Next Article in Journal
Reconstruction of Historical Memory: A Methodological Approach to Uncover the Reasons of the Armed Uprising in the Montes de María, Colombia
Next Article in Special Issue
Movement-Based Participatory Inquiry: The Multi-Voiced Story of the Survivors Justice Project
Previous Article in Journal
Educational Trajectories and Outcomes of Multiracial College Students
Previous Article in Special Issue
[Black] Teachers Resisting Damaged-Centered Research: Community Listening Exchanges as a Reciprocal Research Tool in a Gentrifying City
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:

“Lots of Time They Don’t Pay”: Understanding Wage-Theft and Resistance in Bryan, Texas through Critical Community-Engaged Research

Department of Sociology, Texas A&M University, College Station, TX 77843, USA
Author to whom correspondence should be addressed.
Soc. Sci. 2022, 11(3), 102;
Original submission received: 29 December 2021 / Revised: 22 February 2022 / Accepted: 25 February 2022 / Published: 28 February 2022


This critical community-engaged mixed methods study quantifies worker mistreatment on day labor corners in Bryan, Texas, and examines how day laborers resist labor exploitation. Day laborers seek work in open air spot markets. The work is precarious, with temporary and unregulated employment relations, weak enforcement, and poor working conditions. In this weak penalty and labor enforcement regime, labor violations are not surprising. Contrary to dominant theories, however, we argue that demand-side (industry) characteristics are more important for explaining the prevalence of labor violation than supply-side (worker characteristics). We use the Central Texas Day Labor Survey (2012–2021), 210 ethnosurveys consisting of 55% unauthorized workers, 24% authorized workers, and 20% Latinx, Black, and White citizens. We find that higher indices of labor violations and work abuse are not associated with lower-status workers; all workers, irrespective of legal status or citizenship, experienced abuse by employers. Demand-side characteristics were partially associated with higher levels of wage theft and mistreatment. In terms of wages, we found a gradation of wages with the lowest for unauthorized immigrants, then authorized immigrants, Latinx citizens, Black citizens, and lastly White citizens. Finally, workers collectively fight back against injustice by warning each other about unscrupulous employers.

1. Introduction

In the informal labor market of day laboring, wage, and work are negotiated on the street. The day labor corners in Bryan, Texas, or la veintiuno (21) as workers call it, are nestled behind the gas station and washateria on the busy intersection of Bryan and Highway 21. Small groups of three to five workers stand around on the corners, taking advantage of the few trees along the sidewalk that provide shade as they wait for employers to drive up. The men, the great majority of them immigrant Latinos but increasingly U.S. citizens as well, participate in informal markets where employers—usually small business owners, subcontractors, or homeowners⁠—offer work for a few hours to a few days. The work, which includes roofing, sheetrocking, landscaping, ranching, and cleaning houses, is paid in cash.
As pickups drive up slowly, the men eye the potential employer with interest and suspicion. Is she a regular employer? Is he known to workers as an employer who does not pay? What kind of work is she offering? How much is he willing to pay? Day laborers, who spend hours standing on the street corners at la 21 every day of the week, keep tabs on bad employers, those who skip out on paying for a hard day’s work or pay less than agreed upon, those who threaten day laborers with calling immigration when it’s time to pay. If the employer is known to not pay, others will yell out, “Don’t go with him! He does not pay!” No one except perhaps for newly arrived migrants or those really desperate for work will walk up to the truck. If, on the other hand, the employer is new or known to be reliable, day laborers will gather around the vehicle and negotiate. What kind of work is it? (Cement work is the hardest but roofing the most dangerous.) How much are they offering an hour? Most day laborers will not accept less than USD 12 an hour. The work, they explain, is hard, often perilous, and employers frequently take advantage of workers. You never know when you get in the truck if you will be paid, abandoned at the worksite, or denied breaks. Labor violations are rampant; day laborers share information to protect themselves and demand a measure of respect.
The aim of this mixed method project is to explore day labor work, labor violations, work abuse, and day laborers resistance to labor exploitation. Day laborers seek work in open-air spot markets, reaching verbal agreements with employers for work that usually lasts from a few hours to a few days (Valenzuela 2003). The work is precarious, with temporary and unregulated employment relations, weak enforcement, and poor working conditions (Valenzuela et al. 2006). Contingent employment, such as day laboring, has been on the rise as globalization and neoliberalism increased global competition, pushing employers to increasingly lower costs of production. Many low-wage employers responded by “breaking, bending, or evading long-established laws and standards designed to protect workers” (Bernhardt et al. 2008, pp. 1–2). This downward pressure undermines employers who follow labor laws since they increasingly find it difficult to compete without lowering wages and cutting corners on work conditions themselves. Deregulation that accompanies neoliberalism further weakens enforcement when workplace investigators diminish as workplaces needing inspection increase (Bernhardt et al. 2007; Kalleberg 2011; Piore and Schrank 2018). At the same time, punitive immigration policies, including highly visible work raids under Presidents Bush and Trump and silent I-9 raids that require employers to double-check workers’ authorization to work under President Obama, push out unauthorized workers from formal employment into informal work (Menjívar and Abrego 2012).

2. Literature Review

In this weak penalty and labor-enforcement regime, labor violations are not surprising. Although few large-sample studies examine labor abuse, there are two schools of thought that explain the prevalence of labor violations. One school of thought, made up mostly of economic sociologists and immigration scholars, explains the prevalence of labor violations by the predominance of workers with particularly vulnerable worker characteristics such as unlawful status, poor English proficiency, and willingness to take the wage offered. That is, vulnerable workers without much bargaining power best describe why labor violations persist (Bernhardt et al. 2013; Moss and Tilly 2001). Immigration scholars have shown that being undocumented affects wages in employment sectors (Durand et al. 2016; Massey and Gentsch 2014). Waldinger and Lichter (2003) similarly find that unauthorized immigrants compete with less skilled Black American workers in lower-status jobs. Immigration scholars also argue that ethnic networks used for recruiting workers help explain the preponderance of immigrants in some sectors. Poor English proficiency, lower levels of education, and larger percentages of unauthorized people make Latinx immigrants among the lowest-paid workers (Gammage 2008). This helps explain why the General Accounting Office (2002, p. 11) finds that “immigrants in the United States, especially new ones, are more willing to accept lower wages and substandard work that offers few benefits or protections, which makes them attractive to unscrupulous employers who may exploit them as a cheap source of labor”.
On the hand, labor scholars mostly focus on demand-side explanations, examining labor violations through the lens of job and employer characteristics (Bernhardt et al. 2013). The rise of contingent work, especially subcontracting and nonstandard forms of payment, create greater legal distance between the employer and worker, making enforcement of labor laws more difficult (Massey et al. 2003). Few studies exist that focus squarely on labor violations. An important exception is the Unregulated Work Survey, a large-scale chain-referral sampling study of 4387 frontline workers in low-wage industries in Chicago, Los Angeles, and New York City. In this study, Bernhardt et al. (2013) found that employer characteristics are 2.5 times more important in explaining labor violations. Smaller firms, firms that do not provide workplace benefits or use nonstandard forms of payment, are highly correlated with greater violation rates. In the end, they argue, noncompliance is factored into operating costs for low-road employers. Contrary to dominant theories, we side with labor scholars, arguing that demand-side (industry) characteristics are more important for explaining the prevalence of labor violation than supply-side (worker) characteristics. To understand this relationship, we next examine day labor work, labor laws, the effect of immigration policy on immigrant workers, and immigrant workers’ claims-making for restitution and justice.
The most in-depth study of day laborers, the National Day Labor Survey (NDLS), finds that labor violations suffuse the industry. The NDLS randomly selected 2660 day laborers across 264 hiring sites in 20 states. The most common labor violations encountered were nonpayment (48%) and underpayment (44%), followed by lack of water and breaks (44%), made to work extra hours (32%), insulted (28%), abandoned (27%), and experienced violence (18%) (Valenzuela et al. 2006). Subsequent studies have found similar findings (Haro et al. 2020). Most research on day laborers is conducted by public health scholars. They document increased occupational risks (Rabito et al. 2011; Rathod 2016), inadequate living conditions (Organista et al. 2019), and poor health (Quesada et al. 2011). Day laborers, most of whom are unauthorized immigrants, also experience racialization by employers that heighten their structural vulnerabilities (Negi 2013; Plankey-Videla and Cisneros Franco 2021).
The construction industry represents the greatest number of employers in day labor work (Valenzuela et al. 2006). It is no different in our study. The construction industry is dangerous and rife with poor working conditions. Scholars report a bifurcated construction industry, split into residential and commercial construction, with the former accounting for worse wages and working conditions (Theodore et al. 2008; Torres et al. 2013). The construction industry in Texas records the highest fatality rate in the U.S. Twenty percent of construction workers in Texas, according to a random study of workers in Austin, Dallas, Houston, San Antonio, and El Paso, suffer a workplace injury that requires medical attention (Workers Defense Project 2013).
Day laborers are covered by federal and state labor law (Department of Labor n.d.; Texas Workforce Commission n.d.). Enforced by inspectors in workplaces, the 1938 Fair Labor Standards Act requires a minimum wage, payment for all hours worked, and overtime if more than 40 hours are worked in a 7-day week. There is a 2-year window to file a claim. The 1970 Occupational Safety and Health Act states that employers must provide a safe work environment to avoid injury and death. This includes the ability to report injury without retaliation, as well as accessible water and bathrooms (General Accounting Office 2002). At the state level, the 1990 Texas Payday Law set up the Texas Workforce Commission to enforce that workers be paid within 6 days of work completed, with workers told their work periods and deductions. In Texas, however, workers only have 180 days to file a claim by mail or phone with one office in Austin, Texas (Gleeson 2012). In 2011, the Texas legislature passed the Texas Wage Theft Act, making nonpayment of wages a third-degree felony, allowing for criminal prosecution and damages if the employer fails to pay. In addition, Houston and El Paso have wage-theft ordinances requiring a database of recalcitrant employers and refusal of city permits and licenses to companies convicted of wage theft. Nonetheless, evading wage-theft claims is widespread, with accused companies declaring bankruptcy and quickly reopening under a new name. Moreover, taking advantage of the Texas Wage Theft Act involves costly litigation (Bova 2018; McPherson 2011).
The great majority of labor laws cover all workers, irrespective of immigration status. The one exception entails reinstatement and back pay if fired for union organizing per Hoffman Plastics Inc. v. the National Relations Board (Gleeson 2016). Nevertheless, unauthorized day laborers find themselves in a contradictory location. They do not have the authorization to be in the country or work but they are protected by labor laws, which are not heavily enforced. The 1986 Immigration Reform and Control Act (IRCA) criminalized hiring unauthorized workers but made it the employer’s responsibility to check work eligibility within 3 days of hiring (Massey et al. 2003). Unscrupulous employers take advantage of unauthorized workers’ contradictory location to threaten them with deportation, making it harder for unauthorized immigrants to come forward with claims of labor violations (Gleeson 2010).
In this study, survey results reveal that 55% of day laborers are unauthorized. They speak of deportation fear as they stand hyper-visible on the day labor corners. That is the incongruity of day labor work: the most vulnerable workers need to make themselves visible to employers, and in the process law enforcement, in order to earn a meager living. Since the passage of IRCA in 1986, immigration policies have increasingly been restrictive and punitive, criminalizing noncitizens more harshly than US-born persons for the same crimes (Golash-Boza 2015). In addition, immigration enforcement has devolved to the local level through policies such as 287 (g) and Texas SB 4, which allow local law enforcement to ask for legal status (Mansoor and Pollock 2017; Provine et al. 2016). Taken together, these measures heighten the vulnerability of unauthorized immigrants, criminalizing both authorized and unauthorized immigrants alike.
The loud anti-immigrant screeds of President Trump amplified this vulnerability, creating a deportability regime. In Bryan, Immigration and Customs Enforcement (ICE) picks up immigrants from the county jail twice a week. The past sheriff made public his disdain for unauthorized immigrants and his desire to work closely with ICE enforcing immigration law locally. Driving without a license, which cannot be avoided since there is little public transportation and unauthorized drivers are ineligible for driving licenses, often leads to contact with law enforcement. While the great majority of detentions do not lead to deportation, the possibility of deportation translates into intensified anxiety (Plankey-Videla 2021; TRAC 2020).
In this anti-immigrant framework, the presence of labor regulations alone does not ensure that labor rights are protected. Workers must know their rights, overcome the fear of retaliation and deportation, and even then, argues sociolegal scholar Shannon Gleeson (2010), their dual frame of reference towards their country of origin (Piore 1979; Waldinger and Lichter 2003), the desire for certainty in their lives, and strong identity as good workers (Gomberg-Muñoz 2010), lead them to accept lower wages and disincentivize claims-making. On the day labor corners, the networks that men create with one another may mitigate these effects. Migrants make use of networks to arrive in the U.S., find places to live, and seek employment (Massey et al. 2003; Flores-Yeffal 2013). They also employ networks to mobilize in low-wage industries for workplace rights (Milkman 2008).
Yet immigrants, the same as other low-wage workers, are reluctant to engage in claims-making (Miller and Sarat 1980; Gleeson 2010). Unauthorized immigrants experience the greatest cost to claims-making, with documented individuals and citizens more likely to demand their rights (Gleeson 2010). In the NDLS, Valenzuela and colleagues (2006) found that 70% of day laborers did not know where to report labor violations or work abuse. On the other hand, workers that know their rights and are associated with worker centers or immigration rights organizations are more likely to make claims and successfully recover unpaid wages (Fine and Gordon 2010; National Employment Law Project 2011; Visser et al. 2016).
This article examines labor violations, work abuses, and workers’ resistance to exploitation through knowing and defending their rights. Most studies of labor abuse use small convenience samples (Herrera 2016; Hiemstra 2010; Ordóñez 2015; Quesada et al. 2014). Only a few studies have large random samples that can distinguish between the effect of supply-side (worker) characteristics and demand-side (employer) characteristics to understand labor violations (Bernhardt et al. 2013; Valenzuela et al. 2006). This mixed method study fills the gap. Although a convenience sample, and therefore not generalizable to the whole population, there are enough cases to make statistical inferences, while using qualitative data to understand workers’ experiences and agency. In this article, we test the dominant theory that supply-side (worker) characteristics are more heavily associated with labor violations and work abuses, and we analyze whether violations, abuses, and hourly pay differ by legal status and race. Conversely, we explore whether demand-side (industry) characteristics —such as when construction work is performed—influence differential labor abuse and violation. Thus, although day labor is informal, unregulated work, and ample labor violations and abuses are expected, we argue that the strength of the associations between the variables of interest are not homogenous across legal status and racial lines. More specifically, we test the following hypotheses:
  • High incidence of labor violations and work abuses is associated with lower-status workers, i.e., non-White and undocumented immigrants;
  • High hourly pay rate is associated with higher-status workers, i.e., White and documented immigrants;
  • In accordance with the dominant theory, supply-side characteristics, including educational attainment, English proficiency, and strength of local networks, moderate the effects of being a lower-status worker. Specifically, for undocumented immigrants, higher levels in covariates are positively associated with a lower incidence of labor violations and work abuses to a greater degree than for higher-status workers;
  • High incidence of labor violations and work abuses is associated with participating in the construction industry, one of the most precarious sectors as described in the literature;
  • Being a citizen or authorized day laborer is associated with knowing where to report labor violations, a prerequisite for making claims for justice.
We now turn to describe the methods utilized and later discuss findings on the associations between worker attributes or industry characteristics and prevalence of labor violations and abuses. We end with a discussion of this study as part of a community-engaged research project.

3. Materials and Methods

3.1. Method, Instrument, Participants and Procedure

This mixed method project uses quantitative and qualitative data to gain insight into the work lives of day laborers. As a convergent mixed method study, both quantitative and qualitative data were gathered at the same time (Creswell 2015). We triangulate quantitative data through the closed-ended survey questions with the qualitative data stemming from open-ended questions and participant observation. As such, we are able to discuss outcomes of variables of interest with the process of how they came to be (Teddlie and Tashakkori 2010). In this study, the quantitative and qualitative data converge to demonstrate that day laborers, regardless of citizenship or race, although there is a gradation of disadvantage, experience high levels of labor violations and work abuse. Moreover, day laborers use networks to resist unscrupulous employers.
This mixed method study began as a community-engaged project with a community health clinic that was concerned with high levels of HIV/AIDS among day laborers. The goal was to provide culturally relevant medical information and uncover the mechanisms of elevated health problems (Hong et al. 2015). As a labor scholar, the main author included items about work abuse. After the clinic lost its federal funding and closed the outreach program, the main author, upon consultation with key leaders in the immigrant community, continued to research worker exploitation with the hopes that together we could develop a program to curb wage theft. The goal of the project was, from the beginning, to gather robust data to fight worker abuse.
The study uses the Central Texas Day Labor Survey. Collected over the period 2012–2021 in Bryan, Texas, the ethnosurvey includes a mixture of open-ended and closed-ended questions, complemented with participant observations at the day labor corners. The survey instrument (reproduced in the Appendix A) is a modified version of the NDLS. First amended by Fussell (2011) for implementation in post-Katrina New Orleans by adding questions on migration history, we added inquiries about immigration policy and deportation fear. The site, Bryan/College Station area, has 203,000 inhabitants, with 14% of the population being foreign-born. A total of 75% of those foreign-born are noncitizens, with more than half of these being from Latin America (U.S. Census Bureau 2019). Mexican migration to Bryan/College Station increased after IRCA, as recently regularized migrants left gateway cities in Texas (Alonzo 2018). By the 1980s, the foreign-born population had tripled. New assessments of unauthorized populations estimate that 6% in the Bryan/College Station area were unauthorized in 2014, with roughly 70% being Mexican, 19% Central American, and 9% Asian. By 2018, the Mexican-origin population had augmented to 80%, with 57% having arrived in the U.S. before 2010 (Center for Migration Studies 2014; Warren 2014, 2020).
The ethnosurvey, consisting of open- and closed-ended questions, was fielded at various points throughout each year, from 2012 to 2021. Of the 225 surveys conducted, 15 cases were dropped as they corresponded to participants taking the survey multiple times, in which case the most recent survey prevailed. The final sample consisted of 210 day laborers between 19 and 82 years of age. Of the participants, 100% were men and 83% were foreign-born (Central America: 9%; Mexico: 74%). The questionnaire includes sections on demographics, migration history, work history, experiences with employers on la 21, interactions with law enforcement, opinions about immigration policies, and future intentions. The lead author trained bilingual Latinx-identifying undergraduate and graduate students to conduct the survey, together with her. Students received 4 h of training on survey methodology and the ethnosurvey instrument, with clear instructions to treat the day laborers with the utmost respect. Students were chosen for their interest in the topic and bilingualism. The students and authors recruited day laborers by arriving on the day labor corners on weekends and weekdays all year round, usually around 7 to 10 in the morning since that is day laborers’ main schedule. Individuals had to be day laborers present on-site to participate in the study. There were no other inclusion criteria. Participant observation occurred as the lead author spoke with day laborers while students collected surveys. Field notes were recorded after each visit to the day labor corners.
Informed consent was obtained in the preferred language of the day laborer prior to commencing the survey. The study was conducted in accordance with the Declaration of Helsinki, under Texas A&M University Internal Review Board protocol number 2010-0379D. Interviewers respectfully approached day laborers, explained the study, asked if they wanted to participate in a 20–30-min survey, and offered a USD 25 grocery gift card. At the end of the survey, workers were given labor rights information, a flyer for a local immigration rights organization, and a specially made pocket notebook to jot down employer’s contact information, hours worked, and places of work to better be able to fight a case of wage theft. If an employer drove up during the survey, day laborers were encouraged to take the job and were still given the gift card and labor rights information. Response rates wavered between 50–75%, with workers noting how anti-immigrant rhetoric, especially during Trump’s presidency, affected their inclination to participate in the study. Day laborers shared that their desire to speak out against their criminalization was often what convinced them to participate. Central Americans are probably undercounted as they were the least likely to participate in the study. As newer immigrants, they were more distrustful of interviewers.

3.2. Measures

Independent variables. Immigrants’ legal status was determined by interviewers through cross-referencing answers to several questions. Subjects were not directly asked about their legal status, although most day laborers divulged it during the course of the survey. When asked about migration history, authorized subjects often shared the year they obtained Lawful Permanent Residence (LPR) or volunteered that they traveled often to their country of origin because they had a green card. Unauthorized day laborers were more likely to disclose their status when asked about interactions with employers and law enforcement. For example, when asked if law enforcement ever confiscated his papers, one day laborer said laughingly, “I don’t have papers”. Cross-referencing answers to determine legal status is an often-used method, having been found to be both valid and reliable (Cornelius 1982; Hernández et al. 2013; Massey 1987). Similarly, citizens’ race was determined by interviewers based on participants’ phenotype and subjects’ responses to the survey. Educational level was assessed by asking day laborers to report the highest grade or degree attained. English proficiency was assessed by requesting participants to self-classify in one of four categories of English-speaking fluency, ‘none,’ ‘a little,’ ‘I get by,’ and ‘well.’ The strength of local networks was determined by constructing an index that incorporates information on whether the interviewee: (a) lives in Bryan with friends or family, or found where to live with the help of friends, family, fellow workers, or paisanos (people from the same country of origin); (b) finds employment through friends, family, or paisanos; (c) arrived in Bryan due to presence of friends or family, or due to contact with an employer; (d) belongs to any social club or engages in team sports; and (e) attends any local church or participates in any community organization. Based on an arithmetic sum of the values for the above indicator variables, the index classifies networks as weak (0–1), medium (2–3), or strong (4–5). As for day laborers’ participation in specific industries, we constructed two indicator variables, one for construction and the other for landscaping, by considering the most recent jobs reported by the interviewee.
Dependent variables. Labor violations and work abuses were assessed through questioning participants directly about whether they have ever experienced particular situations. On the one hand, labor violations are actions that infringe on workers’ rights as established by the 1938 Fair Labor Standards Act, the 1970 Occupational Safety and Health Act, or the 1990 Texas Payday Law. They include (1) not being paid, (2) being paid less than agreed, (3) not given breaks or water, (4) being forced to work more hours than agreed, (5) being abandoned at the worksite, and (6) experienced violence at work. On the other hand, work abuses are not official violations although they are considered mistreatment. These include (1) insults, (2) threats to call ICE, (3) being threatened with deportation, and (4) telling day laborers that their social security numbers are incorrect. To analyze income, we constructed a measure of hourly wage based on the self-reported wages and hours worked by each day laborer during the month prior to the survey interview. This measure is the average pay rate of the two most recent jobs first reported by the interviewee. Since not all of their entrepreneurial work is performed full-time, comparing laborers in terms of gross monthly wage would not have been appropriate as it does not account for the contingency of this economic activity (Valdez et al. 2019). To discuss agency and resistance, we asked day laborers if they shared information about abusive employers with each other, and whether they knew of a place or person to whom they could report work abuses. We further assessed the accuracy of their knowledge on where to report work abuses.

3.3. Data Analysis Plan

STATA 16 (2020) was used for statistical analyses, performing chi-square tests, t-tests, and analyses of variance (ANOVA) where appropriate to account for the categorical or continuous nature of the variables. Given the small size of some subgroups (especially citizens), we believe analyses provide conservative assessments of differences between legal status and racial categories. We also utilized Dedoose Version 8.4.43 (2020) to analyze qualitative data, shedding light on the patterns uncovered in the quantitative analyses even when their results were not statistically significant due to small subsample sizes.

4. Results

This section provides descriptive statistics summarizing the sample, followed by a discussion on the stratification of hourly wages, the incidence of labor violations and work abuses by immigration status and racial categories, and tests of the hypotheses. We conclude the results section by bringing in qualitative data to demonstrate the relationship between labor violations, work abuse, and workers’ resistance.

4.1. Summary Statistics

Of the 210 participants in the study, 83% were foreign-born Latinos. Of these, 74% were born in Mexico, 5% in Honduras, 2% in El Salvador, and 2% in Guatemala. Eight foreign-born Latinos became citizens (4% of the overall sample). In contrast, 17% of day laborers interviewed were born in the U.S, with 8% being Black citizens, 5% Latinx citizens, and 3% White citizens. Finally, 55% are unauthorized immigrants and 24% are documented, and more than 64% of all immigrants arrived for the first time in the U.S. to look for jobs before 2001.
Table 1 provides further information about participants’ demographics. Overall, day laborers in the study sample reported ages ranging between 19 and 82 years, with a mean age of 45.3 years, and 14% of workers aged 60 or over. However, for the subsample of authorized immigrants, the mean age and proportion of older-aged workers are higher than average. The latter is consistent with results from ANOVA that confirm differences in mean ages between groups by immigration status and race, as shown in Table 2. Chi-square tests show further differences between groups in terms of proportion married or cohabiting, percentage of workers who send remittances, educational attainment, and network indices. The above significant differences point to the presence of variation in such indicators depending on respondents’ legal status and race. Unauthorized workers report the highest proportions of marriage or cohabitation (62%), remittance activity (85%), and lowest educational attainment (54% elementary school or less), while having stronger local networks (65% medium and 20% strong). Among immigrant workers, there are also significant differences in English proficiency with more unauthorized day laborers reporting no speaking skills (31%). On the other hand, there are no differences in the proportion of workers who have children nor in terms of participation in the construction sector. In contrast with the latter, participation in landscaping jobs differs significantly by immigration status and race.

4.2. Stratification of Hourly Wage

Analyzing the estimated hourly wage for each immigration status and racial category as shown in Table 3, we find a stratification of wages by race and immigration status. We hypothesized that higher hourly pay rates would be associated with higher-status workers, i.e., White and documented immigrants. While results from ANOVA do not show significant differences between subgroups in terms of mean wages per hour, a discernible gradation pattern emerges, with unauthorized workers receiving the least, followed by authorized immigrants, Latinx citizens, Black citizens, and then White citizens, displaying income stratification by legal status and race. Further, when compared to all other subgroups combined, unauthorized immigrants’ average payment per hour (USD 13.35) was found to be significantly smaller. Although the subgroup of White citizens received the highest mean pay rate (USD 17.70), no significant differences were found between them and all other subgroups combined. The latter is most likely due to the small number of White citizens interviewed and the large standard deviation in the hourly income of this subsample. Interestingly, only 3% (6) of day laborers received less than minimum wage. As noted by workers themselves, the main problem is not minimum wage violations but wage theft due to not paying for work completed and paying less than promised.

4.3. Incidence of Labor Violations and Work Abuse

Table 4 provides results on the incidence of labor violations and work abuses by immigration status and racial categories. Among labor violations, wage theft by underpayment and being abandoned at the worksite are significantly associated with laborers’ immigration status or racial category, with a greater proportion of Latinx immigrants and citizens reporting having experienced these types of violations at least once. However, contrary to expectations from the dominant theory, experiencing multiple labor violations and work abuses is not significantly associated with lower-status workers, i.e., non-White and undocumented immigrants. Although immigrants reported having been victims of two or more types of work violations and abuses at higher rates than citizens, chi-square tests revealed no statistically significant differences (see Table 5). Rather, day laborers from all immigration status and racial groups reported having experienced a type of work violation, with nonpayment being the most frequently reported (50%), as was the case in the NDLS (48%). Similarly, we find the next most frequently experienced work violations to be in the same ranking of occurrence as reported in the NDLS, i.e., no food/breaks, underpayment, working extra hours, being abandoned, and suffering violence. However, we find a much lower incidence (between 8 and 10 percentage points less) compared to the NDLS of the latter three.
Regarding work abuses, all groups reported being subject to insults (25%). The next most common abuse was being told their social security number was not good (16%), an abuse that was experienced by Latinx individuals, both immigrants and citizens. Given that day laboring is a cash business, asking for a social security number and reporting that the worker cannot be paid because the social security number is incorrect is a dodgy form of evading payment. Some authorized immigrants and Latinx citizens also received threats from employers about either calling ICE or getting deported. Despite the lack of statistically significant differences, the fact that only Latinx individuals experienced these threats evidences the racialization of Latinx men as undocumented immigrants, and therefore assumed by employers to be more vulnerable (Plankey-Videla and Cisneros Franco 2021).

4.4. Supply-Side versus Demand-Side Explanations

We earlier hypothesized that supply-side characteristics, including educational attainment, English proficiency, and strength of local networks, would moderate the effects of being a lower-status worker; on this matter, evidence was mixed (see Table 5). For unauthorized immigrants, higher educational attainment is associated with a lower incidence of work violations. More specifically, 53% of undocumented workers with a high school diploma or higher education have not experienced work violations, while 58% of those with elementary school or less education have been victims of two or more such violations. Education had no moderating effect for documented immigrants or citizens. Contrary to expectations, higher English proficiency was not consistently associated with a lower incidence of labor violations and work abuses for immigrants. While only 13% of undocumented day laborers who reported speaking English well have experienced two or more work abuses, that proportion reaches 50% for those with intermediate skills compared to 3% and 18% of those with little or no speaking skills. In other words, the hypothesized protective effect of being bilingual seems to be present only at more advanced levels. Still, it is not enough to decrease the occurrence of abuse. Similarly, 50% of unauthorized workers who reported speaking English well and 82% of workers with intermediate English proficiency have experienced two or more work violations compared to 67% and 32% of those with little or no speaking skills. As in the case of education, English proficiency had no moderating effects for authorized immigrants. Interestingly, Black citizens benefited most from stronger networks; 63% of those with medium-strength networks have not experienced work violations in contrast with 22% of those with weak networks. However, these findings were not statistically significant, and Black day laborers have, on average, weaker networks when compared with other subgroups.
Further, we hypothesized that a higher incidence of labor violations and work abuses would be associated with participating in the construction industry, one of the most precarious sectors as described in the literature; on this subject, evidence was mixed too. While participation in this industry and having ever been a victim of work abuse are associated, there are differences by legal status and citizen’s racial category. On the one hand, all Black citizens who have performed day labor work in construction have experienced one or more work violations, while 64% of nonconstruction workers have not been victimized. For other subgroups and the overall sample, we find no significant differences in labor violations in association with working in construction. Further, there is some evidence of differences in work abuse incidence for unauthorized immigrants depending on whether they are involved in landscaping, with 61% of workers in the industry not having been victimized. In this regard, qualitative data provides instances of day laborers’ experiences with wage theft and labor abuse. In the next section, we turn to workers’ stories.

4.5. Bringing Workers Back In

Simón, a 49-year-old Mexican day laborer, exemplifies the situation of many unauthorized men. While he and his two adult sons live and work in the U.S., his wife and two younger daughters live in central Mexico. Simón has come to the U.S. twice, once in 2009 and more recently in 2012, arriving at his brother’s house in Bryan. Knowing some English, he has worked in construction and the cement-laying industry but it has not been easy. Now he can only rely on informal work on la 21. He sends most of his money home to his wife and daughters, affecting his life in the U.S. Simón shares that he often does not have enough food to eat. However, that is not for a lack of trying to get work.
Simón stands on la 21 6 days a week waiting to be hired. The week previous to the survey he had only obtained three jobs, with the main one being cleaning out the remains of a burned-out house for 8 hours for which he earned USD 13 an hour. Asked if he has experienced any labor violations or work abuse, he shares:
Five times [employers] did not pay. One time they did not pay $350 for 2 day’s work. Another time for 5 day’s work. They were going to pay me [USD] 100 per day [of work]. Another time they told me they were going to pay [USD] 100 and only gave [USD]80. I am afraid to say anything because they can abandon me at the worksite and I will have to walk home.
Wage theft by employers robbed Simón of USD 870 he earned. He suffered two forms of wage theft: being paid less than promised and not being paid at all for work completed. In addition, another employer threatened to report him to ICE. Workers share information about abusive employers, so Simón knew that being abandoned at the worksite can mean walking home many miles. There are stories of day laborers having to walk home from 40 miles away after a long day’s work. The power differentials between workers and employers have meaningful and direct consequences on la 21. In total, 74% of unauthorized workers experience labor violations such as wage theft, while 37% endure labor abuse.
Saúl, a 52-year-old day laborer from south-central Mexico with high school education, is an LPR, yet still experiences wage theft. He has lived in the U.S. since 1985, raising U.S.-born children and attaining ownership of his own house. Although he only speaks some English, with papers, he has obtained a series of jobs, first growing cotton in Oklahoma, and then washing dishes, working at the local poultry plant, and now as a delivery driver in Texas. When work is slow, he goes to la 21. The same as 27% of other authorized day laborers, Saúl has a primary job. Similarly, 30% of unauthorized day laborers use the corners as a supplement to their regular job.
When he is not on call for his delivery job, for which he earns USD 17 an hour, Saúl waits for work on the corners of la 21. Authorized workers, similar to unauthorized ones, use la 21 as a second job a quarter of the time. For Saúl, that translates to going to la 21 3 days a week. The week before the survey he went to the corners 3 days and obtained 3 jobs, one installing tile (10 h for USD 10 an hour), another moving furniture (8 hours for USD 12 an hour), and the last at a carwash (3 h for USD 8 an hour). He shared his experiences of labor violations and work abuse:
I’ve experienced not receiving any payment a few times. One time we worked 15 hours and were only paid [USD] 100. Sometimes they do not bring water; they just tell us to drink from the hose. I get told about lots of violations.
Saúl recalls a job with a group of workers where each was paid USD 6.67 an hour, which is below the minimum wage of USD 7.25 an hour. He, the same as many, is especially bothered by not having been given water. To be told to drink from the hose is experienced as mistreatment, an affront to his dignity. Similar to Saúl, 78% of authorized day laborers experience labor violations, and 43% encounter work abuse.
Citizens also experience labor violations and work abuse. A native of Louisiana, Lucas, a 34-year-old Latinx citizen with junior high school education, recounts three forms of wage theft. Before coming to la 21, he framed houses, installed roofs, and laid concrete. Now he stands on the corners three days a week as his primary job. The week before the survey he had come out 3 days and obtained 3 jobs at very disparate rates. First, he helped frame a house for 12 h at USD 12 an hour, next he laid concrete for 6 h for USD 23 an hour, and lastly, he threw down sod for 3 h for USD 7.25 an hour. When asked about a series of labor violations and work abuses, he shared the following:
Last Monday they didn’t want to pay, and [they] wanted to pay less than agreed, but I told them “nah” and they paid. But a couple of them [employers] are shady. They act like they don’t want to pay you and pay you less. They also say “Can I pay you tomorrow?” and I tell them “nah, you may not come back”... [Once] we agreed upon 8–10 h and we ended with 13. Then [the employer] drove us to another site and he said “come on, we really need to finish this”, and [we] ended up working 14 h in total and getting paid [USD] 8 for the extra hours.
Although Lucas knows some of the ways that employers steal from day laborers and tries to protect himself from them, he reports not being paid three times, with one of the times being in the month prior to the interview. In addition, he has been made to work more hours than agreed, as he explained above, and was paid less than agreed. While Lucas states that he does not let employers insult him, he also acknowledges being insulted. Despite citizenship and English proficiency, Latinx citizen day laborers report the highest percent overall of labor violations (100%) and the highest rate of work abuse (44%) among citizens. Almost two-thirds (63%) of Latinx citizens used la 21 as a primary source of employment.
James, a 23-year-old Black day laborer with high school education, has experienced four types of labor violations and work abuse. In the past, he has worked in construction, plumbing, and laying concrete. Nowadays he goes out to the corners twice a week as his primary job. The week previous to the survey, he obtained two jobs. Given that Bryan/College Station is in a primarily rural county, there are agricultural jobs available. James worked as a farmhand for 8 h, earning USD 15 an hour. For his second job, he placed siding on a home for 8 h, also earning USD 15 an hour. When asked if he ever experienced nonpayment of wages on the day labor corners, he responded “All the time”. To the question as to whether he was ever paid less than promised, he replied “Lots”. James also reported being insulted frequently, adding “[There are] racists comments”. Several Black workers recounted experiences of racism with employers on the day labor corners. Comparable to Latinx citizens, about a third of Black workers denote day laboring as their primary economic activity. In total, 59% of Black day laborers experience labor violations and 27% work abuse.
Similarly, 71% of White citizens suffer labor violations but their rate of work abuse (17%) is less than that of Black citizens. Tony, a 25-year old White citizen, has been living in Bryan for 16 years. Single, with two children, he sometimes does not have enough to eat. He usually comes to la 21 Monday through Friday, but recently another day laborer helped him find a semiregular job with a roofing company. He is on call. Content with this new arrangement, he only came to the corners 2 days the week previous to the survey. In those 2 days, he obtained two jobs, one picking up lumber (4 h for USD 9 an hour) and landscaping (10 h for USD 10). Tony reports that he has not been paid for work twice, one of them being the week prior when he was picking up lumber. “He left us and said we’d get paid on Friday”, he recounts. The employer never returned.

4.6. Resistance Mechanism on Highway 21

Table 6 provides results relevant to discussions on resistance mechanisms in the face of work abuses and labor violations. Chi-square tests revealed statistically significant differences in groups engagement when it comes to sharing information about abusive employers as well as knowledge on where to make claims for labor abuse, a prerequisite for making claims for justice. Although 76% of all day laborers say information is shared in las esquinas, only 47% of Black workers agreed. Further, there are significant differences in workers’ self-assessed knowledge of defending themselves against abuses or violations. As hypothesized, being a citizen or authorized day laborer seems to be associated with knowing where to make claims for justice. However, the knowledge workers assert to have is not always accurate. Interestingly, upon examination of their responses, it was found that Latinx individuals (both immigrants and citizens) have more precise knowledge. Moreover, a worthwhile proportion of day laborers (9%) expressed having learned where to report work violations thanks to the community-engaged study throughout the last decade. On the other hand, as shown in Table 7, sharing information about abusive employers is associated with a higher incidence of work violations; the mechanism in motion could be that victimization has spurred resistance.
The vast majority of day laborers tell their fellow day laborers if an employer who drives up to la 21 is known to be abusive. Simón, the undocumented worker, similar to many others, recounts the words he yells out, “Don’t go with him. He does not pay”. That exact phrase was often repeated to the research team. Simón, who has medium-strength local network ties, says he knows his immigration rights (“don’t open the door”) but he does not know where to report abuses, though now, he says he knows the professor (first author). While he is anxious about the new state law SB 4 that allows local law enforcement to ask for legal status, he feels he has no other job option than to stand on the corners. “I am vulnerable but I would not leave”, he says when questioned if he would stay if law enforcement asked for legal status.
Saúl, who has papers to work with, also shares information. He complains that some employers “...go too far. Workers arrive here crying that he did not pay me and, well, there is nothing more that can be done [at that point]”. Different from most workers, Saúl has strong networks evidenced by family and friends in the area and participation in church and the soccer leagues. While he knows to report abuse, he is skeptical that it will work. He feels that since President Trump was elected, Mexicans are further criminalized and discriminated against. Although he is authorized, he feels apprehensive for his fellow workers who stand on the corner without authorization.
Another documented worker, 63-year-old Samuel, took matters into his own hands. While he does not know where to report a nonpaying employer, he protects himself and others by sharing information. An employer with a large fencing company who robbed him and a crew of two other workers USD 140 each, came back to la 21 to pick up more workers. Samuel yelled out, “They don’t pay!” Another time, an employer who skipped out on payment came back to the corners after 2 months. Samuel confronted him about the money he was owed. Then he reached into the car and snatched the employer’s mobile phone. The employer paid right away. Several day laborers reported using this strategy.
Lucas, the Latinx citizen, expounds on why it is necessary to share information. He noted, “Workers share abuse. They say ‘He don’t [sic] pay, he’s a slave driver.’ [They] are always telling you. Most [employers] are good but once in a while they are not and that is when we let each other know”. Although he knows his rights and pushes back when employers try to withhold payment or insult him, he is not always successful. Lucas, the same as 78% of other day laborers, does not know where to report abuse. He relies on his fellow day laborers to warn him of unscrupulous employers.
Neither James, the Black citizen, nor Tony, the White one, know where to report abuse. Although they do not speak Spanish, they are part of the day labor networks that share information about abusive employers. James reports that they share information “all the time. We see how the others behave”. Some English-only speakers recount how Spanish-speaking day laborers make signs to inform them which employer to avoid. Black day laborers, who often stand separate from other day laborers, have the weakest local networks and report the lowest incidence of sharing of employer abuse. Given the anti-Black sentiment in the broader Latinx and immigrant community, it is not surprising that Black day laborers are excluded from key networks to protect themselves (Ordóñez 2015).
Only 22% of day laborers note that they know where to report abuse: the Texas Workforce Commission, immigration rights organization, police, and unions. Interestingly, White citizens had the most incorrect information. The majority of them had worked for establishments with unions and noted the union as the place to report abuse. That would be correct if day laborers had a union, which they do not. Of the 41 day laborers who know where to report abuse, 39% learned it through this community-engaged research project. This is encouraging and calls for more outreach, since this is still a very small percentage of workers who know their rights.

5. Discussion

This study seeks to quantify worker mistreatment on day labor corners in Bryan, Texas, and to understand whether worker or industry characteristics best explain the prevalence of labor violations and work abuse. We found mixed results. Contrary to dominant theories that supply-side (worker) characteristics best elucidate poor labor practices, we found that higher indices of labor violations and work abuse are not associated with lower-status workers. That is, all workers, irrespective of legal status or citizenship, experienced abuse by employers. As related by Tony, being a White citizen does not offer protection from workplace violations. In addition, English proficiency among unauthorized and authorized migrant workers did not have a shielding effect. Other worker characteristics were less straightforward; for instance, having more education was associated with fewer violations but only for unauthorized workers.
While in general supply-side factors did not explain the prevalence of labor abuse, demand-side characteristics were slightly more illuminating. Given the high levels of exploitation in the construction industry, we expected construction to be associated with higher levels of wage theft and mistreatment. This holds true for Black day laborers but not for other groups of day laborers. However, as more data is gathered, more robust evidence of differences in violation or abuse incidence might emerge in connection to this industry and others less discussed in the literature, as in the case of landscaping.
In terms of wages, we find a gradation of wages with the lowest for unauthorized immigrants, then authorized immigrants, Latinx citizens, Black citizens, and lastly White citizens. Differences in wages per hour between unauthorized workers and other workers were statistically significant except when compared to White workers. This is probably due to the small sample size of White workers.
The survey also shows that workers collectively fight back against injustice; 76% of day laborers share information about abusive employers. However, Black citizen day laborers are often left out of these solidaristic networks. Only 47% of Blacks participate in this information sharing. As hypothesized, citizens and authorized day laborers have greater knowledge of where to go to defend their rights, although that information was not always correct. White citizens self-reported having the greatest knowledge of where to report abuse but they were by far the least informed.
In total, 78% of day laborers do not know where to report work abuse. Although similar to what the NDLS found (70%), that is a dismal number. However, an interesting finding is that experiencing more work abuse is positively related to knowing where to report work abuse. This may speak to a portion of workers actively seeking information and support after facing injustice. With information networks being so important on the day labor corners, continued workplace violations may lead to greater empowerment to seek support.
The implications of this mixed method study are multiple. While the study clearly establishes that labor violations and work abuses are prevalent on day labor corners, it also indicates that a considerable number of employers follow the law and treat workers well. That is, conscientious employers do not play on a level playing field; wage-theft disadvantages law-abiding employers. What happens on the day labor corners has ramifications that affect the larger labor market. This study also demonstrates that few workers know their rights. If more information was available, more workers would be able to defend their rights. This is a renewed call to community leaders, immigrant and worker rights organizations, and community-engaged researchers to work with day laborers to help them protect their rights.
Limitations to this study include a relatively small sample size with a still modest number of Black or White participants. Regarding sampling strategy, drawing results from a convenience sample comes with disadvantaged generalizability relative to probability samples. Nonetheless, this type of nonprobability sampling suits the very nature of day labor in las esquinas of Bryan, Texas, where the author and collaborators arrive directly at the field site to build rapport with workers and invite them to participate in the study. Another drawback is that surveys were not conducted evenly throughout the year; thus, some differences across immigration and racial lines in variables such as hourly pay rate or industry participation could go undetected due to a lack of consideration of work seasons. Furthermore, a larger sample could allow subdividing observations further into categories that not only distinguish whether day laborers participate in a particular industry or not (as indicator variables) but allows us to analyze industry overlap. The latter would involve using a variable whose categories include single industries and combinations of them, better capturing that day laborers tend to perform a wide variety of jobs that expand across different industries in a single month. Moreover, broader characterization of employers would be needed to pin down other potential effects of demand-side characteristics on day laborers’ experiences of labor violations work abuses.
Future research could incorporate additional questions to ask day laborers about employer attributes. It could also recruit employers to conduct further surveys or interviews to inquire about their hiring decisions and interactions with day laborers. Future work could also examine how day laborers use worker rights information, such as sharing information, deciding how to make claims individually or collectively. Better understanding the mechanisms that lead to claims-making will help community-engaged researchers and nonprofits assist workers in organizing and demanding their rights. Lastly, we suggest analyzing the impact of the coronavirus disease (COVID-19) pandemic on day laborers’ livelihoods, considering health and safety hazards as well as disruptions to the informal labor market they depend on.

6. Conclusions: Doing Critical Engaged-Community Research

The original study was designed as community-engaged scholarship, guided in its focus and content by the local community clinic. The goal was culturally relevant health information and a better understanding of what risk factors workers engaged in. It did not take aim at social structures (CIC Committee on Engagement 2005; Gordon da Cruz 2018). However, when the first author continued the study with a renewed focus on worker exploitation, it became a critical community-engaged study. According to Cynthia Gordon da Cruz (2017), critical community-engaged scholarship entails working collaboratively with communities, taking local expertise seriously, being racially conscious of systemic racism, pursuing an asset-based understanding of local communities and knowledge, and forthrightly claiming to work for justice.
The CTDLS is an example of a critical community-engaged study because the express goal is to quantify labor violations and work abuse experienced by day laborers in order to support worker and community efforts to obtain just compensation for day labor work. While local immigrant leaders did not partake in the survey development, they provided encouragement for the study to continue to gather data and are currently part of the group deciding how to use that data to combat wage theft. Following critical community-engaged research tenants, the study grew out of community expertise that day laborers suffered from the actions of unscrupulous employers. The research question was not if labor violations and abuse existed, but rather what kinds of mistreatment occurred and how they differed by race and citizenship status. Using an ethnosurvey, an instrument that includes closed- and open-ended questions, provides workers the opportunity to share their knowledge, opinions, and hopes. It takes an asset-based view of workers, knowing they have expertise to share (Gordon da Cruz 2017).
Conscious of the local racial dynamics, the study focuses on race and racial injustice. Located in the conservative semirural south, the Bryan/College Station community is hostile to immigrants and often dismisses the contributions of the Latinx and Black communities. Day laborers are seen as disposable. The goal of the CTDLS has been to make spaces where workers’ voices and experiences can be heard to bring awareness to the injustices that occur at la 21 and provide robust data that can be used by local efforts to effect change. This change comes in two forms. First, the survey ends in a Know Your Rights session. A labor and immigration rights packet, in addition to information about the one immigration rights organization in town, is distributed and discussed after surveys. Although academic colleagues cautioned that passing out this information could affect the data collected (i.e., knowledge about labor rights), the goal of the study was not data in and of itself, but rather what could be done with this data. The labor and immigration rights packet had the dual effect of educating workers about their labor rights and making them aware of local collective efforts for immigrant justice and services. The immigration rights organization, the Brazos Interfaith Immigration Network (BIIN), began 10 years ago fighting wage theft cases and advocating for workers’ rights. It served as an incubator for a worker center that later folded, leading BIIN to continue wage theft cases, with the first author being one of the key volunteers working in this area. BIIN also refers larger cases to the Equal Justice Center and Workers Defense Project in Austin.
The second form of change is a future labor campaign. The lead author and community leaders plan to organize a city-wide wage theft ordinance. Although Texas has a law that permits individuals to sue unscrupulous employers, there are very few labor attorneys who take on individual cases in Texas; the money is just not worth it. Instead, day laborers and other workers have to rely on the few nonprofits available in the state, and these tend to take on larger cases or groups of workers where the legal case has a good chance of succeeding to serve as an industry warning. While volunteers at BIIN will continue to take on individual cases, contacting employers to convince them to pay, and if not, trying to place a lien on their properties until they pay, community activists seek a more structural response by way of a city-wide wage theft ordinance.
A city-wide wage theft ordinance would require employers to (1) disclose any outstanding wages owed or judgments against them for unpaid wages and (2) pay all wages due in order to obtain or renew a construction permit or business license. In addition, we seek to follow the examples of El Paso and Houston and keep a database of nonpaying employers to shame them into compliance (Bova 2018; National Employment Law Project 2011). These policies necessitate vigilant local community leaders so that the twin cities of College Station and Bryan enforce the ordinance and keep the database up-to-date and public.
While it would be ideal for nonpaying employers to also have to pay a fine for wage theft, we must be realistic of what is possible given the local conservative and anti-immigrant political landscape. On the conservative Bryan and College Station city councils sit developers, contractors, and realtors. Marshaling support from key local churches and using the locally resonant language of justice, faith, and morality is the most likely course to achieve the desired outcome of changing employer discriminatory and unjust practices. In the meantime, we, the research team, will continue working with BIIN, other community leaders, and day laborers to fight for that measure of respect and justice workers deserve.

Author Contributions

Conceptualization, N.P.-V.; methodology, N.P.-V.; software, N.P.-V. and C.L.C.F.; formal analysis, C.L.C.F.; investigation, N.P.-V. and C.L.C.F.; writing—original draft preparation, N.P.-V. and C.L.C.F.; writing—review and editing, N.P.-V. and C.L.C.F.; visualization, C.L.C.F.; project administration, N.P.-V.; funding acquisition, N.P.-V. All authors have read and agreed to the published version of the manuscript.


Mexican-American and Latino Research Center, Carlos H. Cantú Hispanic Education and Opportunity Endowment, and Sociology Department, Texas A&M University.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of Texas A&M University (protocol 2010-0379D, approved July 2, 2010 through 2022).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Data not available at this time.


We acknowledge and thank the undergraduate and graduate students that made this project possible: Cindy Barahona, Yvonne Carrillo, Carlo Chunga, Maria Davila, Amy Diaz, Edmundo García, Michel Infante, Kobe Landry, Perla Lopez, Itzia Medrano, Iveri Medrano, Angélica Menchaca, Oscar Morales, David Orta, Mario Paez, Fernanda Preciado, Mariah Alvarez Ramirez, Angela Rodriguez, and Juan Salinas. We also thank Mary Campbell and Robert Mackin for helpful comments on the manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

ID:    ---    ---    ---      ---    
   Month     Day     Year    Interviewer’s  Case No.
Central Texas Day Laborer Survey
INSTRUCTIONS TO INTERVIEWER: Greet subject, establish cordial relationship, and read consent form before beginning survey
*** Must answer/ ### can skip
What year did you take previously? _______________
Section One: Demographic Information.
First, I am going to ask you about yourself and your family
** What is your year of birth? ______________________ Sex: Male_____ Female ____
** In what country were you born? In what state and municipality?
Country:State: Municipality (county_) _____________
Are you currently:
Single (never married):Currently married or cohabitating:Separated or divorced:Widowed:
If you have a spouse, go to question 4, IF NOT, go to question 5
Can I ask you some questions about your spouse?
Spouse’s age:NationalityEducation:
9th grade (Secundaria/Jr. High)  
12th grade (Preparatoria/ high school)  
Technical degree (Carrera técnica)  
Where does your spouse reside? Country/city
Does your spouse work?Type of work Does your spouse live with extended family or alone?
Do you have children? Si   No
How many?    
If there are no children, go to question 8
Are your children with you here in the U.S. or back in country of origin
Here  Country of origin  Both  
** How old are your children, are they in school or do they work?
Age (from eldest to youngest)Sex (M/F)School grade (in YEARS) or if she/he is working, what kind of work?Is she/he with you (Yes)
or in country of origin (NO)
Was she/he born in the U.S.?
** What is the highest grade or degree you have attained?
Primary9th grade
(Junior High)
12th grade
(High School)
technical schoolUniversity
How well can you:
NoneA littleI get byWell
Speak English
Read English
Write English
What type of housing do you currently live in?
An apartmentA houseA trailer/mobile home
On the street/open air settingDorms/ Group quartersHotelOther, specify:
Do you own or rent this place?
OwnRentlive with familyRent with othersFree
How did you find out about this place?
familyFriendFellow workerPaisano (people from your country of originOther, specify
How many people currently live there (including yourself)?    
How many are people 16 years or older who live with you are currently working or looking for work (including yourself)? __________________
What is your portion of rent or mortgage each month? That is, how much do you pay for rent each month, or if you are paying towards owning your home, how much do you pay a month? [ask for the total rent and portion of rent paid by individual]
Total rent/mortgage $_________ Your share of rent/mortgage$ ________
How much do you pay for utility services each month?
They are included in rentIf you have a car, how much do you pay in gasOther, specify
Which of these statements best describes your (or your co-residents’) food consumption in the past month?
We have enough of the kinds of food we want to eatWe have enough but not the kind of food we want to eatWe sometimes do not have enough to eatWe often do not have enough to eatRefused to answer
On average, how much do you pay for food each week?        
Where do you get your food?____________
I cook at home (%)I eat out (%)Someone else cooks (%)Other, specify (%)
Do you send money back to your country? [remit] Yes ______ No______
If they respond NO, go to question 23:Yes, parents or parents-in-law:Yes, spouse and/or children:Yes, siblings:Others, specify:
If you have sent money, how often do you send money?
2–3 times a monthOnce a month1–3 times a month4–7 times a month8–11 times a month
On average, how much do you send?per week_____per month ______times a year ______Why do you send money? Check all that apply
Debt payment:Food:Clothing:Housing:Medical expenses:
Children’s education:Financing a business:Legal expenses:Hometown associations/projects:Other, specify:
What other income does your family have? For example, in addition to formal employment previously mentioned, does your spouse (S) or do your children (C) participate in any of the following activities? If they do, around how much do they earn a month?
Sell antojitos (street food)Sell clothesTake care of childrenOther, specify:
Section Two. Now I am going to ask you about your migratory and labor history:
** When did you come to the U.S. for the first time to look for work?
Month _______ Year ______
How long did you stay on your first trip?
Years:    Months: ______ To date (this is your first trip):
If this is your first trip, go to question 27
How many times total have you come to the U.S. for employment? ________
If this is not the first trip to the U.S., when did you arrive in the U.S. most recently? Month Year________
Where have you worked and what kinds of work have your done? FOR CITIZENS, ASK ABOUT WORK THEY HAVE DONE BEFORE THEY CAME TO 21 AS DAY LABORER
State, cityYearLength of stay ( of work (specify)
What is the main reason you came to Bryan/College Station?
(If respondent does not respond or the answer is very vague, ask the following:
I have friends and family here:I heard there were better job opportunities hereContacts with an employerEmployer recruited in country of origin
Coyotes brought me hereOther, specify:
Did you work in your country of origin prior to migrating to the U.S.? Yes________ No ___________ IF NO, SKIP TO 31
If yes, What kind of work did you do (main occupation) in your last job before you immigrated to the U.S.?
Section Three. In this section I will be asking about the work you perform now and the experiences you have had with your employers, law enforcement, and the immigration authorities.
How many days a week did you come here, to HWY 21, looking for work in the LAST MONTH? ___________
How many days did you come LAST WEEK? [Or the week before if they did not come last week; note if answer is for 2 weeks ago] ___________
How many jobs did you obtain last week? [Or the week before if they did not come last week; note if answer is for 2 weeks ago] __________
What types of work did you perform LAST MONTH? Describe the type of work, the hours worked, and how much you were paid.
Type of work (be specific)Hours (or days and hours per day)Amount paid PER HOUR OR PER DAYDid you work for an individual
(I) or for a company (C)
Do you have another job? Yes_____ No______
If you do, where do you work? What do you do? How do you balance your time and different jobs?
** FOLLOW UP: Compared to the last time you took the survey, are there more or less jobs here at the day labor corners? Why do you think there are more/less/the same amount of jobs?
If subject does not have a second job (no on 35), go to question 37
How did you find out about this job or how do you find employment? (Check all that apply)
Through family:Through friends:Through a paisano:Through hometown associationThrough an employment center
Through a temp agency:Through a recruitrThrough a day labor site OTHER THAN HWY 21:Through advertisements (TV, radio, internet, newspapers):Other, specify:
How often have you experienced the following abuses from employers when working as a day laborer in Bryan/College Station?
0 times1 or more, specify numberIn the last month?Comments
Paid less than agreed
Abandoned at work site
Not given food, water, or breaks
Insults or threats
Worked forced to work more hours than agreed
Was told that you would be deported
Was told that your social security number was no good
Other, specify:
General comments:
Do day laborers share information about abusive employers? Yes___No____
Is it recent that day laborers share information about abusive employers? YesNo
Yes, specify:                    
Do you know of a place or person to whom you can report workplace abuses?
Yes, specify:                    
Do you know if there are any immigrant or day laborers’ associations to defend your rights?
Yes, specify:                    
Do you belong to any social club or sports club? Do you go to the park to play soccer? Describe.
Do you attend any local church or participate in any community organizations? Describe.
What kinds of experiences have you had with the Bryan or College Station Police departments and/or Sheriff’s office? What do you know about how they treat immigrants?
How frequently have you experienced the following from Bryan/College Station police or sheriff?
0 times1 or more, specifyCSPD, BPD,
Sheriff (S)
Insults or harassment
Confiscated papers
Asked about your immigration status
Stopped and cited
(C) or arrested (A) for not having a drivers license
Other, speciy

What do you know about the recent immigration laws passed in Arizona and Alabama?
Some say the police and sheriff’s office are stopping Hispanic immigrants more often lately. What do you think? Why? Does this affect you in terms of seeking employment here on the 21 corners?
Do you know if Texas has passed laws that restrict rights or services to undocumented immigrants? Do you know about the new Texas law SB4, which permits police and sheriff to ask legal status? What do you think about this law? Does it affect you in any way?
Compared to the last time you took the survey, is there a change in how immigrants are treated?
What do you think about President Trump? Specifically, what do you think about his immigration related policies?
Has the way employers treat day laborers changed since President Trump’s election? Is it better, worse or the same? Why do think so? Do employers treat you better/worse/the same since the last time you took the survey? Why do you think?
NEW QUESTION: What do you think about President BIDEN? Specifically, what do you think about his immigration related policies?
Do you know anyone who has been affected by the anti-immigrant climate we see today?
In what ways? Please explain. Do you know anyone who has been deported? Could you say more?
Do you worry about ICE coming here or to people’s homes?
Do you think there could an immigration reform? What should it look like?
Fifth and last section. We are interested in your short-term plans.
How long do you think you will you stay in Bryan/College Station?
Less than 6 monthsBetween 6 and 12 monthsBetween one and two yearsMore than two yearsUntil the work endsPermanentlyI don’t know
Do you have plans to go to another place?
YesWhere?____________________ No
Why ? Have your plans changed since the last time you took the survey?
I have family thereThey say there is work there (what kind of work?)I was offered a job. What kind of job? Who offered job?Other, specify
Would you keep working here if the policy and sheriff began to regularly stop here and ask people’s legal status? Why or why not?
Thank you for your participation! Provide the person interviewee HEB gift card
Length of Interview:    
Date of Interview:
Interviewer’s Name:
Accompanying interviewer’s name:
Respondent’s attitude:
Friendly and interestedCooperative but not particularly interested (C)Impatient and restless (I)Hostile (H)
Adapted from the Survey for the New Orleans Mexican Mobile Consular Visit, Elizabeth Fussell, 2007.
Do you believe they are documented or undocumented? Why?
Description of respondent and interview:


  1. Alonzo, Armando. 2018. Contemporary Social and Legal Conditions of Immigrants in the Brazos Valley of Texas. Paper presented at the Race, Identity & Social Equity Conference, Texas A&M University, College Station, TX, USA, February 8. [Google Scholar]
  2. Bernhardt, Annette, Heather Boushey, Laura Dresser, and Chris Tilly. 2008. The Gloves-off Economy: Workplace Standards at the Bottom of America’s Labor Market. Ithaca: Cornell University Press. [Google Scholar]
  3. Bernhardt, Annette, Michael W. Spiller, and Diana Polson. 2013. All Work and No Pay: Violations of Employment and Labor Laws in Chicago, Los Angeles and New York City. Social Forces 91: 725–46. [Google Scholar] [CrossRef]
  4. Bernhardt, Annette, Siobhan McGrath, and James DeFilippis. 2007. Unregulated Work in the Global City. New York: Brennan Center for Justice. [Google Scholar]
  5. Bova, Gus. 2018. Wage Wars. Texas Observer. The Texas Observer. Available online: (accessed on 20 October 2021).
  6. Center for Migration Studies. 2014. Estimates of the Unauthorized Population. Dataset Based on the Augmented American Community Survey Datafiles Hosted by IPUMS (Integrated Public Use Microdata Series), 2010 to 2014. New York: Center for Migration Studies. [Google Scholar]
  7. Committee on Institutional Cooperation (CIC) Committee on Engagement. 2005. Engaged Scholarship: A Resource Guide. Available online: (accessed on 13 December 2021).
  8. Cornelius, Wayne. 1982. Interviewing Undocumented Immigrants: Methodological Reflections Based on Fieldwork in Mexico and the U.S. International Migration Review 16: 378–411. [Google Scholar] [CrossRef]
  9. Creswell, John W. 2015. Revisiting Mixed Methods and Advancing Scientific Practices. In The Oxford Handbook of Multimethod and Mixed Methods Research Inquiry. New York: Oxford University Press. [Google Scholar]
  10. Dedoose Version 8.4.43. 2020. Los Angeles: Socio Cultural Research Consultants LLC., Available online: (accessed on 29 December 2021).
  11. Department of Labor. n.d. Day Laborers. Available online: (accessed on 22 October 2021).
  12. Durand, Jorge, Douglas S. Massey, and Karen A. Pren. 2016. Double Disadvantage: Unauthorized Mexicans in the US labor market. The ANNALS of the American Academy of Political and Social Science 666: 78–90. [Google Scholar] [CrossRef]
  13. Fine, Janice, and Jennifer Gordon. 2010. Strengthening Labor Standards Enforcement through Partnerships with Workers’ Organizations. Politics & Society 38: 552–85. [Google Scholar]
  14. Flores-Yeffal, Nadia Y. 2013. Migration-Trust Networks: Social Cohesion in Mexican US-Bound Emigration. College Station: Texas A&M University Press. [Google Scholar]
  15. Fussell, Elizabeth. 2011. The Deportation Threat Dynamic and Victimization of Latino Migrants: Wage Theft and Robbery. Sociological Quarterly 52: 593–615. [Google Scholar] [CrossRef]
  16. Gammage, Sarah. 2008. Working on the Margins: Migration and Employment in the United States. In The Gloves-Off Economy: Workplace Standards at the Bottom of America’s Labor Market. Edited by Annette D. Bernhardt, Heather Boushey, Laura Dresser and Chris Tilly. Ithaca: Cornell University Press, pp. 137–61. [Google Scholar]
  17. General Accounting Office. 2002. Worker Protections: Labor’s Efforts to Enforce Protection for Day Laborers Could Benefit from Better Data and Guidance; Washington, DC: General Accounting Office. Available online: (accessed on 1 October 2021).
  18. Gleeson, Shannon. 2010. Labor Rights for All? The Role of Undocumented Immigrant Status for Worker Claims Making. Law & Social Inquiry 35: 561–602. [Google Scholar]
  19. Gleeson, Shannon. 2012. Conflicting Commitments: The Politics of Enforcing Immigrant Worker Rights in San Jose and Houston. Ithaca: Cornell University Press. [Google Scholar]
  20. Gleeson, Shannon. 2016. Precarious Claims: The Promise and Failure of Workplace Protections in the United States. Oakland: University of California Press. [Google Scholar]
  21. Golash-Boza, Tanya Maria. 2015. Immigration Nation: Raids, Detentions, and Deportations in Post-9/11 America. New York: Routledge. [Google Scholar]
  22. Gomberg-Muñoz, Ruth. 2010. Willing to Work: Agency and Vulnerability in an Undocumented Immigrant Network. American Anthropologist 112: 295–307. [Google Scholar] [CrossRef][Green Version]
  23. Gordon da Cruz, Cynthia. 2017. Critical community-engaged scholarship: Communities and universities striving for racial justice. Peabody Journal of Education 92: 363–84. [Google Scholar]
  24. Gordon da Cruz, Cynthia. 2018. Community-engaged scholarship: Toward a shared understanding of practice. The Review of Higher Education 41: 147–67. [Google Scholar] [CrossRef]
  25. Haro, Alein Y., Randall Kuhn, Randall Rodriguez, Nik Theodore, Edwin Meléndez, and Abel Valenzuela. 2020. Beyond Occupational Hazards: Abuse of Day Laborers and Health. Journal of Immigrant and Minority Health 22: 1172–83. [Google Scholar] [CrossRef]
  26. Hernández, Maria G., Jacqueline Nguyen, Saskias Casanova, Carola Suárez-Orozco, and Carrie L. Saetermoe. 2013. Doing No Harm and Getting It Right: Guidelines for Ethical Research with Immigrant Communities. New Directions for Child and Adolescent Development 141: 43–60. [Google Scholar] [CrossRef]
  27. Herrera, Juan. 2016. Racialized Illegality: The Regulation of Informal Labor and Space. Latino Studies 14: 320–43. [Google Scholar] [CrossRef]
  28. Hiemstra, Nancy. 2010. Immigrant “Illegality” as Neoliberal Governmentality in Leadville, Colorado. Antipode 42: 74–102. [Google Scholar] [CrossRef]
  29. Hong, Y. Alicia, Aurelia Lorena Murga, Nancy Plankey-Videla, and Mario Javier Chavez. 2015. HIV/STI Risks in Latino Day Laborers in Central Texas: A Mixed-Method Study. Health Psychology and Behavioral Medicine 3: 315–22. [Google Scholar] [CrossRef][Green Version]
  30. Kalleberg, Arne. L. 2011. Good Jobs, Bad Jobs: The Rise of Polarized and Precarious Employment Systems in the United States, 1970s to 2000s. New York: Russell Sage Foundation. [Google Scholar]
  31. Mansoor, Sanya, and Cassandra Pollock. 2017. Everything You Need to Know about “Sanctuary Cities” Law. The Texas Tribune. May 8. Available online: (accessed on 1 October 2021).
  32. Massey, Douglas S. 1987. The Ethnosurvey in Theory and Practice. International Migration Review 21: 1498–522. [Google Scholar] [CrossRef]
  33. Massey, Douglas S., and Kerstin Gentsch. 2014. Undocumented Migration to the United States and the Wages of Mexican Immigrants. International Migration Review 48: 482–99. [Google Scholar] [CrossRef] [PubMed][Green Version]
  34. Massey, Douglas S., Jorge Durand, and Nolan J. Malone. 2003. Beyond Smoke and Mirrors: Mexican Immigration in An Era of Economic Integration. New York: Russell Sage Foundation. [Google Scholar]
  35. McPherson, Elizabeth. 2011. Texas Legislature Passes Groundbreaking Wage Theft Bill. Construction Citizen. June 9. Available online: (accessed on 1 October 2021).
  36. Menjívar, Cecilia, and Leisy J. Abrego. 2012. Legal Violence: Immigration Law and the Lives of Central American Immigrants. American Journal of Sociology 117: 1380–421. [Google Scholar] [CrossRef][Green Version]
  37. Milkman, Ruth. 2008. Putting Wages Back into Competition: Deunionization and Degradation in Place-bound Industries. In The Gloves-Off Economy: Workplace Standards at the Bottom of America’s Labor Market. Edited by Annette D. Bernhardt, Heather Boushey, Laura Dresser and Chris Tilly. Ithaca: Cornell University Press, pp. 65–90. [Google Scholar]
  38. Miller, Richard E., and Austin Sarat. 1980. Grievances, Claims, and Disputes: Assessing the Adversary Culture. Law and Society Review 15: 525–66. [Google Scholar] [CrossRef][Green Version]
  39. Moss, Philip, and Chris Tilly. 2001. Hiring in Urban Labor Markets. In Sourcebook of Labor Markets: Evolving Structures and Processes. Edited by Ivar Berg and Arne L. Kalleberg. Boston: Springer, pp. 601–43. [Google Scholar]
  40. National Employment Law Project. 2011. Winning Wage Justice: An Advocate’s Guide to State and City Policies to Fight Wage Theft. Available online: (accessed on 1 October 2021).
  41. Negi, Nalini Junko. 2013. Battling Discrimination and Social Isolation: Psychological Distress among Latino Day Laborers. American Journal Community Psychology 51: 164–74. [Google Scholar] [CrossRef]
  42. Ordóñez, Juan Thomas. 2015. Jornalero: Being a Day Laborer in the USA. Berkeley: University of California Press. [Google Scholar]
  43. Organista, Kurt C., Woojin Jung, and Torsten B. Neilands. 2019. Working and Living Conditions and Psychological Distress in Latino Migrant Day Laborers. Health Education & Behavior 46: 637–47. [Google Scholar]
  44. Piore, Michael J. 1979. Birds of Passage. New York: Cambridge University Press. [Google Scholar]
  45. Piore, Michael J., and Andrew Schrank. 2018. Root-Cause Regulation: Protecting Work and Workers in the Twenty-First Century. Cambridge: Harvard University Press. [Google Scholar]
  46. Plankey-Videla, Nancy. 2021. The Deportability Regime: From Bad to Worse in Central Texas under Obama and Trump. In Migration, Racism and Labor Exploitation in the World System. Edited by Denis O’Hearn and Paull Ciccantell. New York: Taylor & Francis Press, pp. 78–98. [Google Scholar]
  47. Plankey-Videla, Nancy, and Cynthia Luz Cisneros Franco. 2021. “Trump Gave Them Wings”: Precarious Employment, Legal Status, Citizenship, and Racism on La Esquina. Paper presented at the American Sociological Association, Chicago, IL, USA, August 10. [Google Scholar]
  48. Provine, Doris M., Monica W. Varsanyi, Paul G. Lewis, and Scott H. Decker. 2016. Policing Immigrants: Local Law Enforcement on the Front Lines. Chicago: University of Chicago Press. [Google Scholar]
  49. Quesada, James, Laurie Kain Hart, and Philippe Bourgois. 2011. Structural Vulnerability and Health: Latino Migrant Laborers in the United States. Medical Anthropology 30: 339–62. [Google Scholar] [CrossRef][Green Version]
  50. Quesada, James, Sonya Arreola, Alex Kral, Sahar Khoury, KurtC Organista, and Paula Worby. 2014. “As Good As It Gets”: Undocumented Latino Day Laborers Negotiating Discrimination in San Francisco and Berkeley, California, USA. City & Society 26: 29–50. [Google Scholar]
  51. Rabito, Felicia A., Sara Perry, Oscar Salinas, John Hembling, Norine Schmidt, Patrick J. Parsons, and Patricia Kissinger. 2011. A Longitudinal Assessment of Occupation, Respiratory Symptoms, and Blood Lead Levels among Latino Day Laborers in a Non-Agricultural Setting. American Journal of Industrial Medicine 54: 366–74. [Google Scholar] [CrossRef] [PubMed]
  52. Rathod, Jayesh M. 2016. Danger and Dignity: Immigrant Day Laborers and Occupational Risk. Seton Hall Law Review 46: 813–82. [Google Scholar]
  53. STATA 16. 2020. College Station: StataCorp LLC.
  54. Teddlie, Charles, and Abbas Tashakkori. 2010. Overview of Contemporary Issues in Mixed Methods Research. In Sage Handbook of Mixed Methods in Social and Behavioral Research, 2nd ed. Edited by Abbas Tashakkori and Charles Teddlie. Los Angeles: Sage, pp. 1–44. [Google Scholar]
  55. Texas Workforce Commission. n.d. Texas Payday Law. Available online: (accessed on 1 October 2021).
  56. Theodore, Nik, Edwin Meléndez, Abel Valenzuela, and Ana Luz Gonzalez. 2008. Day Labor and Workplace Abuses in the Residential Construction Industry: Conditions in the Washington, DC Region. In The Gloves-off Economy: Workplace Standards at the Bottom of America’s Labor Market. Ithaca: Cornell University Press, pp. 91–109. [Google Scholar]
  57. Torres, Rebecca, Rich Heyman, Solange Munoz, Lauren Apgar, Emily Timm, Cristina Tzintzun, Charles R. Hale, John Mckiernan-Gonzalez, Sjannon Speed, and Eric Tang. 2013. Building Austin, Building Justice: Immigrant Construction Workers, Precarious Labor Regimes and Social Citizenship. Geoforum 45: 145–55. [Google Scholar] [CrossRef]
  58. Transactional Records Access Clearinghouse (TRAC). 2020. Latest Data: Immigration and Customs Enforcement Detainers, ICE Data through April 20: Brazos County, Texas. Syracuse University. Available online: (accessed on 5 January 2021).
  59. U.S. Census Bureau. 2019. American Community Survey Quick Facts. Available online:,collegestationcitytexas/PST045218 (accessed on 5 January 2021).
  60. Valdez, Zulema, Nancy Plankey-Videla, Aurelia Lorena Murga, Angélica Menchaca, and Cindy Barahona. 2019. Precarious Entrepreneurship: Day Laborers in the U.S. Southwest. American Behavioral Scientist 63: 225–43. [Google Scholar] [CrossRef]
  61. Valenzuela, Abel. 2003. Day labor work. Annual Review of Sociology 29: 307–33. [Google Scholar] [CrossRef]
  62. Valenzuela, Abel, Nik Theodore, Edwin Meléndez, and Ana Luz Gonzalez. 2006. On the Comer: Day Labor in the United States. Los Angeles: UCLA Center for the Study of Urban Poverty Report. [Google Scholar]
  63. Visser, M. Anne, Nik Theodore, Edwin J. Meléndez, and Abel Valenzuela Jr. 2016. From Economic Integration to Socioeconomic Inclusion: Day Labor Worker Centers as Social Intermediaries. Urban Geography 38: 243–65. [Google Scholar] [CrossRef]
  64. Waldinger, Roger, and Michael I. Lichter. 2003. How the Other Half Works: Immigration and the Social Organization of Labor. Berkeley: University of California Press. [Google Scholar]
  65. Warren, Robert. 2014. Democratizing Data about Unauthorized Residents in the United States: Estimates and Public Use Data, 2010 to 2013. Journal on Migration and Human Security 3: 305–28. [Google Scholar] [CrossRef]
  66. Warren, Robert. 2020. Reverse Migration to Mexico Led to US Undocumented Population Decline: 2010 to 2018. Journal on Migration and Human Security 8: 32–41. [Google Scholar] [CrossRef]
  67. Workers Defense Project in Collaboration with the Division of Diversity and Community Engagement at the University of Texas at Austin. 2013. Build a Better Texas: Construction Working Conditions in the Lone Star State. Austin: Workers Defense Project. [Google Scholar]
Table 1. Demographic characteristics by immigration status and citizens’ racial categories a.
Table 1. Demographic characteristics by immigration status and citizens’ racial categories a.
Sample size
Number of participants1165119177210
Percentage from N (%)5524983100
Mean (s.d.)43 (12)52 (11)44 (10)43 (10)38 (13)45 (12)
60+ years (%)93356014
Marital status (%)
Married or cohabiting62573212052
Family (%)
Children born in the U.S. b1937---26
Country of origin (other than the U.S.) (%)
El Salvador18---2
Year of first arrival in the U.S.
Arrived before 2001 (%)519427--59
Educational attainment c (%)
Elementary school or less5449170043
Junior high school283139292930
High school or more182044717126
English proficiency d (%)
A little5057---52
I get by1018---13
Network Index (%)
Participation in industry e (%)
a All values have been rounded to the closest integer. b These percentages refer only to proportions of children born in the U.S. from immigrant parents. c Three participants did not report educational attainment. d Considering self-reported speaking skills. e Considering the two most recent jobs first reported by the interviewee.
Table 2. Tests comparing subsamples of day laborers by demographic variables of interest.
Table 2. Tests comparing subsamples of day laborers by demographic variables of interest.
Variable aTest Statistic bp-Value c
Age6.270.0001 ***
Married or cohabiting28.360.000 ***
Children born in the U.S.16.480.0001 ***
Remits9.110.004 ***
Educational attainment41.840.000 ***
English proficiency12.060.010 ***
Network index19.340.020 **
Landscaping8.210.088 *
a All tests involve comparing the five groups of day laborers with each other except for those regarding the following variables: remittances (for which comparisons are between the three groups of Latinx laborers), having children born in the U.S., and English proficiency (for which comparisons are between undocumented and documented immigrants). b F-statistic for the variable age to test differences in means. Chi-square for all other variables to test independence. All values have been rounded to the nearest hundredth. c Significance at the * 10% level; ** 5% level; *** 1% level.
Table 3. Hourly income a (adjusted to 2021 USD).
Table 3. Hourly income a (adjusted to 2021 USD).
ImmigrantCitizenAll b
Wage measure
Mean c (s.d.)13.35 d(4.43)14.19 (7.57)15.11 (3.65)15.97 (7.54)17.70 (18.13)14.06 (6.27)
a This measure is the average pay rate of the two most recent jobs first reported by the interviewee. b 24 missing values (11%). c Comparing between the five groups of day laborers: ANOVA’s F-statistic = 1.33; p-value = 0.2590. d Comparing unauthorized immigrants’ mean hourly income against all other categories combined: ANOVA’s F-statistic = 3.18; p-value = 0.0761.
Table 4. Incidence of work violations and abuses a,b.
Table 4. Incidence of work violations and abuses a,b.
ImmigrantCitizenAllp-Value c
Labor violations
No food/breaks4839422929430.477
Underpayment4439682429430.051 *
Worked extra hours2216373514220.198
Abandoned22183200190.028 **
Work abuses
Told SSN was not good20205--160.355
Threat of calling ICE10160--100.182
Threat of deportation61211--70.396
a All values have been rounded to the closest integer. b Results refer to the percentage of day laborers that have experienced the labor violation or abuse at least once in a job obtained at la 21. c Significance at the * 10% level; ** 5% level.
Table 5. Tests of independence between the incidence of labor violation or work abuse and variables of interest a–c.
Table 5. Tests of independence between the incidence of labor violation or work abuse and variables of interest a–c.
Labor Violations ExperiencesWork Abuse Experiences
01≥2p-Value d01≥2p-Value d
Educational attainment 0.017 ** 0.826
Elementary school or less182358 582419
Junior high school26668 701713
High school or more531632 682111
English proficiency 0.017 ** 0.004 ***
None442432 76213
A little181667 542918
I get by9982 50050
Well381350 88013
Network Index 0.702 0.397
Weak192556 561331
Medium261955 622513
Strong30961 701713
Participation in industry 0.965 0.094 *
Landscaping261757 612811
No landscaping261955 651322
Participation in industry 0.482 0.760
Construction212158 672014
No construction301555 592417
Educational attainment 0.928 0.873
Elementary school or less242452 562420
Junior high school251956 632513
High school or more113356 504010
English proficiency 0.289 0.396
None60040 602020
A little213148 522424
I get by221167 78220
Well02971 50500
Network Index 0.564 0.475
Weak133850 383825
Medium261658 652610
Strong183645 502525
Participation in industry 0.125 0.857
Landscaping301060 522919
No landscaping173350 602713
Participation in industry 0.202 0.217
Construction173944 72226
No construction251659 483021
Educational attainment 0.762 0.916
Elementary school or less06733 67330
Junior high school02971 43570
High school or more02971 572914
Network Index 0.302 0.316
Weak06040 80200
Medium01388 38630
Strong04060 602020
Participation in industry 0.120 0.798
Landscaping05050 60400
No landscaping01388 503813
Participation in industry 0.306 0.762
Construction01783 50500
No construction04258 58338
Educational attainment 0.661 0.242
Elementary school or less--- ---
Junior high school602020 10000
High school or more333333 64360
Network Index 0.338 0.662
Weak223344 75250
Medium632513 71290
Strong--- ---
Participation in industry 1.000 0.593
Landscaping333333 80200
No landscaping452727 70300
Participation in industry 0.011 ** 0.462
Construction06733 83170
No construction64927 67330
Educational attainment 0.238 0.667
Elementary school or less--- ---
Junior high school01000 10000
High school or more402040 75250
Network Index 1.000 0.833
Weak01000 10000
Medium333333 80200
Strong--- ---
Participation in industry 0.571 0.167
Landscaping335017 10000
No landscaping00100 01000
Participation in industry 1.000 0.333
Construction404020 10000
No construction05050 50500
a All values have been rounded to the closest integer. b Results refer to the percentage of day laborers that have experienced either zero, one, or two or more types of labor violation or abuse while performing a job obtained at la 21. c In the presence of frequencies equal to zero, we conducted Fisher’s exact test and report the obtained p-value. d Significance at the * 10% level; ** 5% level; *** 1% level.
Table 6. Resistance mechanisms a.
Table 6. Resistance mechanisms a.
ImmigrantCitizenAllp-Value b
Asserts day laborers share information about abusive employers c7686744786760.065 *
Knows where to report work violations1722273357220.094 *
Knowledge on where to report work violations is accurate d941001008025880.005 ***
Learned where to report work violations from the study71497090.584
a All values have been rounded to the closest integer and correspond to percentages. b Significance at the * 10% level; *** 1% level. c Four participants did not answer. d Among those who said to know where to report work violations.
Table 7. Tests of independence between the incidence of labor violation or work abuse and resistance mechanisms a–c.
Table 7. Tests of independence between the incidence of labor violation or work abuse and resistance mechanisms a–c.
Labor Violations ExperiencesWork Abuse Experiences
01≥2p-Value d01≥2p-Value d
Day laborers share information about abusive employers 0.002 *** 0.012 **
Yes192160 562816
No402733 81154
Knowledge on where to report work violations is accurate 0.086 * 0.153
Yes182162 492626
No602020 10000
a All values have been rounded to the closest integer. b Results refer to the percentage of day laborers that have experienced either zero, one, or two or more types of labor violation or abuse while performing a job obtained at la 21. c In the presence of frequencies equal to zero, we conducted Fisher’s exact test and report the obtained p-value. d Significance at the * 10% level; ** 5% level; *** 1% level.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Plankey-Videla, N.; Cisneros Franco, C.L. “Lots of Time They Don’t Pay”: Understanding Wage-Theft and Resistance in Bryan, Texas through Critical Community-Engaged Research. Soc. Sci. 2022, 11, 102.

AMA Style

Plankey-Videla N, Cisneros Franco CL. “Lots of Time They Don’t Pay”: Understanding Wage-Theft and Resistance in Bryan, Texas through Critical Community-Engaged Research. Social Sciences. 2022; 11(3):102.

Chicago/Turabian Style

Plankey-Videla, Nancy, and Cynthia Luz Cisneros Franco. 2022. "“Lots of Time They Don’t Pay”: Understanding Wage-Theft and Resistance in Bryan, Texas through Critical Community-Engaged Research" Social Sciences 11, no. 3: 102.

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop