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Endnotes
1 Jonathan Nuno, interview by author, July 10, 2019: 6-7.
2 Jonathan Nuno, interview by author, July 10, 2019: 13.
3 Ricardo Xavier, interview by author, July 10, 2019: 11–12.
4 Residential construction today is not just the building of individual homes but multi-million dollars housing developments.
5 Francoise Carre and Randall Wilson, The Social and Economic Cost of Employee Misclassification in Construction, December 17, 2004. https://scholarworks.umb.edu/cgi/viewcontent.cgi?article=1042&context=csp_pubs
6 Tom Juravich, Wage Theft at the North Square Apartments in Amherst, Massachusetts, Working Worker Series. Juravich Wage Theft at North Square 6 29 20f.pdf (umass.edu)
7 Extensive documentation of wage theft appears in a 2009 study by Annette Bernhardt and her colleagues, who surveyed 4,387 low-wage workers in three US cities: Chicago, Los Angeles, and New York. Of those surveyed, they found that more than two-thirds had experienced a pay-related violation in the previous week. Additionally, 26 percent were paid less than the minimum wage, and of those workers, 60 percent were paid more than a dollar less than the minimum wage; moreover, 76 percent of those working overtime had not been paid the legally required overtime rate. “Foreign-born Latino workers had the highest minimum wage violations of any ethnic-group” A more recent study conducted by the Economic Policy Institute focuses on just one component of wage theft: paying below minimum wage. In a survey of the 10 most populous states, it reports that workers are underpaid by more than $8 billion, and the institute extrapolates that this would amount to more than $15 billion dollars lost each year by workers in the country who are paid below the minimum wage.
8 Our interviews, ranging from 45 to 120 minutes long, were recorded and professionally transcribed. While some individuals requested that their name be used, the majority of quotations are anonymized for their protection. Although it is not possible in a report of this length to include statements from everyone we interviewed, our account is representative of what we learned (and from whom) during the entire process.
9 Frederick Abernathy, Kent Colton, Kermit Baker, and David Weil, Bigger Isn’t Necessarily Better: Lessons from the Harvard Home Builder Study (Boston: Lexington Books, 2011).
10 Russell Ormiston, Dale Belman, Julie Brockman, and Matt Hinkel, “Residential Construction,” in Paul Osterman (ed.) Shifting to the High Road: Job Quality in Low-Wage Industries (Cambridge, MA: MIT Press, 2020).
11 Kenneth D. Walsh, Anil Sawhney, and Howard H. Bashford, “Cycle-Time Contributions of Hyper specialization and the Time-Gating Strategies in US Residential Construction,” http://www.leanconstruction.dk/media/18103/Cycle-Time%20Contributions%20of%20Hyper-Specialization%20and%20 Time-Gating%20Strategies%20in%20US-%20Residential%20Construction.pdf
12 David Weil, “The Contemporary Industrial Relations System in Construction: Analysis, Observations and Speculations,” Labor History 46, no. 4 (August 2006): 447–471; Theodore, “Rebuilding the House of Labor,” 59–97.
13 Ormiston et al., “Residential Construction.”
14 Interview with author, July 6, 2020: 12.
15 Brian Richardson, interview by author, July 10, 2019: 7.
16 Ruth Milkman, Immigrant Labor and the New Precariat (Cambridge: Polity Press, 2020).
17 Tom Flynn, Interview with author, June 23, 2020: 2.
18 Beacon Communities, “About Us,” n.d., https://www.beaconcommunitiesllc.com/about-us/mission/
19 Letter from Amherst Town Manager Paul Bockelman to begin community’s development, February 7, 2017. https://www.amherstma.gov/Document-Center/View/37884/Town-Manager-Letter-Approving-Tax-Incentive-02-07-17?bidId=
20 https://www.manta.com/d/mb4vc00/combat-drywall-inc
21 Jonathan Nuno, interview by author, July 10, 2019: 6.
22 Jonathan Nuno, interview by author, July 10, 2020: 48–49.
23 https://metrowalls.net/executive-team
25 Mergent Intellect is a major commercial database of employers in the U.S. that compiles data from the Dun and Bradstreet reports that all businesses complete on a regular basis. Their report https:// www-mergentintellect-com.silk.library.umass.edu/index.php/-search/printquickreport/360640424 indicates that Metro Walls has only 52 employees which calls into question how many of “250 employees” are in fact independent contractors?
26 Jonathan Nuno, interview by author, July 10, 2019: 6–7.
27 Jonathan Nuno, interview by author, July 10, 2019: 13.
28 Brian Richardson, interview by author, July 10, 2019.
29 Jonathan Nuno, interview by author, July 10, 2019: 10–11.
30 Carlos (pseudonym), interview by author, January 9, 2020: 10.
31 Jonathan Nuno, interview by author, July 10, 2019: 22–23.
32 Francisco (pseudonym), interview by author, January 9, 2020: 7.
33 Combat Workers, interview by author, January 9, 2020: 9.
34 Frank Gomez, interview by author, January 9, 2020: 15.
35 Frank Gomez, interview by author, January 9, 2020: 5.
36 Combat Workers, interview by author, January 9, 2020: 10.
37 Wage Complaint filed by Frank Gomez, NERCC, on behalf of nine workers, July 28, 2019.
38 Carlos (pseudonym), interview by author, January 8, 2020: 23.
39 Fernando (pseudonym), interview by author, January 9, 2020: 4.
40 Brian Richardson, interview by author, July 10, 2019: 13.
41 Interview by author, July 6. 2020: 43.
42 Interview by author, July 6. 2020: 44.
43 Interview by author, July 6. 2020: 44.
44 Interview by author, July 6. 2020: 30–31.
46 Jorge (pseudonym), interview by author, January 29, 2020: 11.
47 Jorge (pseudonym), interview by author, January 29, 2020: 8, 9.
48 Richard Pelletier, interview by author, July 14, 2020: 3.
49 Richard Pelletier, interview by author, July 14, 2020: 3, 9–10.
50 Richard Pelletier, interview by author, July 14, 2020: 5.
51 Richard Pelletier, interview by author, July 14, 2020: 5.
52 Tom Flynn, interview with author, June 23, 2020: 31–32.
53 Interview by author, July 6, 2020: 13.
54 Deborah M. Figart and Thom Barr. “Inside the World of Check-Cashing Outlets,” Dollars & Sense, January/February 2015 http://www.dollarsandsense.org/archives/2015/0115barr-figart.html; A Report by the Money Service Business Facilitated Workers’ Compensation Fraud Work Group file:///C:/Users/ juravich/AppData/Local/Microsoft/Windows/INetCache/Content.Outlook/V6CT9UTM/WC_MSBReport-Rec-1.pdf
55 Gladys Vega, interview by author, July 13, 2020: 22.
56 Interview by author, July 26, 2021.
57 Fernando (pseudonym), interview by author, January 9, 2020: 3.
58 Interview by author, July 10, 2019: 4.
59 Interview by author, July 10, 2019: 13.
60 Martin Sanchez, interview by author, December 16. 2019: 13.
61 Martin Sanchez, interview by author, December 16. 2019: 13–14.
62 Jose Anaya, interview by author, July 10, 2019.
63 Ernesto Belo, interview by author, December 12, 2019: 6.
64 Brian Richardson, interview by author, July 10, 2019: 14–15.
65 Jonathan Nuno, interview by author, July 10, 2019: 19–20.
66 Jonathan Nuno, interview by author, July 10, 2019: 32.
67 Jonathan Nuno, interview by author, July 10, 2019: 38–39.
68 Jonathan Nuno, interview by author, July 10, 2019: 34.
69 Jonathan Nuno, interview by author, July 10, 2019: 5.
70 Carlos (pseudonym), interview by author, January 9, 2020: 13.
71 Fernando (pseudonym), interview by author, January 9, 2020: 5.
72 Millagros Barreto, interview by author, October 15, 2020: 5.
73 Millagros Barreto, interview by author, October 15, 2020: 14.
74 Brian Richardson, interview by author, July 10, 2019: 15.
75 Contractor, interview by author, July 14, 2020: 46.
76 Contractor, interview by author, July 14, 2020: 12.
77 Frank Gomez, interview by author, December 16, 2019: 1.
78 Frank Gomez, interview by author, December 16, 2019: 1–2.
79 Neal McNamara, “Man Nearly Dies After Fall at Framingham Apartment Work Site,” The Patch, October 14, 2019. https://patch.com/massachusetts/framingham/man-nearly-died-after-fall-framingham-apartment-work-site
80 Frank Gomez, interview by author, December 16, 2019: 3–4.
81 Interview with author, July 6, 2020: 30.
82 Tom Flynn, interview by author, June 23, 2020: 13–14.
83 Martin Sanchez, interview by author, December 16, 2019: 19.
84 Tom Flynn, interview by author, June 23, 2020: 4–5.
85 The only other known approach to collect direct evidence of payroll fraud has been through surveying workers on construction job sites about the legality of their employment status. These studies offer important insight and have typically revealed substantial amounts of payroll fraud occurring in the industry. While these reports reveal substantial rates of illegality—ranging from 32% to 47% of the industry—these types of studies typically feature concerns about the representativeness of the sample, the sample size, or both. For more, see: Workers Defense Project. 2013. “Building a Better Texas: Construction Conditions in the Lone Star State”; Workers Defense Project. 2009. “Building Austin, Building Injustice”; Theodore, Nik, Bethany Boggess, Jackie Cornejo, and Emily Timm. 2017. “Build a Better South: Construction Working Conditions in the Southern U.S.”; Sinai, Clayton and Ernesto Galeas. Forthcoming. “The Underground Economy and Wage Theft in Washington, D.C.,’s Commercial Construction Sector.” Catholic Labor Network.
86 This report—colloquially referred to as the “Harvard Study” as it was sponsored by the Labor and Worklife Program of Harvard Law School and the Harvard School of Public Health—was groundbreaking, as it was one of the first published studies in the nation that offered direct and demonstrable proof of widespread wage and tax fraud in the construction industry. In addition to its impact in the Commonwealth, Carrè and Wilson’s report sparked a wave of similar studies across multiple states—Maine (2005), Illinois (2006), New York (2007), Minnesota (2007), Michigan (2009), Indiana (2010) and Virginia (2012)—in the years that followed. While we are not aware of a UI audit study that has been published since that time, a 2019 report out of Washington State used the same approach to publish the results of workers’ compensation audits, which similarly demonstrated widespread fraud in the state’s construction sector. For more, see: Carre, Francoise, and Randall Wilson. 2004. “The Social and Economic Costs of Employee Misclassification in Construction,” Cambridge, MA: Construction Policy Research Center and Labor and Worklife Program, Harvard Law School and Harvard School of Public Health. Accessed at: https://lwp.law.harvard.edu/publications/social-and-economic-costsemployee-misclassification-construction. For a review of each of these studies and their findings, see Ormiston, Russell, Dale Belman, and Mark Erlich. 2020. “An Empirical Methodology to Estimate the Incidence and Costs of Payroll Fraud in the Construction Industry.” Accessed at: http://iceres.org/wpcontent/uploads/2020/06/ICERES-Methodology-for-Wage-and-Tax-Fraud.pdf.
87 Neither random audits nor the sum of random and targeted audits is perfectly representative of the industry. A part of this is that audits are disproportionately completed of large construction employers, an outcome that will be explored later in the paper. That issue aside, employers who are the recipients of an audit are typically exempt from a random audit for up to three years; this means that the results of random audits are likely excluding a small number of firms who were subject to a targeted audit within the past three years. In other words, while random selection is often seen as a way of identifying the most representative sample of firms—and it is our preferred method in this study—there are reasons to believe that restricting the analysis to random audits would undercount the amount of worker misclassification. Conversely, the sum of random and targeted audits would be expected to overestimate the amount of worker misclassification (since it overweights the worst offenders), assuming that the underlying population of firms was representative of the industry as a whole. Finally, note that the 2004 Harvard Study referred to the random audit totals as the “Low Estimate” and the combination of random and targeted audits as the “Moderate Estimate.”
88 The fact that the sum of random and targeted audits produces higher estimates of worker misclassification than random audits alone is unsurprising. Construction employers scrutinized by the DUA via a targeted audit are substantially more likely to be committing illegal labor practices (50.0% of construction employers) than firms identified through a random audit (16.8%).
89 Representatives from the Massachusetts Department of Unemployment Assistance note that “misclassification” includes both workers falsely receiving a 1099-MISC instead of a W-2 as well as others who may be paid off-the-books. However, in the authors’ conversations with multiple individuals from the DUA, it seems that misclassified independent contractors are much easier to detect in the auditing process and are likely to make up a substantial portion of the total even if we were not provided with a breakdown of each category.
90 This number is calculated by dividing the number of misclassified workers (numerator) by the sum of legitimately classified wage-and-salary workers and the number of misclassified workers (denominator); this is the approach taken in most state studies presenting UI audit results and this study follows that lead. However, later calculations in this paper will also include the self-employed in the denominator in order to estimate the overall proportion of the industry’s workforce.
91 The results of the random audits reflect a ratio of 14.07 legitimate wage-and-salary construction workers for every one misclassified worker in the industry. The Massachusetts DUA’s Labor Market Information portal suggests that there were 163,106 private-sector wage-and-salary workers in the state’s construction sector in 2019; this ratio leads to an estimated 11,013 workers affected by payroll fraud. For more on LMI data, see: https://lmi.dua.eol.mass.gov/lmi/EmploymentAndWages.
92 Between 2017 and 2019, the DUA conducted thousands of audits across all industries in Massachusetts. Across all industries—including construction—the results of random audits reflect that 15.3% of employers were discovered to be misclassifying workers. Firms who were determined to be misclassifying did so extensively, as there were 13.1 affected employees, on average, among firms who were classifying workers incorrectly. Altogether, the results suggest that 6.2% of all employees in Massachusetts were affected by misclassification; extrapolating that against the number of private-sector employees in the Commonwealth in 2019, this suggests that misclassification affected an estimated 211,249 workers that year statewide. This number is calculated by multiplying the rate of misclassification (1 misclassified worker for each 15.2 legitimate wage-and-salary employee) by the number of private-sector employees in the state as presented by the Massachusetts DUA’s Labor Market Information portal (3,201,289 in 2019).
93 Predictably, the inclusion of targeted audits increases the rate of discovered misclassification when reviewing the DUA results across all industries for 2017 through 2019. Summing random and targeted audits, the results suggest that 16.5% of all employers were misclassifying, affecting 9.4% of private-sector employees in the state (or 330,060 workers). We suggest caution, however, when using the sum of random and targeted audits to represent the all-industry total. As discussed in a previous endnote, the inclusion of firms subject to targeted audits—where there are reasons to suspect that an employer is misclassifying before the audit—undermines the representative of the sample and results in presumed overestimation of statewide misclassification. This appears especially true in the all-industry totals. While 15.3% of businesses subject to a random audit were found to be misclassifying (averaging 13.1 workers per offending firm), those subject to a targeted audit were far more likely to be engaged in widespread illegality: 57.5% of firms were misclassifying, averaging 73.1 affected workers per offending firm. The substantial gap between these two totals are not unexpected, however this would cause the sum of random and targeted audits to overstate the extent of misclassification across all of Massachusetts.
94 When combining random and targeted audits, the proportion of construction employers committing worker misclassification was higher in 2001-03 (24%) than in 2017-19 (17.9%). However, given that the authors do not know the proportion of targeted audits that made up this estimate in 2001-03, they cannot be sure whether that is reflective of a trend or simply the outcome of the DUA conducting relatively more targeted outcomes in 2001-03. As such, a comparison of random audit totals represents a more apples-to-apples comparison between years.
95 Using a mix of random and targeted audits, the Harvard Study suggested that offending employers were misclassifying 48% of the workforce.
96 Sub-sectors are identified using four-digit NAICS codes, or the North American Industrial Classification System. The authors have access to industry codes 2361XX (Residential Building Construction), 2381XX (Foundation, Structure, and Building Exterior Contractors), 2382XX (Building Equipment Contractors), and 2383XX (Building Finishing Contractors). While there are other four-digit NAICS codes within the construction industry, results for these sub-sectors were not made available to the authors due to DUA disclosure rules. A full composition of each subsector is as follows: Residential Building Construction (Single-Family Housing Construction; Multifamily Housing Construction; New Housing For-Sale; Residential Remodelers); Foundation, Structure, and Building Exterior Contractors (Poured Concrete Foundation and Structure; Structural Steel and Precast Concrete; Framing; Masonry; Glass and Glazing; Roofing; Siding; Other); Building Equipment Contractors (Electrical; Plumbing, Heating and Air Conditioning; Other); and Building Finishing Contractors (Drywall and Insulation; Painting and Wall Covering; Flooring; Tile and Terrazzo; Finish Carpentry; Other).
97 The only study known to the authors that had audit results for subsectors within the construction industry was a 2007 report by the Office of the Legislative Auditor in the State of Minnesota. The report noted that 15% of construction employers were found to be engaging in worker misclassification, with the highest rates in roofing (38%) and drywall installation (31%). Unfortunately, data at this granular level of the industry were not available to the authors for Massachusetts due to DUA disclosure rules prohibiting the public dissemination of results where it would be possible to identify individual firms. For more, see: Office of the Legislative Auditor. 2007. “Misclassification of Employees as Independent Contractors,” State of Minnesota. https://www.leg.state.mn.us/docs/2007/other/070704.pdf.
98 The fact that the DUA audit data does not represent a complete estimate of all payroll fraud in the industry should in no way be an indictment of the agency’s work. First, it may be that the goals of the DUA are to maximize their limited resources to identify as much worker misclassification as possible, rather than to conduct a representative “census” of the industry. Second, our conversations with industry stakeholders and regulators inside and outside of Massachusetts highlight that identifying cash-only arrangements and accessing small employers is a national problem and is not reflective of deficiencies in the DUA’s work.
99 DUA auditors may be successful in identifying a labor broker’s workers if visiting a job site, but Chapter 1 of this reports makes clear that a direct audit of labor brokers would reveal substantially more numbers of workers who are operating off-the-books. Further, Chapter 1 highlights the extent that contractors and labor brokers often go in order to conceal these workers from government auditors and regulators.
100 Conversations with multiple DUA officials have confirmed the difficulty of proving cash-only employment compared to misclassified workers hired using a 1099-MISC form; this outcome should be of little surprise given the lengths some contractors will go to conceal their actions (e.g., the purported practices of Metro Walls offered in Chapter 1).
101 Small employers comprise a substantial part of the Massachusetts construction industry, as more than two-thirds of firms had five or fewer employees as of the first quarter of 2019. For more, see: https://data.bls.gov/cew/apps/-data_views/data_views.htm#tab=Tables.
102 Source: Author’s conversation with DUA auditor, March 12, 2021.
103 The average private-sector, UI-paying construction employer in Massachusetts had 7.6 employees in 2019; meanwhile, the average construction business for which a random audit was completed in 2017-19 had 25.0 employees (pre-audit total). The former number is calculated from the Massachusetts DUA website, which lists 21,389 private-sector construction establishments in 2019 employing an average 163,106 workers on a monthly basis (7.6 workers per firm). For more, see: https://lmi.dua. eol.mass.gov/LMI/EmploymentAndWages.
104 As discussed in the Appendix, this method relies on the critical assumption that the industry codes inputted on worker surveys and submitted by employers are identified and coded correctly. In terms of worker surveys, there is research identifying that it is not uncommon for workers’ occupations to be miscoded; we presume similar findings may occur for workers’ industries. Similarly, our conversations with DOR representatives in the course of this research revealed that employers’ industry classifications may evolve over time without the companies updating their industry code from their initial time of registration with the state; further, it is possible that construction employers may be strategic in how they classify their industry code on state and federal forms. While these issues are likely to affect the employment estimates in some way, there are no known credible assessments of the net effect in construction. As a result, we assume the net effect is zero and that the data utilized in this study are accurate, a presumption bolstered by the fact that the data sets are extracted from two government agencies—the Census Bureau and the Bureau of Labor Statistics—that represent the gold standard for large-scale data collection in the United States.
105 One methodological complication is that the difference between worker surveys (which offers “total employment”) and payroll records (which presents “legal wage-and-salary employment”) includes both legal self-employment and illegal self-employment (i.e., misclassified independent contractors and off-the-books workers). Unfortunately, there is no clear way of distinguishing between legal and illegal self-employment in this data. For a full discussion of this issue and all of the approaches used to navigate this issue, see Ormiston, Russell, Dale Belman, and Mark Erlich. 2020. “An Empirical Methodology to Estimate the Incidence and Costs of Payroll Fraud in the Construction Industry.”
106 For more, see Ormiston, Russell, Dale Belman, and Mark Erlich. 2020. “An Empirical Methodology to Estimate the Incidence and Costs of Payroll Fraud in the Construction Industry.”
107 As a starting point of the analysis, the results suggest that worker surveys reflected 57,373 more construction jobs than were presented in contractors’ payrolls submitted to the DUA in 2019. For more on how this was used to produce the specific estimates of how many of these were misclassified workers, see Appendix A.
108 The estimated proportion in this section—and throughout the rest of the chapter—include all construction workers in the denominator, including the self-employed. This differs slightly from the discussion of DUA audits earlier in the chapter, as the authors wished to remain consistent with the approach featured in the 2004 Harvard Study and other state-specific studies using UI audits.
109 We have heard numerous anecdotal reports—inside and outside of Massachusetts—about construction workers who are W-2 employees but who receive a considerable amount of compensation in cash that is unreported on income and tax documentation. While it could be argued that these workers should be counted as “misclassified” or otherwise employed via illegal means, we do not have any empirical data to inform us how often this occurs. As a result, this method treats anyone who receives a W-2 as a legitimate employee (i.e., not misclassified). This decision lends further support to the hypothesis that our estimates of worker misclassification may undercount the extent of the problem.
110 For more, see Cooke, Oliver, Deborah Figart, and John Froonjian. 2016. “The Underground Construction Economy in New Jersey”; Liu, Yvonne Yen, Daniel Flaming, and Patrick Burns. 2014. “Sinking Underground: The Growing Informal Economy in California Construction”; Canak, William, and Randall Adams. 2010. “Misclassified Construction Employees in Tennessee.”
111 Of primary importance, research by Katherine Abraham (University of Maryland) has shown that a similar household survey to the ACS—the Current Population Survey—understates the number of jobs in the economy. In essence, some survey respondents simply failed to acknowledge that a household member works for money. For example, in a 2019 paper by Abraham and Ashley Amaya, it was shown that the CPS missed an estimated 21.9% of informal jobs (including 13.0% of informal work lasting more than four hours per week). Findings that household surveys such as the ACS undercounts the number of jobs has a direct effect on the current study; this conclusion would mean that the number of self-reported jobs unaccounted for by payroll records would be substantially larger than the projections in this paper. This would subsequently mean that the indirect method underestimates the gap between worker surveys and employer payroll records, thereby undercounting payroll fraud in the construction industry. This exact conclusion was offered directly to the authors by Abraham, as she reviewed the 2020 ICERES study when it was presented at the national 2021 Labor and Employment Relations Association (LERA) conference. However, while her research would support the decision to increase the estimates offered in this study, the authors choose not to make such an adjustment (a) in order to remain true to conservative assumptions in the face of statistical uncertainty, (b) the uncertainty of differences between the CPS and ACS, and (c) because Abraham’s findings are not construction-specific, meaning that the economy-wide average may not be perfectly applicable to construction. For more, see Abraham, Katherine, and Ashley Amaya. 2019. “Probing for Informal Work Activity,” Journal of Official Statistics, 35(3), 487-508; Abraham, Katherine, John C. Haltiwanger, Claire Hou, Kristin Sandusky, and James R. Speltzer. 2020. “Reconciling Survey and Administrative Measures of Self-Employment.”
112 There are two studies using worker surveys that are particular compelling for this study. First, a 2017 study surveyed 1,435 construction workers—by far the largest sample in this type of study—and concluded that 32% of workers in six Southern cities were either misclassified or working off the books. Meanwhile, a forthcoming study by the Catholic Labor Network featured survey results from 79 construction workers on commercial construction sites in Washington, D.C.; the results showed that 47% were either misclassified or working off-the-books. For more, see Theodore, Nik, Bethany Boggess, Jackie Cornejo, and Emily Timm. 2017. “Build a Better South: Construction Working Conditions in the Southern U.S.”; Sinai, Clayton and Ernesto Galeas. Forthcoming. “The Underground Economy and Wage Theft in Washington, D.C.,’s Commercial Construction Sector.” Catholic Labor Network.
113 The Massachusetts Department of Revenue has access to all 1099-MISC filings through its interface with the Internal Revenue Service, however the IRS maintains control over the federal database and did not grant the author’s access. This was unsurprising, as access to IRS records typically involves a long and lengthy application-and-review process. As such, the 1099-MISC data in this study capture only filings made directly to the DOR.
114 This outcome is not the fault of the DOR: research from the IRS shows income underreporting is far more common among non-W-2 employees on a national basis. For more, see: Internal Revenue Service. 2016. “Federal Tax Compliance Research: Tax Gap Estimates for Tax Years 2008-2010.” IRS Publication 1415; https://www.irs.gov/pub/irs-soi/p1415.pdf.
115 There are multiple reasons why Massachusetts residents may not file income taxes. First, residents making less than $8,000 per year are not required to file; while this may account for some non-filers, it certainly does not represent the majority of cases. Between 2016 and 2019, DOR records reveal that there were 4,244 forms issued to Massachusetts residents for $8,000 or more using Social Security Numbers that never appeared on a state tax return. In other words, a minimum of 17% of 1099-MISC forms were issued to Social Security Numbers that were not featured in state income tax filings with the DOR despite the dollar value automatically triggering the need for taxes to be filed. A second potential problem that may explain non-filing behavior is that some businesses list their identification type as a Social Security Number, but their tax filings appear in the corporate tax system of the DOR; in other words, they are counted as “non-filers” when SSNs are compared to personal income tax forms, but the entity may have legitimately filed its taxes. This would inflate the total number of non-filers among personal income tax records. Conversations with DOR representatives reflect that this does affect a decent number of businesses, however, not enough to dramatically alter the estimated nonfiling percentage or the conclusion that there are thousands of Massachusetts construction workers who were issued 1099-MISC forms but who simply failed to file income tax returns with the Department of Revenue.
116 The authors do not have an exact figure for how many non-filers were done for legitimate reasons; as such, this study cannot accurately give a precise projection of the proportion of non-filers are due to tax fraud.
117 In many parts of the country, contractors have been discovered to fraudulently “rent” a workers compensation policy to be able to win a contract. In this scenario, a shell company purchases a cheap workers compensation insurance policy under the false pretenses that it is a small construction firm in a relatively safe sector (to lower the cost of the premiums). The owner of the shell company then “rents” their policy out to numerous contractors—both “legitimate” and off-the-book types—in order to allow them to secure work on projects (which often require proof of insurance). This means that the original policy may covers scores of workers across many different contractors, with the payments between the shell company owner and the contractors renting the coverage routed through check-cashing operations to avoid regulatory detection. In Florida, a task force of government agencies, insurance companies, construction unions, and employers estimated that the scheme could be defrauding the state close to $1 billion annually. For more, see Ormiston, Russell, Dale Belman, Julie Brockman, and Matt Hinkel. 2020. “Rebuilding Residential Construction,” In P. Osterman (Ed.), Creating Good Jobs: An Industry-Based Strategy, MIT Press; “A Report by the Money Service Business Facilitated–Workers’ Compensation Fraud Work Group,” https://www.myfloridacfo.com/siteDocs/-MoneyServiceBusiness/WC_MSBReport-Rec.pdf.
118 Results generated from the authors’ analysis of the 2017 through 2020 annual reports of the Workers’ Compensation Advisory Council. For more, see: https://www.mass.gov/lists/-workers-compensation-advisory-council-annual-reports.
119 This number comes from the authors’ assessment of page 24 of “Using Massachusetts Workers’ Compensation Data to Identify Priorities for Preventing Occupational Injuries and Illnesses among Private Sector Workers,” a report published in 2019 by the Massachusetts Department of Industrial Accidents, the Massachusetts Department of Public Health and the Massachusetts Department of Labor Standards. For more, see: https://www.mass.gov/doc/dph-dia-and-dls-release-new-study-on-utilization-of-workers-compensation-data/download.
120 These results are also consistent with the conclusion of the academic research that injury rates are substantially higher on job sites overseen by contractors who cut corners and are less committed to safety. The link between a workplace commitment to safety and lower injury rates in the construction industry is typically supported by studies showing that injury rates are far lower among union contractors than non-union contractors, despite the former working on inherently more dangerous projects. As an example, a 2021 study by the Ontario Construction Secretariat showed that unionization in construction was linked with a 31% decline in lost time allowed injury claims in Canada. For more, see Robson, Lynda, Victoria Landsman, Desiree Latour-Villamil, Hyunmi Lee, and Cameron Mustard. 2021. “Updating a Study on the Union Effect on Safety in the ICI Construction Sector,” Institute for Work and Health; Zullo, Rolland. 2011. “Right-to-Work Laws and Fatalities in Construction.” Ann Arbor, MI: Institute for Research on Labor, Employment, and the Economy, University of Michigan; Miller, Harry, Tara Hill, Kris Mason, and John Gaal. 2013. “An Analysis of Safety Culture & Safety Training: Comparing the Impact of Union, Non-Union, and Right-to-Work Construction Venues.” Online Journal of Workforce Education and Development, Vol. VI, No. 2; Weil, David. 1992. “Building safety: The Role of Construction Unions in the Enforcement of OSHA.” Journal of Labor Research, Vol. 13, Issue 1, pp. 121-132; Gillen, Marion, David Baltz, Margy Gassel, Luz Kirsch, and Diane Vaccaro. 2002. “Perceived Safety Climate, Job Demands, and Coworker Support among Union and Nonunion Injured Construction Workers.” Journal of Safety Research, Vol. 33, No. 1, pp. 33-51.
121 The starting point of the indirect method of estimating the prevalence of payroll fraud is the American Community Survey, a survey designed to ask workers about their employment situation. Unfortunately, the ACS—and all other Census household surveys—do not provide industry codes about workers’ employer beyond “construction.” Thus, while other data government sources feature subsector codes (e.g., drywall contractor) based on the North American Industrial Classification System (NAICS), Census worker surveys do not. This incongruity means that the indirect method cannot be estimated for subsectors of the construction industry.
122 The difference will also include workers who are employed by staffing agencies. While the 2019 OES indicates that there were 840 workers in construction occupations working legally for staffing agencies, the data does not offer enough detail to assign these 840 workers to specific trades.
123 The authors’ analysis of the ACS using data from IPUMS reflects overall construction industry employment levels in Massachusetts that are approximately 2% higher than what is represented on the Census’s ACS website (when comparing industry employment among state residents). It is unclear what is behind this minor inconsistency. However, this concern is at the statistical margin and is offset by the fact that second job holding in the national construction industry is about 2%; this would suggest that these authors’ estimates may better approximate total industry employment given that the Census’s website only reflects the industry of a person’s primary job. American Community Survey data extracted from ipums.org: Steven Ruggles, Sarah Flood, Ronald Goeken, Josiah Grover, Erin Meyer, Jose Pacas and Matthew Sobek. IPUMS USA: Version 10.0 [dataset]. Minneapolis, MN: IPUMS, 2020. Occupational Employment Statistics data extracted from: https://www.bls.gov/oes/2019/may/oes_research_estimates.htm.
124 As an example, the authors proposed the conclusion that painters, carpenters and laborers are most affected by payroll fraud to a large group of union organizers who are on construction sites regularly. There was near-unanimous agreement that these three trades were the ones in which fraud is most prevalent. (Source: Author’s notes from a conversation with NASRCC organizers, Boston, Mass., February 24, 2020).
125 There are numerous reasons to be concerned about occupational comparisons between the American Community Survey (worker survey) and the Occupational Employment Statistics (employer survey). Worker surveys are notorious for occupational coding errors—meaning respondents are assigned an occupation in the survey that does not represent their true job duties—that make the outcomes less reliable. Further, there are temporal differences between surveys that may be generating some of the differences. The 2019 OES survey is actually the result of six panels of surveys ranging from 2016 through 2019 (November 2016, November and May 2017 and 2018, and May 2019). To adjust for this, we weight the annual ACS estimates to reflect the timing of the OES. Nevertheless, given that the ACS is an annual survey while the OES is taken at specific points in time, it is expected that there will be some error related to discrepancies in the timing of each survey. For more, see (among others): Mathiowetz, Nancy A. 1992. “Errors in Reports of Occupation,” The Public Opinion Quarterly, 56(3), pp. 352-355; Kambourov, Gueorgui, and Iourii Manovskii. 2010. “A Cautionary Note on Using (March) CPS and PSID Data to Study Worker Mobility,”; and https://www.bls.gov/oes/current/oes_tec.htm. https://www.sas.upenn.edu/~manovski/-papers/CautionaryNote.pdf.
126 As another concern, the last row of Table A denotes that, in both the worker and employers’ surveys, first-line construction supervisors are categorized without regard to trade. If some these individuals also work as tradespeople on the jobsite, their inclusion in the employment numbers would increase the size of the discrepancy among carpenters in Table A but may decrease the estimated proportion.
127 While this may be the first known study of payroll fraud in the construction industry that has access to tax data, it is acknowledged that this analysis excludes the labor practices of corporations. While the authors have data on the number of returns and gross receipts among corporations by industry code, there is no method of identifying their usage of contract labor in tax returns. The authors were provided similar data on partnerships, however there were relatively few of them in many industry codes, to the point where individual firms could be identified. Further, questions about contract labor differ markedly on tax filings for partnerships compared to sole proprietorships; as such, the authors could not be sure that it was measuring the same outcomes as the questions on Schedule C for sole proprietorships. Given these issues, partnerships were excluded from the analysis. While these empirical issues limited this study’s direct analysis to sole proprietorships, Appendix C offers some perspective on the broader trends in this data even if they are not directly connected to issues of payroll fraud.
128 The 2019 Massachusetts Schedule C form can be accessed here: https://www.mass.gov/doc/2019- schedule-c-massachusetts-profit-or-loss-from-business/download.
129 The initial focus on specialty trades contractors was to minimize concerns about firms playing different roles in the subcontracting chain (e.g., a comparison between a home builder vs. a drywall contractor). All specialty trades contractors have a similar industry code (their NAICS code starts with “238”). Table 5 only includes the results from industry codes featuring at least 100 companies. Finally, this analysis excludes thousands of firms who are identified in the category of “Other Specialty Trades Contractors,” as the type of work included in this category is too varied to draw any conclusions about the results.
130 It is expected that the values in Table 5 understate the amount of contract labor employed by sole proprietorships in Massachusetts, as it is expected that contractors who operate entirely on a cashonly basis (i.e., hiring contract labor) will not file tax returns. It is further expected that non filers are likely to be disproportionately featured in framing, drywall, and other contractor types featured in the “high contract labor usage” group; this effect would thus further exacerbate the disparity between the two groups of contractors in Table 5.
131 The proportion of wages paid by residential contractors within an employer category is the best available signal of the residential/nonresidential split within a subsector. The data comes from the 2019 Quarterly Census of Employment and Wages, available from the QCEW data viewer: https:// data.bls.gov/cew/apps/data_views/data_views.htm. While the proportions are available for Massachusetts construction employers (instead of at the national level), there were some contractor categories that featured small values for the number of firms in the UI system in the state totals (e.g., nonresidential framing and nonresidential siding contractors). As a result, the authors chose to use national data so as better approximate the type of work performed. Nevertheless, the use of state vs. national values do not qualitatively change the conclusions offered.
132 Regression analyses connecting the contract-labor-to-wage ratio (y-variable) and the proportion of wages paid by residential contractors (x-variable) indicate that the correlation is statistically significant with greater than 99% confidence. Excluding siding contractors, the results suggest that a one percentage point increase in the proportion of wages paid by residential firms in a contractor category is linked to an expected 0.033 increase in the contract-labor-to-wage ratio (p=0.0031). Including siding contractors into the analysis raises the effect to 0.058 and the regression coefficient continues to be statistically significant with greater than 99% confidence (p=0.0013).
133 For more, see: Belman, Dale, and Aaron Sojourner. 2019. “Economic Analysis of Incentives to Fraudulently Misclassify Employees in District of Columbia Construction,” Office of the Attorney General for the District of Columbia.
134 These numbers are generated as follows. First, the DUA reported to the authors that the average construction employer paid 7.37% on the first $15,000 of an employee’s wages into the state UI fund in 2019. Second, the Workers’ Compensation Rating and Inspection Bureau of Massachusetts reported publicly that construction employers paid an average of $4.742 per $100 payroll for workers compensation insurance coverage in 2019. Third, the authors’ analysis of the National Compensation Survey reflects that 2.4727% of employee wages in construction on a national basis are derived from overtime and premium pay (i.e., the “half” in “time-and-a-half”); the calculations offered assume that workers affected by payroll fraud have the same weekly work hours as regular employees. For more, see. https://www.wcribma.org/mass/-IndustryInformation/RateFiling/2020/-WCRIBMA_Filing/Filing_2020.pdf and https://www.bls.gov/web/ecec/ececqrtn.pdf.
135 These estimates were generated by first calculating the economic costs on a per-worker basis then multiplying those values the total number of workers affected. This step may slightly overstate the costs for programs—such as the state UI system and FICA taxes—where social contributions phase out after a certain income level. In Massachusetts, this is likely to most affect UI contributions, which are only required on the first $15,000 of an employee’s taxable wages. Since the average per-worker earnings of affected workers do not exceed $15,000, it is assumed that the employer must pay UI contributions on every dollar earned by the employee. But it is likely that there is a wide distribution in the earnings of affected workers, meaning that UI contributions would not be collected for some employees once they reach that income threshold. However, since we have no information on the proportion of workers who exceed this threshold, we assume that all workers earn the industry average.
136 This approach generally follows three steps: (1) making assumptions about what affected workers would have earned in the legitimate construction economy, (2) calculating the per-worker costs of wage and tax fraud and (3) multiplying the per-worker costs by the number of workers presumed to be affected. The use of assumptions—even ones carefully and conservatively selected—is recognized to introduce a nontrivial margin of error into the projections, an unsurprising outcome considering that we are tasked with assessing the costs of something that occurs largely in the shadows of the economy. A full overview of this approach and its application to Massachusetts is presented in Appendix D. For more, see: Belman, Dale, and Aaron Sojourner. 2019. “Economic Analysis of Incentives to Fraudulently Misclassify Employees in District of Columbia Construction,” Office of the Attorney General for the District of Columbia; Ormiston, Russell, Dale Belman, and Mark Erlich. 2020. “An Empirical Methodology to Estimate the Incidence and Costs of Payroll Fraud in the Construction Industry.”
137 This number ($35,200) represents the 10th percentile of annual earnings in construction occupations among those legally employed by Massachusetts employers in 2019. There are numerous reasons supporting the use of the 10th percentile as a starting point in this analysis. First and foremost, it is recognized that construction workers most often affected by payroll fraud are in lower-skill, lower-paying jobs. Second, an analysis of the American Community Survey reveals that this number nearly matches the 25th percentile of earnings ($35,000) of all construction-industry workers in Massachusetts in 2019, a number that includes misclassified independent contractors and off-the-books workers. Further, among construction workers in the four most affected trades identified in the main report—painters, carpenters, laborers, and roofers—this value ($35,200) is between the 35th and 40th percentile of earnings in Massachusetts according to the ACS. Additionally, this value approximates the median earnings ($35,000) of non-incorporated self-employed construction workers in the Commonwealth in 2019, who are often considered the group in national survey data that is most likely to feature misclassified independent contractors and off-the-books workers. Finally, our conversations with industry stakeholders suggests that hourly wage rates of $17 to $22 per hour are rather common for off-the-books workers (equating to $34,000 to $44,000 annually for an individual working 2,000 hours in a year).
138 The use of the 25th percentile ($44,960) of earnings among those legally employed in construction occupations in Massachusetts was also considered because (a) the 25th percentile has been the choice in other similar studies and (b) it recognizes that there are some high-skill, high-wage workers who also are employed off-the-books. However, we had concerns about only presenting the 25th percentile in Massachusetts because the state features the third-highest level of earnings in construction occupations at the 25th percentile in the United States (trailing only Alaska and Hawaii). Data for occupational earnings among Massachusetts employers for 2019 can be found at: https://www.bls.gov/oes/tables.htm.
139 In a 2016 report, the IRS noted that only 1% of W-2 earnings were misreported on tax forms; in contract, the agency assessed that 64% of nonfarm proprietor income—which is subject to “little to no information reporting”—is underreported on tax forms. For more, see: Internal Revenue Service. 2016. “Federal Tax Compliance Research: Tax Gap Estimates for Tax Years 2008-2010.” IRS Publication 1415.
140 The 1% starting point for the value of wage theft was loosely based on back-of-the-envelope calculations in analyzing worker surveys among construction workers in Texas. For more, see: Workers Defense Project. 2013. “Building a Better Texas: Construction Conditions in the Lone Star State”; Workers Defense Project. 2009. “Building Austin, Building Injustice.”
141 Among other issues, data limitations inhibit the empirical assessment of two critical indirect costs of payroll fraud: lost profitability by law-abiding employers and the potential decline in wages among workers in the legitimate sectors of the economy. To the authors’ knowledge, there has not been an academic study of either of these issues. Basic economic theory would strongly suggest that payroll fraud would be destructive to most entities in the legitimate construction sector, with the damage especially concentrated in trades most affected by fraud (e.g., carpenters, painters, laborers). This would include, but not be limited to, fewer work opportunities and lower wages amongst those in “legal” work situations. That said, the authors cannot rule out that a small number of entities in the law-abiding construction sector may experience some benefits (i.e., those in relatively protected trades benefitting from an increase in construction projects made possible by cheaper overall costs in other sectors).
142 As identified in Appendix B, the average 1099-MISC filed with the DOR between 2016 and 2019 was for $20,146 in non-employee compensation. Given that this total also includes an unknown number of 1099-MISCs issued to businesses—and not individual workers—we suspect that this total is a bit larger than the average if the files were restricted to individual workers.
143 Of most concern, a business that accidentally uses a 1099-MISC and a personal Social Security Number to secure a contract and then submits corporate (not personal) income taxes to the DOR would be considered a “non-filer” in this data. DOR representatives highlight that is not a rare occurrence, although we do not have data to confirm an exact proportion. Given that income generated by a business on a 1099-MISC is expected to be substantially larger than the earnings provided to an individual worker, we would anticipate that this problem affects the missing money much more than the number of 1099-MISCs issued.
144 The authors also do not find it coincidental that the trades with the lowest dollar values of 1099- MISC forms (e.g., electrical contractors) often feature some of the lowest rates of payroll fraud as estimated earlier in this study. The data on 1099-MISC are too incomplete to make any conclusive statements, however. For more information 1099-MISC by trades, see Appendix B.
145 As evidence, these findings are consistent with a 2016 IRS study that noted that a wide disparity in income underreporting rates between jobs featuring a W-2 and positions that do not. For more, see: Internal Revenue Service. 2016. “Federal Tax Compliance Research: Tax Gap Estimates for Tax Years 2008-2010.” IRS Publication 1415; https://www.irs.gov/pub/irs-soi/p1415.pdf.
146 The data features a description of the accident and the employer for all cases; however industry data is not included and occupational information is provided for most, but not all, cases. Further, occupation data is both ambiguous— “laborers” seem to be represented in many industries—and at times inconsistent (i.e., a listed occupation does not match the incident description). Further, additional ambiguity exists because some of the listed employers are listed as individuals; this could either be a boss’ name or an off-the-books employer.
147 At least a part of this money comes the DIA’s issuance of fines related to Stop Work Orders (SWO) when the agency gets reports that a company does not have a valid workers’ compensation insurance policy. In fiscal year 2019, the DIA issued 2,028 SWO; such an order has particular consequences in construction, as businesses issued a Stop Work Order are placed on the DIA Debarment List and are thus prevented from bidding on state or municipal funded contracts for three years. For more, see: https://www.mass.gov/doc/fy-2019-annual-report/download, https://www.mass.gov/service-details/debarment-list-businesses-ineligible-to-bid-on-state-or-muncipally-funded-contracts.
148 Joanne F. Goldstein, interview by author, May 3, 2021.
149 “AG Healy Assesses Nearly $3 Million in Penalties and Back Wages Against Construction Companies in 2019” https://www.mass.gov/news/ag-healey-assesses-nearly-3-million-in-penalties-and-backwages-against-construction-companies-in-2019.
150 Bureau of Economic Analysis; data series SQGDP2; https://www.bea.gov/data/gdp/gdp-state)
151 Massachusetts DUA
152 Tom Flynn, interview by author, June 23, 2020: 22.
153 Interview by author July 6, 2020: 33
154 Citation for Violation of Massachusetts Wage and Hours Laws, Alvarez Drywall, 4/16/2020.
155 Citation for Violation of Massachusetts Wage and Hours Laws, Alvarez Drywall, 4/16/2020.
156 Citation for Violation of Massachusetts Wage and Hours Laws, Combat Drywall, 4/16/2020.
157 https://www.mass.gov/doc/cue-annual-report-2018/download
159 https://www.mass.gov/orgs/the-council-on-the-underground-economy-cue
160 Letter from Massachusetts Office of the Attorney General Secretary Rosalin Acosta, Executive office of Labor and Workforce Development, January 20, 2021. To date, there has not been a response according to Correspondence from Lauren Moran to Joanne Goldstein, chief of FLD, April 23, 2021.
161 Joann Goldstein, interview by author July 10, 2019, 33.
162 Contractor interview by author, July 14, 2020: 4.
163 Contractor, interview by author, July 14, 2020: 17.
164 David Weil, Creating a Strategic Enforcement Approach to Address Wage Theft; One Academic’s journey in Organization Change,” Journal of Industrial Relations, April 20, 2018.
165 https://malegislature.gov/Bills/192/H1959.
166 https://malegislature.gov/Bills/192/S1179.
167 J. T. Scott, interview by author, January 6, 2021.
168 Janice Fine, “Enforcing Labor Standards in Partnership with Civil Society: Can Co-enforcement Succeed Where the State Alone Has Failed?” Politics & Society, 2017, Vol 45(3), 359-388.
169 Cathy Shoen, interview by author, December 14, 2020: 11.
170 The Epidemic of Wage Theft in Massachusetts, Labor Center Working Paper, https://www.umass.edu/lrrc/research/working-papers-series/wage-theft.
171 Interview the author, July 2, 2020: 17.
172 Beth Healy and Megan Woolhouse, “In Building Boom, Immigrant Workers Face Exploitation,” Boston Globe, September 17, 2016 https://www.bostonglobe.com/business/2016/09/17/construction-boom-immigrant-workers-face-perils-exploitation/WmlvDkLB4bRE9jp71wca2M/story.html.
173 Karl Flecker and Teresa Healy. International Labor Migration: Re-regulating the Private Power of Labor Brokers. Washington, DC: Solidarity Center, 2015.