondent’s race. In alignment with findings from the U.S. Sentencing Commission’s 2006 report on sentencing attitudes, we also predict individuals with higher levels of educational attainment may adopt more liberalizing views when it comes to sentencing preferences, regardless of the specific drug (p. 148).


Our population of interest is all U.S. adults. The sampling frame consists of members of the opt-in YouGov panel. The survey was conducted online by YouGov using a nonprobability sample of 924 respondents that was then matched to the American Community Survey (ACS) and weighted to known population targets to result in a final data set of 850 respondents. Information on completion rates was not provided. Our sample overrepresented Whites (68%) and was more highly educated than the national population. In all, 63% of respondents reported having at least some college, with 19% having 4 years of college and 11% having a postgraduate education. Full descriptive statistics of our sample can be found in Table 1.

Table 1. Descriptive Statistics of Respondents (N = 850)
Native American80.94
Middle Eastern10.12
No high school374.35
High school graduate27432.34
Some college20023.53

To operationalize our independent variable of racial resentment, we used the classic 5-point racial resentment scale originally formulated by Kinder and Sears (1981) and built upon by Kinder and Sanders (1996). Lower values on the scale indicate less racial resentment, whereas higher values indicate more racial resentment. Table 2 illustrates what the Cronbach’s alpha coefficient for the scale would be if each item was to be excluded. Table 2 also includes the overall alpha value of .87 which is reflective of good internal consistency.

Table 2. Cronbach's Alpha for the Racial Resentment Scale.
ItemCronbach's alpha
Irish, Italians, Jewish, and many other minorities overcame prejudice and worked their way up. Blacks should do the same without any special favors..8152
Generations of slavery and discrimination have created conditions that make it difficult for Blacks to work their way out of the lower class..8284
Over the past few years, Blacks have gotten less than they deserve..8409
It’s really a matter of some people not trying hard enough; if Blacks would only try harder, they could be just as well-off as Whites..8330
Test scale.8664

We subsequently created another scale to measure the dependent variable of drug sentencing toughness. This scale was composed of two questions with a 7-point response scale:

  1. The sentence for a first-time drug offense for possession of heroin is usually about 1 year in prison. Do you think the sentence is too tough, too weak, or about right?
  2. The sentence for possession of illegal prescription painkillers, sometimes called opioids, including drugs such as morphine, Percocet, OxyContin, and Vicodin is also about 1 year in prison. Do you think that the sentence is too tough, too weak, or about right?

The scale achieved a Cronbach’s alpha coefficient of .90, which is once again a strong measure of reliability. Lower values on the scale indicate more forgiving sentencing preferences, whereas higher values indicate more punitive sentencing preferences.

We measured the moderating variable of a drug’s racial schema by asking respondents the following questions:

  1. If you had to guess, approximately what share of all users of heroin are White? Please use the slider below.
  2. If you had to guess, approximately what share of all users of illegal prescription painkillers, sometimes called opioids, including drugs such as morphine, Percocet, OxyContin, and Vicodin are White? Please use the slider below.

In our analysis with the moderating variable, we created a dummy variable depending on whether the drug’s perceived composition was majority or minority non-White. Thus, a value of 0 means the respondent perceived non-Whites to be a minority of the drug’s users, whereas a value of 1 means the respondent perceived non-Whites to be a majority of the drug’s users.


Effects of Racial Resentment on Sentencing Preferences

Our first hypothesis is that racial resentment leads to an increase in general drug sentencing toughness. To investigate this claim, we conducted a series of three ordinary least squares regressions of drug sentencing severity—one for general toughness, one for heroin-specific toughness, and one for opioid-specific toughness. First, we used our racial resentment scale as the sole independent variable. The results of the regression confirmed our hypothesis. As one moves one unit on the racial resentment scale with 0 being the least racially resentful and 1 being the most racially resentful, we would expect to see an increase of 0.26 points on the sentencing scale with 0 being the most forgiving sentence and 1 being the strictest sentence. The results are highly statistically significant (p < .01).

Interestingly, the impact of racial resentment on the drug sentencing severity scale is rather small compared to racial resentment’s impact on other policy outcomes asked about in the survey. For instance, a simple linear regression analysis with racial resentment as the independent variable and support for declaring a national emergency to fund a wall on the border with Mexico as the dependent variable yielded a coefficient of .88. In other words, a one-unit increase on the racial resentment scale should lead to an increase of 0.88 points on support for the border wall. Our coefficient of .26 for the drug sentencing dependent variable is over 3 times smaller than the coefficient for the border wall dependent variable, indicative of a deracialization effect in heroin and illegal prescription opioids. The reasons for this correspond to our surprising finding that the racial schemas for both drugs are predominantly White—thereby diffusing the impact of racial resentment on sentencing preferences because subjects actually perceive heroin and illegal prescription opioid users to be White.

To further investigate our first hypothesis, we reran the regression analysis with a number of control variables, including a dummy variable for whether the respondent was White and variables for education, political ideology, and age.1

Table 3. Relationship Between Racial Resentment and General Drug Sentencing Toughness From Least Racially Resentful to Most Racially Resentful (Standard Error in Parentheses).
General toughness (DV)
Racial resentment.130***
White respondent-.023
Note. DV = dependent variable.

Every variable except White respondent reached significance (p < .05). Furthermore, the ideology and age predictors were highly significant (p < .01). The coefficient on racial resentment in the full model with all control variables is slightly lower than the model without controls; nevertheless, the coefficient is still significant (p < .01). We can interpret the regression coefficient on the new racial resentment predictor in the full model as an increase of 0.130 points on the drug sentencing severity scale. Once again, this is a much smaller substantive result than we expected. The reasons why will be illuminated in the following section.

Racial Schema for Users of Heroin and Illegal Prescription Opioids

Respondents accurately perceived users of heroin and illegal prescription opioids to be composed mainly of White people. The average non-White proportion for drug users is 39% for heroin and 34% for illegal prescription opioids. The distributions for both drugs can be found in Figure 2. As our sample is more highly educated than the national population, it is possible that they can be expected to make more accurate predictions about the racial schemas for these drugs. Indeed, the perception that users of heroin and illegal prescription opioids are mainly White is actually empirically correct: The NSDUH 2002–2011 found 79.3% of past year heroin initiates were non-Hispanic Whites and only 3.1% were non-Hispanic Blacks (Muhuri, Gfroerer, & Davies, 2013).

Figure 2. Distributions of the perceived composition of non-White users for heroin and illegal prescription opioids.
Figure 2. Distributions of the perceived composition of non-White users for heroin and illegal prescription opioids.

Conversely, our respondents perceived 61% of heroin users and 66% of opioid users to be White. While a simple t test between the two user compositions indicated that the two statistics happen to be statistically different from each other at a 1% level, we are more interested in how both these values imply non-Whites are the minority of drug users. This is a key finding and will play a crucial role in our forthcoming moderating variable analysis. It seems that opioids and heroin do not have the same salient racial division that cocaine and crack did.

Although we were initially surprised that the racial schemas for users of both heroin and illegal prescription opioids were predominantly White, we believe a major contributor to this equally racialized perception is the remarkable public attention the opioid epidemic has attracted, specifically in the past decade. Even the term “opioid epidemic” has become particularly politically and publicly salient. Members of Congress now openly speak of combating the opioid epidemic, and many Americans know someone personally struggling with drugs. As drug addiction and mental health issues are becoming less taboo to speak publicly about in society, representation of their victims is also becoming whiter.

Documentaries and news stories on the U.S. opioid crisis tend to focus on White people, with the archetype being White people specifically in West Virginia and other blue-collar areas ravaged by heroin and prescription opioids. These stories even go one step further: telling the stories of wealthy, educated White people who became addicted—a demographic historically very sheltered from publicity. One movie which received a nomination to the 2019 Golden Globes, Beautiful Boy, recounts the true story of an affluent, White teenager living in San Francisco who suffers from a heroin addiction. Similarly, according to Iyengar (1991), these stories on the opioid epidemic are thematic frames, de-emphasizing the role of an individual drug user while highlighting the larger sociocultural factors undergirding the epidemic and representing the user demographics more factually. The desensitization of drug use in the media, especially with regard to White drug use, is a likely contributor to why our sample believes—accurately—that opioid users are mostly White.

After further research, we also attribute this deracialization effect to prescription opioids being now considered a gateway drug to heroin (Netherland & Hansen, 2017; Volkow, 2014). A Center for Behavioral Health Statistics and Quality data review of the National Survey of Drug Use and Health found 80% of recent heroin initiates previously used nonmedical pain relievers—or in other words, illegal prescription opioids (Muhuri et al., 2013). The gateway theory is based on how opioids and heroin produce the same neuropharmacological effects (National Academies of Sciences, Engineering, and Medicine, 2017). In other words, the drugs affect one’s nervous system and behavior the same way, so the substances are natural substitutes of one another. Heroin is more potent than low-dose prescription opioids, such as Vicodin and Percocet, and users may turn to heroin to achieve the same high that opioids once produced (National Academies of Sciences, Engineering, and Medicine, 2017). Thus, the gateway theory may explain why respondents did not perceive illegal prescription opioids and heroin to be racialized in the same way as crack and cocaine were racialized years ago.

Racial Schema Moderation Analysis

To investigate our second hypothesis that racial schemas act as effect modifiers in the relationship between racial resentment and drug sentencing preferences, we conducted two regressions: one for heroin and one for illegal prescription opioids. We converted the continuous variable of the non-White proportion of drug users, racial schema, into a binary variable coded 0 if the respondent perceived non-Whites to be a minority of the drug’s users and 1 if they perceived non-Whites to be a majority. Next, we created an interaction term between racial resentment and the dummy variable of minority/majority non-White. The moderating variable regressions on both heroin and illegal prescription opioids yielded insignificant results.

Figures 3 and 4 illustrate the moderating variable regression results. As evident by overlapping 95% confidence intervals, there is insufficient evidence to conclude a drug’s racial schema moderates the relationship between racial resentment and drug sentencing preference.

Figure 3. Ninety-five percent confidence intervals for moderating variable regression analysis of racial resentment’s impact on heroin toughness by racial schema.
Figure 3. Ninety-five percent confidence intervals for moderating variable regression analysis of racial resentment’s impact on heroin toughness by racial schema.
Figure 4. Ninety-five percent confidence intervals for moderating variable regression analysis of racial resentment’s impact on illegal prescription opioid toughness by racial schema.
Figure 4. Ninety-five percent confidence intervals for moderating variable regression analysis of racial resentment’s impact on illegal prescription opioid toughness by racial schema.

That said, it is important to note that the interaction is in the expected direction. In other words, if one thinks the users of a drug are non-White, the effect of racial resentment is larger—just not significantly so. This is the case for both heroin and illegal prescription opioids. The effect modifier hypothesis fails both because the respondents thought both sets of users were White (so their attitudes about non-Whites were less relevant) and because they thought both drugs were about equally non-White.

While these results contradict our second hypothesis, they are appropriate given the data we uncovered about the perceived racial schemas for these drugs both being majority White (61% in the case of heroin and 66% in the case of illegal prescription opioids). Thus, we did not find that these schemas powerfully moderate the effect of racial resentment on sentencing simply because people envision these users as being White in the first place. These results are surprising and indicative of an equally racialized effect for these drugs in particular. We attribute the insignificant moderation effects to illegal prescription opioids often being a gateway drug to heroin (National Academies of Sciences, Engineering, and Medicine, 2017) wherein the user of the drug does not change, but the substance does. In contrast to the crack and cocaine racial disparities where cheaper crack was seen as a predominantly Black drug and punished at a 100:1 sentencing disparity, opioids and heroin are not seen as two separate classes of drugs used by two separate races. Rather, it would seem that the public accurately perceives opioids and heroin to be similar substances both used predominantly by White people.

In conclusion, we find that racial resentment predicts punitive attitudes about drug sentencing. Interestingly, negative racial attitudes predict tougher sentencing regardless of the respondent’s racial schemas about the drug’s users. This result is consistent with our finding that, on average, respondents perceive both heroin and illegal prescription opioid users to be majority White instead of non-White. We suspect these White user schemas probably suppress the effect of racial resentment overall on sentencing toughness, although it is impossible to tell for sure given our observational design. Our findings imply that heroin and opioids may be thought of much differently than other drugs, and this might have significant consequences for the popularity of policy interventions that could help address the problem.


Although our results indicate statistically significant and politically meaningful findings, we hope to ideally conduct another survey with more questions on drug user perceptions, sentencing preferences, and demographics. A randomized control experiment is conducive to this type of research. Future research might compare sentencing attitudes between a photo of a White person using drugs and a non-White person using the same drugs. Similar to Bobo and Johnson (2004), we might also add a racial bias cue to uncover drug user perceptions and sentencing preferences.

The sensitive nature of racial probing in surveys is also worth exploring. We acknowledge that a social desirability bias might occur when respondents were asked to quantify what proportion of heroin and illegal prescription opioid users were non-White. The fact that our sample perceives users of both drugs to be predominantly White could result from a sense that answering the question in the opposite direction (i.e., drugs users are mainly non-White) might indicate racism. This heightened awareness to not coming off as racist might cause respondents to change their rating on the explicit schema measures. While we cannot determine for certain whether this kind of bias exists, it is always important to recognize potential opportunities for error.

Moreover, the intersection of gender and sentencing attitudes add another nuance to this complex topic; we would like to assess whether sentencing preferences significantly differ for a female offender versus a male offender. Finally, the salience of the vocabulary poses another potential limitation of our study. While we were explicit as possible in defining what we meant by “illegal prescription painkillers, sometimes called opioids, including drugs such as morphine, Percocet, OxyContin, and Vicodin,” some individuals might know them as their substances or brand-specific names (e.g., morphine or “Norco”) and may have not recognized what we meant.


These results illuminate a number of curious findings. Most importantly, the perceived White majorities for drug users we uncovered are illustrative of a deracialization—or more accurately, an equally racializing—effect on heroin and illegal prescription opioids. Contrary to the disparate races associated with crack and cocaine, we did not find this to be the case with heroin and opioids. As aforementioned, we believe this phenomenon can be attributed to the gateway-like nature of opioids leading to heroin. Indeed, the race of the user vis-à-vis the drug does not change; rather, it is the drug itself that does. In sum, regardless of the type of drug—whether heroin or illegal prescription opioids—respondents perceive the majority of users to be White.

Our first hypothesis that increasing levels of racial resentment predict an increase in punitive sentencing preferences was strongly supported in the data. These findings have consequential impacts on tangible policy decisions. In application, a more racially conservative legislative body may vote in favor of harsher sentences for drug offenders, which will perpetuate the injustices of the crack cocaine epidemic 30 years ago. While Black people are equally as likely to use illegal drugs as White people, they are 6 to 10 times more likely to be incarcerated for drug offenses (Bigg, 2007; Goode, 2013; Netherland & Hansen, 2017). With a prison population of 2.3 million people, of which drug offenders comprise nearly half, this issue has never been more relevant (Federal Bureau of Prisons, 2019; Wagner & Sawyer, 2019).


1. White respondent was calculated by recoding the survey’s race question into two groups of Whites and non-Whites. All other races (comprising Hispanic, Black, Asian, Mixed, Native American, Middle Eastern, and Other) were collapsed into the non-White category.


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