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In 2025, public sector construction in the United States was a little more than $517 <br />billion, according to the Federal Reserve Bank of St. Louis. Why that matters for public <br />officials is the fact that most of this funding was required to be awarded to the lowest <br />bidder. Low bid requirements certainly play a role in helping contain costs on public <br />projects, but they have the unintentional side effect of incentivizing contractors to seek <br />any advantage that will increase their odds of submitting the winning low bid. And <br />unfortunately, more than a century of hard experience has taught us that some <br />contractors will choose to employ schemes like misclassification, payroll fraud, and wage <br />theft in order to increase their chances of winning low bids. <br />And research backs this up, showing how prevalent these issues have become within the <br />construction industry: <br />23% of Minnesota construction workers experience wage theft, reducing their wages and <br />benefits by 36% on average.' This reduces state tax revenues from the construction <br />industry by about $136 million each year ($65 million in income taxes, $13 million in <br />unemployment insurance contributions, and $58 million in workers' compensation <br />premiums). <br />Across all industries, 316,000 Minnesota workers are victims of wage theft each year, <br />losing about $6.2 billion. This also deprives government entities of about $1.3 billion in tax <br />revenue annually, putting more strain on crucial public safety net programs like Social <br />Security, Medicare/Medicaid, unemployment insurance, and workers' compensation. [I <br />grabbed these figures from Aaron's presentation — can we add citations for these figures?] <br />1 Goodell, N., & Manzo IV, F. (2021). The Costs of Wage Theft and Payroll Fraud in the Construction Industries <br />of Wisconsin, Minnesota, and Illinois. Midwest Economic Policy Institute. <br />httns: //dwd.wi.gov/misclass-taskforce /ndf/meetings /210114 /costs-of-navroll-fraud.ndf <br />