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. Author manuscript; available in PMC: 2026 Jul 16.
Published before final editing as: Int J Ment Health Addict. 2026 Jan 26:10.1007/s11469-025-01632-w. doi: 10.1007/s11469-025-01632-w

THE GENDER SEGREGATION OF PRECARIOUS WORK CONTEXTS AMONG PEOPLE WHO USE DRUGS

Kaitlyn Jaffe 1,2, JinCheol Choi 2, Thomas Kerr 2,3, Cheyenne Johnson 2,6, Kanna Hayashi 2,4, M-J Milloy 2,3, Lindsey Richardson 2,5
PMCID: PMC13372255  NIHMSID: NIHMS2187717  PMID: 42459205

Abstract

Employment is a social determinant of health, yet people who use drugs (PWUD) often face barriers to employment. To meet their financial needs, some engage in alternate income-generating activities (e.g., sex work, recycling, drug selling) that expose them to social and health-related harm. Drawing from two cohorts of community-recruited PWUD in Vancouver, Canada (n=2366, 2006–2023), we used generalized estimating equations to characterize gendered patterns of income generation among men (n=1501, 63.4%) and women (n=865, 36.6%). Employment and income assistance were similarly patterned across genders and associated with fewer structural harms (e.g., criminalization), while drug selling and illegal activities were associated with elevated risks of harms, including incarceration and violence. Gendered differences emerged in street-based and sex work—activities often segregated by gender—revealing distinct social and structural exposures. These findings emphasize the need for income generation supports that address the specific needs and social locations of PWUD.

Keywords: drug use, gender, work contexts, income generation, employment

Introduction

As more people are underemployed or engaged in informal work, there has been an expansion of knowledge on the health-related consequences of precarious work (Kreshpaj et al., 2020). Past research has described precarious employment as work that is insecure or detrimental to health, for instance, through exposure to physical hazards, stress, or material deprivation (Benach et al., 2014; Burgard & Lin, 2013; Scott-Marshall & Tompa, 2011). The negative health impacts of precarious work may be amplified for individuals facing complex barriers to economic engagement, particularly in environments characterized by socioeconomic marginalization with fewer economic and social supports (Burgard & Lin, 2013). For example, people who use unregulated drugs (PWUD) living in environments with limited work opportunities may be further excluded from labor markets, due to substance use, criminalization, and/or discrimination (Galea & Vlahov, 2002; Richardson et al., 2013). To supplement low levels of income support and meet their basic needs (Laidley & Aldridge, 2020), socioeconomically marginalized PWUD commonly engage in informal work, activities that occur outside formal labor market (e.g., recycling for money, panhandling, etc.). Informal work that is illegal includes activities that are criminalized by law, including drug selling and theft. Prohibited work refers to activities forbidden by institutions or policies (e.g., panhandling in certain zones; unlicensed vending; sex work), which may lead to sanctions but are not necessarily illegal. Though they vary in their legality, these informal work activities share core features of precarious work, including a lack of legal protections, insufficient pay, and at times, exposure to criminalization and social and health-related harms (Boyd et al., 2018; Greer et al., 2020; Jaffe et al., 2018; Saberi Zafarghandi et al., 2021).

Despite greater attention to precarious work, less is known about the interplay between work contexts and gender. Understanding precarious work through a gendered lens is important as gender is a social-structural force that organizes social roles, expectations, and power relations on the basis of perceived sex, and therefore access to different income-generating opportunities and exposure to harm (e.g., violence, policing practices; Annandale, 2013; Huang et al., 2011; Ludwick et al., 2015; McNeil et al., 2014; Mitra et al., 2024). For instance, poverty and gendered labor markets can push women who use drugs toward informal and higher risk activities, such as drug selling, theft, and sex work (Boyd et al., 2018; Coppel & Perrin, 2024; Hogue et al., 2010). Gendered norms, including the pressure to display “street masculinity,” may reinforce the relationship between risk-taking, income generation, and health, for instance, through external pressures to engage in higher frequency drug use, drinking, or illegal activities (Courtenay, 2000; Grundetjern, 2015; Lemke et al., 2008; Mullins, 2006). Dominant forms of masculinity may also position men as more likely to engage in violence to resolve disputes, whereas women who use drugs are more likely to be victims of violence (Marshall et al., 2008). Although people who use illicit drugs are engaged in illegal activities and thus vulnerable to criminalization, gendered stereotypes may position women as less susceptible to police surveillance (Ludwick et al., 2015), but more subject to targeting in activities like sex work, where they are disproportionately represented and lack the legal protections of formal employment (Krüsi et al., 2016).

Although there is growing interest in how work affects health, wellbeing, and social status, gaps remain around how gender shapes the earning context for PWUD, and how different income-generating activities may be characterized by unique social and structural exposures (e.g., violence; criminalization). Previous gender-based analyses of income-generating activities have identified how substance use patterns among women and men diverged among those involved in formal income generation but largely converged for those engaged in informal and illegal income-generating activities (Jaffe et al., 2021). In this exploratory analysis, we utilize data from two prospective cohort studies of primarily socioeconomically marginalized PWUD in Vancouver, Canada to better understand social and structural exposures embedded in different income-generating activities. By presenting gender-stratified models, this analysis allows us to identify shared and distinct gendered pathways between precarious work and harm to inform the development of interventions that provide health-promoting and gender-specific economic supports.

Methods

Setting

The study occurred in Vancouver, British Columbia, Canada, a city experiencing an ongoing crisis in drug-related morbidity and mortality and a sustained housing affordability crisis that limits economic mobility and amplifies marginalization. Additionally, some neighborhoods, like the Downtown Eastside (DTES) where this study was conducted, have concentrated drug scenes and intensive policing, as well as strong community networks and supportive social programming that further shape opportunities for income generation (Ivsins et al., 2019; Liu & Blomley, 2013). Situating our study in this setting helps us recognize some of the structural drivers that constrain options and create precarious work contexts.

Data collection

Data are derived from two ongoing prospective cohort studies: the Vancouver Injection Drug Users Study (VIDUS); and the AIDS Care Cohort to evaluate Exposure to Survival Services (ACCESS). The VIDUS cohort is comprised of HIV-seronegative people who inject drugs, and the ACCESS cohort is comprised of people living with HIV who use drugs (other than or in addition to cannabis). Both cohorts have been enrolling participants since 2006 through community-based methods. These analyses include data collected between June 2006 and May 2023. Participants completed baseline and semi-annual follow-up surveys with measures on sociodemographic characteristics, substance use patterns, income generation, health, and structural exposures (e.g., homelessness), and were compensated $50CAD (in most recent follow-ups) for each interview. Data collection between cohorts is harmonized to allow for pooled analysis. All participants provided written informed consent. Both studies have received approval from the University of British Columbia/Providence Health Care research ethics board.

Measures

The primary outcome is type of income generation, derived from the question: “In the last six months, what were your sources of income?” Responses were categorized into six different income-generating activities as distinct binary outcomes: (1) employment (regular job; temporary job; self-employed); (2) income assistance (income assistance; old-age pension; employment insurance); (3) street-based activities (e.g., informal recycling; squeegeeing; panhandling); (4) sex work; (5) drug selling; and (6) illegal activities (e.g., theft; robbery).

We are primarily interested in social and structural exposures associated with each income-generating activity for women and for men. Data on gender were derived from the question: “Do you now consider yourself to be….” accompanied by six options: man; woman; trans woman; trans man; Two-Spirit (a non-binary identity used by Indigenous people); or “other” with an open response option. In analysis, women included those identifying as cisgender women, transgender women, or Two-Spirit. The numbers of transgender and Two-Spirit participants were too small to sufficiently power separate multivariable analyses, so we include them together, as groups facing greater vulnerability to gender-based social harm, but we recognize they experience distinct forms of marginalization. Men included those identifying as cisgender or transgender men. Substance use variables included daily unregulated opioid use (heroin; fentanyl; down; illicit prescription opioids), daily stimulant use (cocaine; crack-cocaine; methamphetamine), heavy alcohol use (defined as >4 drinks per day for men, >3 drinks per day for women; National Institute on Alcohol Abuse and Alcoholism, n.d.), injecting drugs in public, and non-fatal overdose. Social and structural exposures included experiencing homelessness, incarceration, police confrontations, being under community supervision (i.e., currently on probation, parole, bail; conditional sentence; or diversion programs), and experiencing physical or sexual violence. We also included as a covariate residence in the Downtown Eastside (DTES) neighborhood. Apart from current community supervision, all substance use and social/structural exposure variables refer to the six months prior to follow-up. Sociodemographic variables included age (time-updated), education (less than high school [vs. high school or greater]), and relationship status (married, common-law partner, regular partner [vs. no partner]). Consistent with Canadian conventions, the data include self-identified race, ethnicity, or cultural identity. The largest categories were White (58.3%), Indigenous identity (First Nations, Métis, or Inuit; 35.6%), and non-Indigenous people of color (5.6%). For analysis, we created two dichotomous variables: Indigenous (vs. non-Indigenous people of color) and White (vs. non-Indigenous people of color).

Analysis

Descriptive analyses assessed baseline differences across gender for independent variables and income-generating activities, using Pearson’s χ2 test for categorical variables and Wilcoxon rank-sum test for continuous variables. In the main analyses, we used generalized estimating equations (GEE). First, we assessed bivariate associations between independent variables and each income-generating activity in separate analyses for women and for men. Then, we conducted multivariable GEE analysis to assess associations between each income-generating activity and independent variables, including all covariates to consider potential suppression or mediation effects. Since six outcomes were analyzed separately by gender, we defined each outcome-by-gender combination as a family of tests. To address potential false positives due to multiple comparisons, we applied the Benjamini-Hochberg procedure to control the false discovery rate in each family. Bonferroni-adjusted p-values are also provided in the supplemental information as a robustness check. All p-values were two-sided. All analyses were performed using R (Version 4.4.1., R Foundation for Statistical Computing, Vienna, Austria).

Results

The sample included 2366 individuals who contributed 33,102 observations, including 865 (36.6%) women and 1501 (63.4%) men. Baseline descriptive statistics stratified by gender are displayed in Table I. Women were more likely to generate income through income assistance and sex work, whereas men were more likely to generate income through employment, street-based activities, and illegal activities. Notably, there were no gender differences at baseline in the proportion of those engaged in drug selling.

Table I.

Baseline characteristics among people who use drugs in Vancouver, Canada, stratified by gender, 2006–2023 (n=2366)

Characteristic Total (%) (n = 2366) Gender
 p-value x2
Men (%) (n =1501) Women (%) (n =865)
Sociodemographic
Age (median, IQR) 41 (33–47) 42 (35–49) 38 (31–43) <0.001
White 1379 (58.3) 984 (65.6) 395 (45.7) <.0001
Indigenous 842 (35.6) 397 (26.5) 445 (51.5) <0.001
Non-Indigenous people of color 133 (5.6) 111 (7.4) 22 (2.5) <0.001
Minimum HS education 1152 (48.7) 780 (52.0) 372 (43.0) <0.001
Relationshipa 734 (31.0) 376 (25.1) 358 (41.4) <0.001
Social and structural exposure
DTES residencea 1650 (69.7) 1002 (66.8) 648 (74.9) <0.001
Homelessnessa 916 (38.7) 585 (39.0) 331 (38.3) 0.744
Incarcerationa 400 (16.9) 280 (18.7) 120 (13.9) 0.003
Police confrontationsa 591 (25.0) 409 (27.3) 182 (21.0) 0.001
Community supervision 465 (19.7) 327 (21.8) 138 (16.0) <0.001
Victim of physical violencea 531 (22.4) 344 (22.9) 187 (21.6) 0.51
Victim of sexual violencea 45 (1.9) 10 (0.7) 35 (4.1) <0.001
Drug use a
Daily opioid use 800 (33.8) 440 (29.3) 360 (41.6) <0.001
Daily stimulant use 1098 (46.4) 637 (42.4) 461 (53.3) <0.001
Heavy alcohol useb 309 (13.1) 171 (11.4) 138 (16.0) 0.001
Public injection 952 (40.2) 597 (39.8) 355 (41.0) 0.51
Non-fatal overdose 240 (10.1) 150 (10.0) 90 (10.4) 0.73
Income generation a
Employmentc 561 (23.7) 434 (28.9) 127 (14.7) <0.001
Income assistance 2089 (88.3) 1303 (86.8) 786 (90.9) 0.003
Street-based activitiesd 597 (25.2) 421 (28.1) 176 (20.4) <0.001
Sex work 362 (15.3) 48 (3.2) 314 (36.3) <0.001
Drug selling 719 (30.4) 470 (31.3) 249 (28.8) 0.198
Illegal activitiese 352 (14.9) 242 (16.1) 110 (12.7) 0.025
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a

In the 6 months prior to follow-up

b

defined as average >4 drinks per day

c

regular job; temporary job; self-employed

d

informal recycling; squeegeeing; panhandling

e

theft; robbery

IQR, Interquartile range; HS, high school; DTES, Downtown Eastside

In this section, we report outcomes for 12 GEE models, one each for women and men for all six income-generating activities, with adjusted Odds Ratios and confidence intervals displayed in Tables II–IV. While we report significant associations related to substance use, to build on previous analyses that focused on substance use patterns (Jaffe et al., 2021), in this analysis we center social and structural exposures relevant to each income generation context. In Models 1 and 2 (Table II) examining formal employment, relationships between social and structural exposures and employment were similar for men and women and largely characterized by negative associations with these exposures. Notably, homelessness and DTES residence were negatively associated with employment among men and women. Among men, there was an inverse association with incarceration. Across substance use measures, there were negative associations between stimulant use and employment for men and women, but positive associations with heavy alcohol use for men and non-fatal overdose for women. Across sociodemographic measures, both women and men were more likely to be in a relationship and have at least a high school education. Men were less likely to be Indigenous (versus non-Indigenous people of color). Older age was positively associated with employment for women (all p<0.05).

Table II.

GEE analysis of factors associated with income generation activities among people who use drugs in Vancouver, Canada, 2006–2023 (n=2366)

Employment Income assistance
Characteristic Model 1: Men Model 2: Women Model 3: Men Model 4: Women
Sociodemographic
 Age (per 10 years) 0.99 (0.98 - 1.00) 1.02** (1.01 - 1.03) 1.05*** (1.04 - 1.07) 1.05*** (1.02 - 1.07)
 Indigenous (vs. people of color) 0.64* (0.46 - 0.90) 0.90 (0.39 - 2.05) 3.06*** (1.81 - 5.15) 1.45 (0.38 - 5.44)
 White (vs. people of color) 0.73 (0.53 - 0.99) 0.78 (0.34 - 1.78) 2.40*** (1.50 - 3.84) 1.76 (0.47 - 6.64)
 Minimum HS education 1.30** (1.11 - 1.53) 1.36* (1.07 - 1.73) 0.67* (0.50 - 0.89) 0.67 (0.43 - 1.05)
 Relationshipa 1.18** (1.08 - 1.30) 1.17* (1.01 - 1.35) 0.86 (0.73 - 1.01) 1.05 (0.83 - 1.32)
Social and structural exposure
 DTES residencea 0.81*** (0.74 - 0.89) 0.81* (0.69 - 0.96) 1.47*** (1.25 - 1.73) 1.89*** (1.40 - 2.55)
 Homelessnessa 0.81*** (0.72 - 0.90) 0.81** (0.69 - 0.95) 0.95 (0.81 - 1.10) 0.72* (0.54 - 0.96)
 Incarcerationa 0.78** (0.67 - 0.90) 0.96 (0.73 - 1.27) 0.82* (0.68 - 0.99) 0.62* (0.43 - 0.91)
 Police confrontationsa 0.98 (0.88 - 1.09) 1.03 (0.84 - 1.26) 0.98 (0.85 - 1.14) 1.10 (0.80 - 1.51)
 Community supervision 0.88 (0.78 - 0.99) 0.83 (0.66 - 1.05) 1.12 (0.95 - 1.33) 1.12 (0.80 - 1.51)
 Victim of physical violencea 0.92 (0.83 - 1.01) 0.99 (0.83 - 1.18) 1.09 (0.93 - 1.27) 1.01 (0.73 - 1.39)
 Victim of sexual violencea 1.83 (0.90 - 3.70) 0.82 (0.54 - 1.27) 0.71 (0.34 - 1.49) 0.91 (0.55 - 1.52)
Drug use a
 Daily opioid use 0.93 (0.84 - 1.03) 0.86 (0.73 - 1.01) 0.79** (0.69 - 0.91) 1.02 (0.79 - 1.31)
 Daily stimulant use 0.80*** (0.72 - 0.88) 0.78*** (0.68 - 0.90) 1.19* (1.04 - 1.36) 0.98 (0.78 - 1.23)
 Heavy alcohol useb 1.24** (1.07 - 1.43) 1.09 (0.91 - 1.31) 0.92 (0.75 - 1.15) 1.33 (0.90 - 1.99)
 Public injection 1.01 (0.91 - 1.11) 0.92 (0.79 - 1.08) 1.07 (0.91 - 1.25) 0.92 (0.73 - 1.17)
 Non-fatal overdose 1.04 (0.93 - 1.17) 1.31** (1.10 - 1.56) 1.20 (1.00 - 1.43) 1.19 (0.88 - 1.60)
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*

p <.05,

**

p <.01,

***

p<.001; p-values adjusted for multiple comparisons using Benjamini-Hochberg procedure

a

In the 6 months prior to follow-up

b

defined as average >4 drinks per day

GEE, Generalized Estimating Equations; HS, high school; DTES, Downtown Eastside

Table IV.

GEE analysis of factors associated with income generation activities among people who use drugs in Vancouver, Canada, 2006–2023 (n=2366)

Drug selling Illegal activities
Characteristic Model 9: Men Model 10: Women Model 11: Men Model 12: Women
Sociodemographic
 Age (per 10 years) 0.98*** (0.97 - 0.98) 0.99* (0.98 - 1.00) 0.95*** (0.94 - 0.97) 1.00 (0.98 - 1.01)
 Indigenous (vs. people of color) 0.71 (0.50 - 1.02) 1.00 (0.49 - 2.07) 1.30 (0.70 - 2.42) 1.39 (0.43 - 4.43)
 White (vs. people of color) 0.89 (0.63 - 1.24) 0.96 (0.47 - 1.99) 1.66 (0.91 - 3.03) 1.41 (0.44 - 4.47)
 Minimum HS education 1.08 (0.91 - 1.28) 1.01 (0.82 - 1.25) 1.17 (0.93 - 1.48) 1.03 (0.73 - 1.44)
 Relationshipa 1.06 (0.94 - 1.19) 1.12 (0.98 - 1.28) 1.02 (0.85 - 1.22) 1.23 (1.01 - 1.51)
Social and structural exposure
 DTES residencea 1.27*** (1.14 - 1.43) 1.26** (1.07 - 1.47) 1.23* (1.02 - 1.48) 1.51** (1.16 - 1.97)
 Homelessnessa 1.23*** (1.10 - 1.37) 1.29*** (1.11 - 1.49) 1.59*** (1.33 - 1.90) 1.28* (1.05 - 1.56)
 Incarcerationa 1.15 (0.99 - 1.33) 1.33* (1.06 - 1.67) 1.72*** (1.41 - 2.10) 1.38* (1.04 - 1.82)
 Police confrontationsa 1.78*** (1.59 - 1.99) 1.80*** (1.52 - 2.13) 1.34*** (1.14 - 1.57) 1.06 (0.81 - 1.38)
 Community supervision 1.04 (0.91 - 1.19) 0.97 (0.80 - 1.18) 1.77*** (1.49 - 2.12) 1.71*** (1.32 - 2.20)
 Victim of physical violencea 1.55*** (1.39 - 1.72) 1.28*** (1.11 - 1.47) 1.48*** (1.27 - 1.74) 1.51*** (1.21 - 1.89)
 Victim of sexual violencea 1.12 (0.56 - 2.23) 0.97 (0.70 - 1.34) 1.61 (0.68 - 3.80) 0.74 (0.35–1.57)
Drug use a
 Daily opioid use 1.88*** (1.68 - 2.11) 1.82*** (1.56 - 2.12) 1.53*** (1.29 - 1.83) 1.71*** (1.39 - 2.10)
 Daily stimulant use 2.01*** (1.81 - 2.24) 2.42*** (2.10 - 2.77) 2.14*** (1.80 - 2.55) 1.23 (0.99 - 1.52)
 Heavy alcohol useb 1.13 (0.97 - 1.32) 1.17 (0.99 - 1.38) 0.92 (0.69 - 1.22) 0.97 (0.73 - 1.29)
 Public injection 1.81*** (1.61 - 2.02) 1.52*** (1.31 - 1.77) 1.86*** (1.56 - 2.20) 1.47*** (1.18 - 1.82)
 Non-fatal overdose 1.30*** (1.14 - 1.47) 1.33** (1.04–1.83) 1.31* (1.06 - 1.62) 1.40* (1.10 - 1.79)
Open in a new tab
*

p <.05,

**

p <.01,

***

p<.001; p-values adjusted for multiple comparisons using Benjamini-Hochberg procedure

a

In the 6 months prior to follow-up

b

defined as average >4 drinks per day

GEE, Generalized Estimating Equations; HS, high school; DTES, Downtown Eastside

In Models 3 and 4 (Table II) examining income assistance, among both women and men, DTES residence was positively associated with receiving income assistance, while incarceration was negatively associated with income assistance. Among women, periods of receiving income assistance were less likely to include experiences of homelessness. In their substance use patterns, men were less likely to use opioids daily, but more likely to use stimulants daily compared to men not receiving income assistance. Among men and women, older age was associated with receiving income assistance. Men were also more likely to be White or Indigenous (versus non-Indigenous people of color), but less likely to have a high school education (all p<0.05).

In Models 5 and 6 (Table III) examining street-based work, there were primarily positive associations between social and structural harms and engagement in street-based activities. Both women and men during periods of engaging in street-based activities were more likely to have recently experienced police confrontations and physical violence. Further, men were more likely to experience homelessness, but less likely to experience recent incarceration compared to those not engaged in street-based activities. For men and women, daily opioid and stimulant use was associated with street-based activities, as well as heavy alcohol use and injecting in public among men. Men in street-based activities were more likely to be Indigenous or White (versus other people of color) and younger (all p<0.05). In Models 7 and 8 (Table III) estimating associations with sex work, again we observed significant positive associations between social harms and our outcome, though there were far fewer men engaged in sex work than women. Among both women and men, experiencing physical violence was positively associated with engagement in sex work. Among women, DTES residence and experiencing sexual violence were associated with sex work. Across substance use measures, daily stimulant use was associated with sex work for both men and women. For women, daily opioid use and public injection was significantly associated with sex work. Both men and women were younger and women in sex work were also less likely to have a partner.

Table III.

GEE analysis of factors associated with income generation activities among people who use drugs in Vancouver, Canada, 2006–2023 (n=2366)

Street-based activities Sex Work
Characteristic Model 5: Men Model 6: Women Model 7: Men Model 8: Women
Sociodemographic
 Age (per 10 years) 0.98*** (0.97 - 0.99) 1.00 (0.99 - 1.01) 0.90*** (0.88 - 0.92) 0.93*** (0.92 - 0.94)
 Indigenous (vs. people of color) 2.59*** (1.63 - 4.10) 1.84 (0.80 - 4.24) 3.51 (0.81 - 15.18) 0.95 (0.37 - 2.44)
 White (vs. people of color) 3.09*** (1.99 - 4.81) 1.78 (0.77 - 4.11) 2.24 (0.54 - 9.32) 1.51 (0.59 - 3.87)
 Minimum HS education 0.83 (0.70 - 0.99) 1.07 (0.86 - 1.34) 0.99 (0.58 - 1.70) 0.77 (0.58 - 1.02)
 Relationshipa 1.00 (0.91 - 1.11) 1.14 (0.98 - 1.31) 1.04 (0.69 - 1.56) 0.70*** (0.60 - 0.80)
Social and structural exposure
 DTES residencea 1.10 (0.99 - 1.23) 1.20 (1.01 - 1.42) 0.93 (0.66 - 1.31) 1.41*** (1.21 - 1.65)
 Homelessnessa 1.15* (1.03 - 1.28) 1.06 (0.91 - 1.23) 1.20 (0.79 - 1.81) 1.03 (0.90 - 1.18)
 Incarcerationa 0.82** (0.72 - 0.94) 0.95 (0.76 - 1.20) 0.74 (0.53 - 1.06) 0.85 (0.69 - 1.06)
 Police confrontationsa 1.36*** (1.23 - 1.50) 1.39*** (1.15 - 1.69) 0.95 (0.68 - 1.34) 1.20 (1.00 - 1.44)
 Community supervision 1.03 (0.92 - 1.14) 1.10 (0.90 - 1.34) 0.87 (0.63 - 1.19) 1.18 (0.98 - 1.41)
 Victim of physical violencea 1.27*** (1.15 - 1.41) 1.23* (1.04 - 1.46) 1.43* (1.04 - 1.97) 1.29*** (1.13 - 1.47)
 Victim of sexual violencea 1.96 (1.01 - 3.80) 0.90 (0.64 - 1.26) 2.42 (0.47 - 12.48) 1.51** (1.14 - 2.00)
Drug use a
 Daily opioid use 1.18** (1.05 - 1.32) 1.26** (1.08 - 1.48) 0.81 (0.55 - 1.19) 1.22* (1.06 - 1.40)
 Daily stimulant use 1.30*** (1.17 - 1.45) 1.42*** (1.23 - 1.64) 1.97*** (1.36 - 2.84) 1.74*** (1.52 - 2.00)
 Heavy alcohol useb 1.17* (1.03 - 1.33) 1.17 (0.99 - 1.39) 0.89 (0.57 - 1.39) 1.17 (0.97 - 1.41)
 Public injection 1.24*** (1.11 - 1.38) 1.11 (0.95 - 1.29) 1.31 (0.94 - 1.82) 1.24** (1.07 - 1.43)
 Non-fatal overdose 1.06 (0.93 - 1.21) 1.14 (0.94 - 1.38) 1.56 (1.02 - 2.38) 1.15 (0.96 - 1.38)
Open in a new tab
*

p <.05,

**

p <.01,

***

p<.001; p-values adjusted for multiple comparisons using Benjamini-Hochberg procedure

a

In the 6 months prior to follow-up

b

defined as average >4 drinks per day

GEE, Generalized Estimating Equations; HS, high school; DTES, Downtown Eastside

In Models 9–12 (Table IV), there were structural and social vulnerabilities associated with engaging in drug selling and illegal activities, and associations were similar across genders. Men and women who undertook drug selling were more likely to reside in the DTES and to experience homelessness, physical violence, and police confrontations, and women were more likely to have experienced incarceration. For both women and men, daily opioid and stimulant use, injecting in public, and non-fatal overdose were associated with engaging in drug selling. Finally, both men and women in drug selling were more likely to be younger. With similar patterns to drug selling, men and women engaged in illegal activities were more likely to live in the DTES, experience homelessness, physical violence, and be under community supervision than those not involved in illegal activities. Men were more likely to have confrontations with police. Similar to those involved in drug selling, men and women were more likely to use opioid daily, inject in public, experience non-fatal overdose, and men were more likely to use stimulants daily. Across sociodemographic measures, men were likely to be younger.

Results of our GEE analyses reveal patterns of social and structural vulnerability associated with different forms of income generation, with some associations varying by gender. Notably, social harms, including physical violence and police confrontations, were associated with prohibited or illegal forms of income generation for both women and men. Other associations were gendered. For instance, among men, homelessness was significantly associated with engagement in street-based activities, whereas residence in the DTES and sexual violence were associated with engagement in sex work among women. These findings highlight the complex interplay between gender and the social and structural context of precarious work.

Discussion

This study explores the social-structural position of women and men who use drugs engaged in six different income-generating activities, and by focusing on similarities and differences, reveals how gender may play a role in patterning social and structural exposures associated with the different contexts of informal, prohibited, or illegal work. Descriptive results revealed significant gender differences in the frequency of engaging in income-generating activities. Reflecting previous findings (Richardson et al., 2010), women who use drugs were less likely to be employed and more likely to receive income assistance compared to men. Previous research has documented structural barriers to labor market engagement among women in the general population (Haynie & Gorman, 1999; Moyser, 2017), and among women who use drugs (Brown & Riley, 2005; Hogue et al., 2010) and our results are consistent with past analyses. Men were more likely to engage in street-based and illegal activities, which may suggest women self-selecting out of these activities or choosing sex work as potentially more lucrative despite potential harms (Deering et al., 2011; Strathdee et al., 2015). Women may feel excluded from male-dominated activities like informal recycling, due to the physical risks, gendered assumptions about the work, or intentional social exclusion by men (Gowan, 2009; Maher & Hudson, 2007). Notably, there were no baseline differences in the proportions of men and women engaged in drug selling. While commonly conceptualized as a male-dominated activity, women may be engaged in the drug trade in similar numbers, with prior research documenting how women are often relegated to lower status positions within drug selling networks (Maher & Hudson, 2007), or are able to operate through newer configurations of drug markets that involve less physical risk (Anderson & Kavanaugh, 2017). These overall patterns of engagement across income-generating activities among women and men begin to demonstrate the ways gender may or may not play a central role in precarious work contexts.

Sociodemographic Characteristics

Beyond gender, individual-level characteristics like age, ethnicity, education, and relationship status can structure income generation through social processes linked to disadvantage, for example, through structural racism in hiring or barriers to retaining work. Cross-cutting disadvantage of direct relevance to income generation practices are evidenced by key notable associations. Women in employment were likely to be older, which may point to greater economic stability over time (Slesnick et al., 2018), or development of labor capital required for employment (Hogue et al., 2010). Younger women may also be disproportionately burdened by caregiving duties and as time passes, older women may have greater autonomy to enter the workforce (Huang et al., 2011). We also incorporated a race/ethnicity/ancestry variable reflecting the three largest groups in the sample: white (58.3%), Indigenous (35.6%), and non-Indigenous people of color (5.62%). Men identifying as white or Indigenous were more likely to access income assistance or engage in street-based income generating activities, compared to non-Indigenous people of color, which may highlight differences in access to social services or pathways to employment. Data also revealed employed participants were more likely to be in a relationship. Partnership may provide stability, practical and emotional support, or increase pressure to seek employment (Umberson & Karas Montez, 2010). Women in sex work were less likely to be partnered, an association possibly shaped by gendered norms around monogamy and intimacy in heterosexual relationships (Warr & Pyett, 1999), or as a way to avoid gendered relations where male intimate partners exert control over women’s sex work environments (Deering et al., 2014; Shannon, Kerr, et al., 2008). These patterns of age, gender, ethnicity, and relationship status demonstrate that sociodemographic characteristics of PWUD may not be singularly connected to engagement in income-generating activities, but when linked to other social processes (e.g., gendered work norms), these identities may influence socioeconomic marginalization.

Social/Structural Environment

Central to understandings of precarious work contexts are analyses of PWUD’s social/structural environment. Most participants reported living in the DTES neighborhood, a setting characterized by strong social ties and a hub of informal economic activity that facilitates buying, selling, and trading drugs and goods, as well as sex work—all activities that produce risk (Ivsins et al., 2019; Wittmer & Parizeau, 2016). However, embedded social ties and concentrated structural disadvantage can also shape PWUD’s opportunity and capacity for income generation. With limited opportunities for employment, informal work activities may be an accessible and lower risk alternative for PWUD living in the DTES (Boyd et al., 2018). For instance, drug selling may be an income-generating activity that is easier to conduct while living in the DTES, given existing drug markets and proximity to social networks (Small et al., 2013). Women in sex work may also feel it is easier to work in the DTES despite potential exposure to violence, due to the concentration of healthcare and social services and potential safety net of social networks (Shannon, Rusch, et al., 2008). For PWUD residing in the DTES, the precarious work context can shape access to opportunities, at times in gendered ways, and further engagement in informal or illegal activities.

Given links to economic stability and work, housing is a central consideration of precarious work contexts. People experiencing homelessness may encounter difficulty obtaining employment due to existing structural barriers (Ferguson et al., 2012), or retaining employment once they lose housing (Desmond & Gershenson, 2016). Those experiencing homelessness or in social housing with objectionable conditions will likely spend more time on the street, with opportunities to engage in drug selling, theft, and street-based activities that largely occur outdoors (Boyd et al., 2018). Notably, homelessness was associated with drug selling and illegal activities for both women and men, highlighting linkages between these activities and housing instability, potentially complicated by the strict rules and occupancy requirements in social housing (Boyd et al., 2016). Income assistance may mitigate some risks of homelessness by linking PWUD to housing support, but data only showed a significant negative association between income assistance and homelessness among women, which may point to gender-specific dynamics in housing access (Miller & Neaigus, 2002). However, many housing resources are located in the DTES (City of Vancouver, 2017), which may make it more difficult for those accessing these resources to exit the community should they wish to do so. Taken together, neighborhood residence and housing status structure an environment in which informal, prohibited, and illegal income generation become necessary for survival, and transitions into less precarious employment become more challenging (Galea & Vlahov, 2002; Richardson et al., 2013).

Structural and Everyday Violence

To understand the relationship between social and institutional structures and precarious work contexts, we frame our results in terms of structural violence, or social structures that perpetuate harm and preserve inequality (Farmer et al., 2006), and everyday violence to describe pervasive interpersonal violence with roots in structural violence (Bourgois et al., 2004). As institutions that enact structural violence against PWUD (e.g., through policing; area restrictions; surveillance, etc.), the carceral system can negatively impact opportunities for income generation (Kerr et al., 2005; McNeil et al., 2015). These results may reflect a causal process, where having reliable income reduces the need to engage in acquisitive crime and consequently reduces targeting by police. There may also be selection effects, as documented by previous research identifying pervasive labor market discrimination for people with a record of carceral system involvement (Pager, 2003). Significant associations among men may highlight gendered discrepancies in police interactions (Ludwick et al., 2015), and that men, especially those without records, encounter fewer barriers to employment. Although men in street-based activities were less likely to be incarcerated, both women and men involved in such activities were more likely to have police confrontations. Compared to explicitly illegal activities, street-based activities may be considered “lower risk,” but still subject participants to police surveillance and response (e.g., ticketing, being forced to move, confiscation of possessions) through policies designed to contain “public disorder” (Boyd et al., 2018; Jaffe et al., 2018; Wittmer & Parizeau, 2016). Similar findings among those engaged in drug selling have highlighted impacts of police targeting initiatives (Kerr et al., 2005). Associations between carceral system involvement and engagement in drug selling or illegal activities may be attributed to the legal status of such activities, however a cyclical process may occur, where participants with records of carceral legal system involvement experience barriers to employment and thus engage in these activities to generate adequate income. Additionally, the association between police confrontations and illegal activities among only men may point to a phenomenon where women are not as systematically targeted by police for petty crimes (Ludwick et al., 2015). In the short-term, the precarious context of informal, prohibited, and illegal income-generating activities is shaped by interactions with the carceral system and may produce outcomes that vary by gender. As an enduring form of structural violence, the carceral system continues to limit opportunities, burden communities, and reinforce socioeconomic inequalities.

Without institutional protections, PWUD in precarious work contexts can experience pervasive everyday violence. Interpersonal physical violence related to income generation may potentially arise from people angry with PWUD in public spaces (Boyd et al., 2018), from drug debts (Laing & Richardson, 2024), or from retaliation from other drug dealers (Richardson et al., 2015). Violence can also be attributed to institutional processes, such as area restrictions that confine people to living and working in smaller areas with increased exposure to interpersonal violence (McNeil et al., 2015). Women in sex work were likely to experience both physical and sexual violence, which can arise from clients or “bad dates” (Haritavorn, 2019; Shannon, Kerr, et al., 2008). Sex workers may believe they cannot report violence to police, nor can they rely on authorities for physical protection, given the stigma of sex work, placement of blame onto sex workers, and normalization of violence in sex work among police (Deering et al., 2014; Krüsi et al., 2016). In these work contexts, forms of everyday violence can be linked to structural violence, for instance, as a reactionary response to experiencing socioeconomic marginalization or as a manifestation of societal gender relations (Bourgois et al., 2004; Haritavorn, 2019). Not only are these precarious work contexts physically dangerous, but fear, stigma, and criminalization that produce and are produced by violence can make it challenging for PWUD to transition to safer work (Richardson et al., 2015).

Limitations and Conclusion

This analysis has limitations. First, the VIDUS and ACCESS studies are nonrandom samples and may not reflect the broader population of PWUD. Nonetheless, given the challenges associated with data collection from marginalized individuals and lack of registries of PWUD, cohort studies are commonly used for gathering data from populations for which there is no sampling frame (Reddon et al., 2020). It should also be noted that we excluded ACCESS participants whose only substance use is cannabis, who may still experience marginalization and engage in alternative forms of income generation. Second, although we treat each income-generating activity as reflecting a unique social location associated with specific forms of risk, PWUD likely engage in multiple forms of income generation, meaning configurations of risk for any one individual may be compounded through multiple forms of income generation that in the current study were analyzed separately. Relatedly, we rely on individual-level survey measures (e.g., homelessness, DTES residence, incarceration, police confrontation) as proxies for broader structural and policy indicators (e.g., drug laws, neighborhood effects), as we lack the data for multilevel analyses of upstream drivers. Third, structuring analyses as a gender dichotomy (men vs. women) may inadvertently reinforce gender binaries and obscure experiences of those with identities outside of this binary who were not represented in the sample in sufficient numbers to adequately power statistical analyses. We recognize that transgender, non-binary, and Two-Spirit participants face different forms of discrimination, colonial harm, and structural vulnerabilities and that grouping them can obscure these distinct experiences. Further, while the models include important sociodemographic covariates and are stratified by gender, limited sample sizes precluded a formal intersectional analysis for this study. Future research with larger sample sizes or mixed methods designs can better capture gender diversity and how multiple social locations jointly shape precarious work and associated harms. Fourth, the sensitivity of topics around income generation may subject data to response bias. However, established study relationships encourage trust between staff and participants, which may help to reduce systematic underreporting of sensitive data. Finally, as an observational study, residual confounding is possible. Because GEE provides population-averaged estimates, these results reflect associations at the group level and do not model individual trajectories or establish causality.

By exploring associations with six different income-generating activities, this study highlights how local environment, and structural and everyday violence position PWUD for engagement in informal work. Further, gender-based analyses provide additional insight into how income generation practices may be embedded in some cases in gender-specific configurations of social-structural risk and harm. The distinct configurations of vulnerability for each income-generating activity and convergent and divergent findings for women and men in our data suggest the potential for work opportunities to accommodate the social locations of PWUD and include the development of gender-sensitive supports and interventions.

Supplementary Material

Supplementary Tables

Acknowledgements

The authors thank the study participants for their contribution to the research, as well as current and past researchers and staff.

Funding

The study was supported by the US National Institutes of Health (U01DA038886, U01DA021525). At the time this research was conducted, LR was supported by the Michael Smith Foundation for Health Research (MSFHR) Scholar Award, a Canadian Institutes of Health Research (CIHR) New Investigator Award (MSH 217672), and a CIHR Foundation Grant (FDN-154320). KH was supported by the MSFHR Scholar Award and CIHR New Investigator Award (MSH 141971), and in part by the NIH grant (U01DA038886). KH also holds the St. Paul’s Hospital Chair in Substance Use Research supported through the St. Paul’s Foundation. MJM was supported by an MSFHR Scholar Award, CIHR New Investigator Award (MSH 360816), and the US National Institutes of Health (U01-DA0251525). TK was supported by a CIHR Foundation Grant (20R74326). CJ was supported by CIHR via the Canadian Research Initiative in Substance Misuse (CRISM) (MDZP GR006330) and the provincial government of British Columbia. At the time of writing, KJ was a Harvard-Brandeis SPIRE Center SUD Systems Performance Scholar. This research was undertaken, in part, thanks to funding from the Canada Research Chairs Program through a Tier II Canada Research Chair in Social Inclusion and Health Equity, which supports Lindsey Richardson. Funders had no role in study design, preparation of the manuscript, or decision to publish.

Footnotes

Conflict of interest

MJM’s institution has received an unstructured gift to support him from NG Biomed, Ltd., a private firm with a government license to produce cannabis, to support his research. He holds the Canopy Growth professorship in cannabis science, which was established through unstructured gifts to the University of British Columbia from Canopy Growth Corporation, a licensed producer of cannabis, and the Ministry of Mental Health and Addictions of the Government of British Columbia. Authors KJ, JC, TK, CJ, KH, and LR declare they have no conflict of interest.

DECLARATIONS

Ethics Approval

VIDUS and ACCESS have received ethics approval from Providence Health Care/University of British Columbia’s Research Ethics Board.

Consent to Participate

All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2000. Written informed consent was obtained from all study participants.

Consent for Publication

Not applicable.

Data Availability

Research data are not available due to the dynamics of collecting data on illegal activities from criminalized and marginalized populations.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Tables
NIHMS2187717-supplement-Supplementary_Tables.docx (32.6KB, docx)

Data Availability Statement

Research data are not available due to the dynamics of collecting data on illegal activities from criminalized and marginalized populations.

RESOURCES