The art of deciding with data: evidence from how employers translate credit reports into hiring decisions

The art of deciding with data: evidence from how employers translate credit reports into hiring... Abstract About half of US employers consider personal credit history when hiring, a practice that connects individuals’ prospects for employment to their financial pasts. Yet little is known about how employers translate credit reports, complicated financial documents, into hiring decisions. Using interviews with 57 hiring professionals, this paper offers the first in-depth look at how employers move from document to decision. Faced with the context-free numbers of a credit report, and without predictively valid credit scores to fall back on, hiring professionals struggle to make sense of financial data without knowing the details of job candidates’ lives. They therefore reach beyond credit reports, both by inferring events that led to delinquent debt and by testing to see if candidates can offer morally redeeming accounts. A process of moral storytelling re-inflates credit reports with social meaning and prevents people with bad credit from getting jobs. This process carries implications for the reproduction of economic disadvantage since judgments about when it is and is not legitimate to have unpaid debt seem to at least partly depend on social background. 1. Introduction Consumer credit reports have spread far beyond lending institutions. In the USA, organizations consult credit reports when making decisions about whether to grant individuals access to a wide range of economic resources, including rental housing, insurance, utilities and—most controversially—jobs (Shepard, 2012; Traub, 2013).1 About half of American employers at least sometimes consider credit history in deciding whom to hire (Society for Human Resource Management, 2012), a practice that ties financial history to prospects for employment. Yet how hiring professionals make sense of credit reports remains a mystery (Bryan and Palmer, 2012). Different from credit scores, credit reports are long documents with detailed data about individual credit card accounts, mortgages, student loans, medical debts, money-related court judgments and more. Figure 1 shows the sort of report employers see. While economic sociologists and surveillance scholars have drawn attention to the increasing reach of credit records into everyday life (Marron, 2009; Fourcade and Healy, 2013; Roderick, 2014; Rona-Tas, 2017), this work generally assumes that the rationalized understandings and calculative technologies of financial firms, like risk-based credit scores, carry over to non-lending realms. This comports with organizations’ increasing reliance on metrics for making decisions (Espeland and Sauder, 2007; Espeland and Stevens, 2008; Timmermans and Epstein, 2010), although a lack of clear evidence about whether credit history mathematically predicts workplace behavior complicates this possibility (Weaver, 2015). By contrast, literature demonstrating the irrepressibly social and moral meaning of economic life (Zelizer, 1994, 2011, 2012; Fourcade and Healy, 2007; Wherry, 2008; Livne, 2014) opens the door for another possibility: that hiring professionals might draw on more traditional understandings of credit history as a mark of moral character (Carruthers, 2009; Graeber, 2012). Employers could quantify and compare job candidates or evaluate them morally as individuals, two very different mechanisms, with different implications, for linking credit data to hiring outcomes. Figure 1. View largeDownload slide View largeDownload slide Demonstration employment credit report. Note: This demonstration of the sort of document employers see is based on employment credit reports from background screening companies. These reports often include elaborate codes. I have spelled out categories here for ease of reading. Figure 1. View largeDownload slide View largeDownload slide Demonstration employment credit report. Note: This demonstration of the sort of document employers see is based on employment credit reports from background screening companies. These reports often include elaborate codes. I have spelled out categories here for ease of reading. Research on the aggregate effects of credit report use underscores the importance of understanding how credit history shapes employment outcomes. Credit reports can prevent people from getting jobs, and they can interact with other traits, such as race, to exacerbate or offset existing inequalities (Thorne, 2007; Maroto, 2012; Bartik and Nelson, 2016; Bos et al., 2016; Clifford and Shoag, 2016). Yet this largely quantitative literature stops short of exploring how employers move from raw credit-report data to decisions. Some 80% of employers that review credit reports sometimes hire people despite financial problems (Society for Human Resource Management, 2012). This suggests that the connection between credit history and hiring outcomes is contingent. What it is contingent on has been, until now, unclear. In this paper, I draw from interviews with 57 hiring professionals to study how employers translate credit reports into hiring decisions. I find that rather than simply use the standardized fields of credit reports to categorize and compare job candidates, hiring professionals selectively draw details from credit reports and turn to narrative in order to judge whether a person who has become delinquent on loans has acted immorally. In some cases, hiring professionals try to rely on the numerical fields of credit reports, but find themselves unable to do so fully. Faced with data about financial behavior but not social context, hiring professionals feel compelled to reach beyond the credit report, both by inferring the events that led to unpaid debt and by asking candidates to recount them. A process of moral storytelling connects credit history to hiring decisions. Job candidates at a disadvantage are those whose patterns of unpaid debt or explanations for lack of payment do not resonate with hiring professionals as morally legitimate, judgments that seem to at least partly hinge on social background. Whether a person with bad credit gets a job is contingent on a particular hiring professional’s understanding of the structural causes of bad credit, what counts as frivolous spending, types of debts that are sacred and must always be repaid, amounts of debt that are extreme and times in creditor–debtor relationships when it is okay to withhold payment. These findings demonstrate that even with robust data available, storytelling and moral meaning can remain indispensable links in deciding who gets what. Credit reports give hiring professionals copious data about job candidates’ financial pasts, but they do not explain the context in which financial events transpired, nor do they provide a score or other metric that mathematically links credit data to workplace outcomes. Hiring professionals therefore turn to narrative and moral judgment to move from document to hiring decision. Considering that social position shapes cultural know-how, like how to tell convincing stories (Polletta et al., 2011; Lareau, 2015), and understandings of morally upright behavior (Lamont, 1992; Hitlin and Vaisey, 2010), these findings carry important implications for the reproduction of economic disadvantage. 2. The systematization of credit history Given what we know about the sociology of credit, how might we expect hiring professionals to use credit reports? How a person repays borrowed money has long been seen as a moral matter, with timely repayment taken as a sign of trustworthiness (Carruthers, 2009; Graeber, 2012). Before credit bureaus circulated information about financial pasts, lenders relied on a potential borrower’s reputation within a community to decide whether he could be trusted with money (Carruthers and Espeland, 1998; Carruthers, 2005). Expectations of loan repayment and moral character have been so tightly tangled historically that when commercial credit agencies first arose in the USA in the mid-19th century, they tracked not only whether companies repaid borrowed funds, but also the details of proprietors’ lives, such as if they were hard-working and well-liked, or prone to drinking, gambling and philandering (Sandage, 2005; Lauer, 2008). Over time, commercial credit agencies, and the consumer credit bureaus that followed, worked to rationalize the presentation and interpretation of the information they gathered (Lauer, 2010). As agencies and bureaus created set categories for recording the financial behavior of companies and people, they funneled information into a form well-suited to mathematical models of probability and risk, moving away from individualized, character-based understandings of credit history (Guseva and Rona-Tas, 2001; Marron, 2009; cf. Moulton, 2007). In the late 19th century, commercial agencies developed ordinal credit ratings (Cohen, 2012; Carruthers, 2013), and in the early 20th century, consumer credit bureaus pioneered points systems for predicting who would repay (Gandy, 1993). In the 1950s, the company Fair Isaac (FICO) began to convince banks and retailers to extend consumer credit based on ‘scorecards,’ the predecessor of the FICO credit score (Poon, 2007). Calculation replaced moral judgment as users of credit history learned to sort and rank individuals into risk pools which predicted the likelihood of repayment. Lenders ostensibly no longer cared whether borrowers were trustworthy in any generalizable sense, only if their credit scores were high or low (Leyshon and Thrift, 1999; Marron, 2007). A crucial step along the way was taking the messy details of individual instances of borrowing, repayment and default, and fitting them into standard, de-contextualized categories of data, such as those that appear on a credit report. Hirschman et al. (2016) describe quantification as a multi-stage process, one that includes categorization, classification and commensuration, or the ‘transformation of different qualities into a common metric’ (Espeland and Stevens, 1998, p. 314). Fourcade and Healy (2017, p. 289) write that the ‘composite device’ of a credit score ‘depend[s] in the first instance on choices about the way credit events are defined and measured.’ A loan payment that is 90 days late because a person lost his job and a loan payment that is 90 days late because a person has a lackadaisical attitude toward financial obligations look exactly the same on a credit report, even though they might be qualitatively different in important ways. Although sociologists have yet to empirically study the use of credit reports in hiring, some have theorized about the migration of credit history into non-lending realms. These scholars evoke rationalized and de-personalized modes of sense-making similar to that deployed by lenders, referring to ‘scoring’(Fourcade and Healy, 2013), ‘rating’(Rona-Tas, 2017) and other computational processes. Indeed, in recent decades, ratings, rankings and formalized standards have spread throughout organizations of all types as accepted—even expected—modes of analysis and decision-making (Espeland and Sauder, 2007; Espeland and Stevens, 2008; Timmermans and Epstein, 2010). Such practices are often sold on the pragmatic promise of more dispassionate decision-making and coordination of activity, though they also can serve to signal to stakeholders that an organization is behaving legitimately, whether or not there is any material benefit (Power, 2007; Timmermans and Epstein, 2010). In hiring, the rigidity of bureaucratic rules and standards is at times celebrated for its ability to remove personal discretion from decisions and the bias it can introduce (Porter, 1995; Bielby, 2000). Following from these bodies of work, one possibility for how employers use credit history is that they adopt the lending industry’s practices of commensuration and comparison in order to take the information in a credit report and make predictions about the behavior of would-be employees. In such an approach to credit data, decisions would be made according to explicit standards or rules. Information about people would be reduced to forms—such as hierarchical categories or scores—that made it easy to consistently decide which individuals are ‘better’ than others by abstracting away from the particularities of individual situations. One complication to this possibility is that existing research provides few convincing correlations between personal financial data and employee behavior. Using nationally representative panel data, Weaver (2015) finds no link between credit outcomes, such as being late on a mortgage payment or rejected for credit, and productivity. Bryan and Palmer (2012) study a randomly selected sample of employees at a financial services firm and find no consistent relationship between credit report data—late payments, debts in collection and charge-offs—and job performance or termination. Drawing from a self-selected, non-random sample of university students, employees and alumni, Bernerth et al. (2012) find no correlation between credit scores and supervisors’ reports of bad workplace behavior, although the authors find a relationship between credit scores and supervisors’ assessment of task performance and ‘organizational citizenship,’ such as helping co-workers. Oppler et al. (2008) use a random sample of employees from a government agency, and find a correlation between self-reported loan delinquency and bankruptcy on the one hand, and ‘counterproductive work behaviors,’ such as failure to pay debts, misuse of credit cards and theft, on the other hand.2 It is difficult, though, to know what to make of this correlation, since certain independent and dependent variables are so similar (e.g. loan delinquency is an explanation while failure to pay debts is an outcome), and the authors note a surprisingly high rate of counterproductive work behaviors, casting doubt on generalizability. A lack of established predictive validity has not, however, impeded employer use of credit reports. This is ironic considering that credit reports were marketed and adopted after a 1988 law made it harder for employers to use polygraph tests because of validity concerns (Fuchsberg, 1990; Rona-Tas, 2017).3 Credit bureaus and background screening outfits portray credit reports as a way to identify which job candidates are likely to steal and otherwise be dishonest.4 This is reflected in employers’ explanations of why they use the reports, the best evidence of which comes from a set of low-response-rate surveys from the Society for Human Resource Management (SHRM), a trade group. In those surveys, the most chosen reason for using credit reports is ‘to reduce/prevent theft and embezzlement, other criminal activity.’ Another top reason is ‘to assess the overall trustworthiness of the job candidate’ (Society for Human Resource Management, 2012). Employers equate bad credit with an untrustworthy, irresponsible disposition. The strength of that belief may lend authority to credit report data, even without documented mathematical correlation. Predictive validity may also be beside the point if credit reports serve a different function. In the SHRM surveys, another oft-chosen reason for checking credit is ‘to reduce legal liability for negligent hiring,’ an outward-facing rationale that mimics a function of commercial credit ratings, which were used to signal sound decision-making long before there was evidence of their predictive validity (Carruthers, 2013). In hiring, what may matter more than a proven correlation is a broadly shared perception that credit reports are a sensible, defensible screening mechanism—perhaps tied to credit bureaus’ institutional role as arbiters of consumer financial standing. As in Power’s (1999) ‘audit society,’ certifying workers as safe by checking credit reports may serve as a ‘ritual of verification,’ a way to establish institutional legitimacy, regardless of the effect checking has on actual workplace outcomes.5 3. The enduring morality of economic life Even in the absence of predictive validity, employers could still approach credit reports with a calculative mindset, commensurating and comparing in unvalidated ways. Yet other sociological works suggest a second option: that hiring professionals might instead cling to traditional and personalized understandings of the particular ways in which credit history marks moral character. In their effort to rationalize lending decisions, credit bureaus may have formally stripped financial information of rich social context, but sociologists have shown time and again that money and markets carry moral valence and social meaning, even when they seem to be commodified (Zelizer, 1994, 2012; Fourcade and Healy, 2007; Wherry, 2012). Moral weight and social meaning can come from the form money takes (Zelizer, 1994), its source (Sykes et al., 2015) or the nature of relationships between economic actors (Zelizer, 2012). A credit report shows the timeliness of loan repayments, but also reveals which firms people have borrowed from, what they have borrowed for (as with a car loan) and the debts they prioritize repaying on time (Avery et al., 2003). Hiring professionals might use any of these details in determining what a credit report says about a job candidates’ moral standing. Indeed, Americans often distinguish between ‘good debt,’ like borrowing to buy a home, and ‘bad debt,’ like carrying a balance on a credit card (Kalousova and Burgard, 2013; Wherry, 2016). Sometimes people see debt as a self-empowering investment (Dwyer et al., 2012) and at other times as a shameful liability (Hodson et al., 2014). Even among delinquent debts there is room for variation, with context like the type of debt and nature of the creditor relationship affecting how people morally locate the act of non-payment. Studying debt collectors at work, Polletta and Tufail (2014) find that borrowers want to be absolved of debts they see as unfair, but often refuse to pay less than they owe on those tied to symbolically worthy spending, such as education or to providers of personally meaningful services, such as doctors—reflecting Zelizer’s (1989, 1994) foundational insight that people use money to mark the meaning of relationships. Similarly, Tach and Greene (2014) find that financially strapped borrowers have an easier time defaulting when they feel they were misled about the terms of a loan or treated poorly by a lender, but insist on paying debts that re-inforce a positive identity, like being upwardly mobile. From an employer’s perspective, how a person spends money could also be telling. Sociologists have long observed that spending patterns serve to designate social boundaries (Veblen, [1899] 1973; Bourdieu, 1984). Wherry (2008) extends these observations and theorizes that consumption does not simply categorize people, but also identifies them in morally imbued ways that rest partly on a person’s social position. Whether a purchase is seen as, say, foolish, frugal or frivolous, depends both on the price paid and the identity of the purchaser. When the US mortgage crisis of the late 2000s primarily affected working-class racial minorities, loan defaults were attributed to foolish borrowing, but once problems spread to richer white homeowners, there emerged a national narrative to lay blame on structural financial forces over which individuals had no control (Harvey, 2010; Wherry, 2012; on the foreclosure crisis and blame, also see McCormack, 2014). In the same vein, Hurst (2012) finds that bankruptcy judges treat debtors differently based on their class standing: those with advanced degrees are lectured to be ambitious in their careers to make more money, while those with no college education are admonished to spend less and have humbler life expectations. A very different line of research—about how organizations select individuals—also offers reason to think that employers might forgo calculative approaches to credit reports in favor of more personalized and culturally situated modes of understanding financial pasts. Many of the ways hiring professionals pick employees (e.g. with interviews) rest on mathematically unvalidated beliefs about how to identify good candidates (Dana et al., 2013). At times employers impose bureaucratic rules, tests and other standards to select workers more rationally, although even when rules guide action, decision-makers often re-introduce discretion, either intentionally or as a result of unconscious in-group bias (Gorman, 2005; Castilla, 2011; Dobbin et al., 2015). Having gone to a similar university as a job candidate, for example, can lead a hiring professional to especially value that candidate’s degree (Rivera, 2012). Employers (and other gatekeepers, like college admissions officers) often understand information about individuals through the dual prisms of culture and personal experience, harnessing tools such as narratives, symbols and emotional reactions to decide whether a person belongs (Moss and Tilly, 2001; Stevens, 2007; Rivera, 2015a). Even bank employees sometimes use subjective impressions of loan applicants and the stories they tell in deciding whether to lend money (Moulton, 2007). Therefore, a second possibility for how hiring professionals use credit reports is that they avoid de-personalized, calculative processes and instead make sense of credit history by assigning social and moral meaning to individual situations of borrowing and default. In this approach to credit data, decisions would be made by focusing on the particularities of the person at hand and according to employers’ understandings of acceptable and unacceptable behavior. Information about candidates would be understood through forms, such as stories, that leave substantial room for discretion and exception. In translating raw information into meaning, hiring professionals would draw on their own experiences, beliefs and moral distinctions. 4. Data and methods To understand how employers use credit history in hiring decisions, I conducted in-depth interviews with individuals who hire, including recruiters, human resources officers and corporate managers. Interviews are well-suited for understanding how people interpret the world around them and take actions in line with those interpretations (Weiss, 1994). Interviews with employers show that the way hiring professionals assign meaning to information can significantly shape hiring decisions (Kirschenman and Neckerman, 1991; Miller and Rosenbaum, 1997; Moss and Tilly, 2001; Rivera, 2012). Since this is the first research to examine how employers use credit reports, I used purposive sampling to adjust the mix of respondents as I learned about the phenomenon under study (Glaser and Strauss, 2007; Charmaz, 2012). From January to December 2014, I interviewed 57 hiring professionals, including 21 who evaluate credit reports and 17 who work alongside those who do. Hiring is typically a collaborative process (Rivera, 2015b), and different individuals often conduct interviews and pull information from administrative records like credit reports. The diversity of the sample allows me to understand this process as a whole. The remaining 19 respondents either discussed organizational policies regarding credit reports, work at background screening companies that sell credit reports to employers and have insight into the practices of many firms, or, in one case, advise companies as an employment lawyer. Five of these respondents discussed why their companies do not use credit reports, which helped put into analytical relief the arguments of those whose companies do. About half of respondents were female. The strength of purposive sampling is its ability to establish the contours of an unfamiliar process, but it does not produce a representative set of cases, and I cannot estimate frequencies in a larger population with the data. In addition to theoretical motivations, a key practical concern drove how I gathered respondents: there is no extant list of employers that use credit reports. I began with a list of large (Fortune 1000) and fast-growing (Inc. 5000) firms in the Boston area, and approached those likely to use credit reports.6 I assessed this by identifying industries described in the press as common users of credit reports in hiring, by searching for online job ads mentioning credit checks and by reading web sites where job seekers discuss applying for jobs at specific companies. As I combed these sources for information about companies on the list, I added other firms, including some outside the Boston area. I also searched the networking site LinkedIn.com for hiring professionals who listed experience evaluating credit reports. I contacted potential respondents either by phone, by email, through LinkedIn or in person at a firm’s headquarters or store. Individuals who hire tend to be busy professionals, and it often took multiple efforts at making contact—including showing up at places of employment—to get respondents to agree to interviews. Whenever possible, I asked an acquaintance for an introduction to increase the quality of the data, since hiring professionals are less likely to frankly share information with a stranger (Bewley, 1999). I presented myself as a researcher working on a study of how companies hire. Interviews covered the entire hiring process. About two-thirds of respondents brought up credit checks on their own. I recorded the interviews, which typically lasted between 1 and 2.5 h. To analyze the data, I began with a two-part memoing process (Miles and Huberman, 1994; Lofland et al., 2006). After each interview, I wrote a memo to capture salient aspects of the interview and to identify potentially meaningful concepts and practices. As I accumulated data, I began a second set of memos, to develop themes that cut across interviews. I imported transcripts into the software program MAXQDA to more formally assign codes and test ideas I developed inductively. To make theoretical sense of observed patterns, I returned to the literature, and then iterated between the data and extant research. The respondents I interviewed hire for many sorts of positions. The jobs I heard about included bank teller, hardware store manager, accountant, mortgage loan originator, window salesman, parking-lot attendant, computer software tester, fast-food restaurant assistant manager, call-center operator, bookkeeper, corporate vice president, executive chef, apartment handyman and human resources assistant. Table 1 presents a list of respondents’ titles and industries, as well as examples of jobs discussed. These jobs represent a mix of professional and non-professional positions, although the sampling strategy could have missed jobs for which hiring professionals evaluate credit history differently from the ways I present below, a limitation I discuss at the end of this paper. A few hiring professionals pulled credit reports for all job positions (largely to avoid claims of discrimination), but in most companies only certain positions required credit checks, typically those involving access to money, financial records or sensitive information, or which carried managerial or fiduciary responsibility.7 Table 1 List of respondents No.  Industry or sector  Respondent’s title†  Example of job discussed‡  1  Manufacturing  Human resources manager  Human resources administrator  2  Financial services  Employment officer  Bank teller  3  Real estate  Senior vice president  Property manager  4  Staffing  Recruiter  Accountant  5  Financial services  Recruiting administrator  Mortgage loan officer  6  Staffing  Managing director  Administrative assistant  7  Higher education  Recruitment consultant  Chief technology officer  8  Retail  Store manager  Cashier  9  Manufacturing  Senior corporate recruiter  Salesperson  10  Technology  Staffing lead  Software development engineer  11  Real estate  Property manager  Maintenance worker  12  Higher education  Vice president  College president  13  Retail  Human resources manager  Buyer  14  Retail  Recruiter  Database administrator  15  Staffing  Recruitment specialist  Receptionist  16  Staffing  Sourcing specialist  Administrative assistant  17  Financial services  Recruiter  Assistant branch manager  18  Financial services  Human resources manager  Benefits and payroll specialist  19  Retail  Human resources officer  Sales associate  20  Retail  Corporate recruiter  Store manager  21  Retail  Human resources officer  Sales associate  22  Staffing  Recruiter  Forklift operator  23  Financial services and technology  Senior technical recruiter  Software tester  24  Manufacturing  Human resources assistant  Salesperson  25  Financial services  Human resources manager  Loan officer  26  Financial services  Corporate recruiter  Loan officer  27  Staffing  Account recruiting manager  Production tech  28  Hotels  General manager  Director of sales  29  Financial services  Senior vice president, human resources  Mortgage loan originator  30  Retail  Director of human resources  Accountant  31  Technology management  Director of staffing  Software engineer  32  Retail  Corporate recruiting manager  Store manager  33  Retail  Human resources generalist  Baker  34  Government  Human resources staffing manager  Financial examiner  35  Manufacturing  Human resources manager  Accountant  36  Financial services  Vice president, administration  Bank teller  37  Hotels  General manager  Housekeeper  38  Technology  Human resources manager  Warehouse worker  39  Non-profit  Employment coordinator  Budget manager  40  Real estate  Senior vice president, human resources  Sales consultant  41  Staffing  Account manager  Scientist  42  Retail  Owner  Assistant store manager  43  Higher education  Assistant director of employment  Dean  44  Information technology  Hiring manager  Call center operator  45  Information management  Director of executive recruiting  Head of revenue management  46  Information management  Senior recruiter  Security analyst  47  Food services  Recruiter  Executive chef  48  Financial services and technology  Human resources director  Client services representative  49  Food services  Director of human resources  Assistant restaurant manager  50  Financial services  Human resources officer  Bank teller  51  Food services  Restaurant manager  Cashier  52  Insurance  Senior recruiting specialist  Call center operator  53  Background screening  Strategy manager  Mortgage loan originator  54  Background screening  Chief marketing officer  Bank teller  55  Background screening  Vice president of marketing  Truck driver  56  Background screening  Owner  Call center operator  57  Legal services  Employment lawyer  Corporate executive  No.  Industry or sector  Respondent’s title†  Example of job discussed‡  1  Manufacturing  Human resources manager  Human resources administrator  2  Financial services  Employment officer  Bank teller  3  Real estate  Senior vice president  Property manager  4  Staffing  Recruiter  Accountant  5  Financial services  Recruiting administrator  Mortgage loan officer  6  Staffing  Managing director  Administrative assistant  7  Higher education  Recruitment consultant  Chief technology officer  8  Retail  Store manager  Cashier  9  Manufacturing  Senior corporate recruiter  Salesperson  10  Technology  Staffing lead  Software development engineer  11  Real estate  Property manager  Maintenance worker  12  Higher education  Vice president  College president  13  Retail  Human resources manager  Buyer  14  Retail  Recruiter  Database administrator  15  Staffing  Recruitment specialist  Receptionist  16  Staffing  Sourcing specialist  Administrative assistant  17  Financial services  Recruiter  Assistant branch manager  18  Financial services  Human resources manager  Benefits and payroll specialist  19  Retail  Human resources officer  Sales associate  20  Retail  Corporate recruiter  Store manager  21  Retail  Human resources officer  Sales associate  22  Staffing  Recruiter  Forklift operator  23  Financial services and technology  Senior technical recruiter  Software tester  24  Manufacturing  Human resources assistant  Salesperson  25  Financial services  Human resources manager  Loan officer  26  Financial services  Corporate recruiter  Loan officer  27  Staffing  Account recruiting manager  Production tech  28  Hotels  General manager  Director of sales  29  Financial services  Senior vice president, human resources  Mortgage loan originator  30  Retail  Director of human resources  Accountant  31  Technology management  Director of staffing  Software engineer  32  Retail  Corporate recruiting manager  Store manager  33  Retail  Human resources generalist  Baker  34  Government  Human resources staffing manager  Financial examiner  35  Manufacturing  Human resources manager  Accountant  36  Financial services  Vice president, administration  Bank teller  37  Hotels  General manager  Housekeeper  38  Technology  Human resources manager  Warehouse worker  39  Non-profit  Employment coordinator  Budget manager  40  Real estate  Senior vice president, human resources  Sales consultant  41  Staffing  Account manager  Scientist  42  Retail  Owner  Assistant store manager  43  Higher education  Assistant director of employment  Dean  44  Information technology  Hiring manager  Call center operator  45  Information management  Director of executive recruiting  Head of revenue management  46  Information management  Senior recruiter  Security analyst  47  Food services  Recruiter  Executive chef  48  Financial services and technology  Human resources director  Client services representative  49  Food services  Director of human resources  Assistant restaurant manager  50  Financial services  Human resources officer  Bank teller  51  Food services  Restaurant manager  Cashier  52  Insurance  Senior recruiting specialist  Call center operator  53  Background screening  Strategy manager  Mortgage loan originator  54  Background screening  Chief marketing officer  Bank teller  55  Background screening  Vice president of marketing  Truck driver  56  Background screening  Owner  Call center operator  57  Legal services  Employment lawyer  Corporate executive  † I have slightly changed some respondents’ job titles to preserve confidentiality. ‡ Some respondents, whose companies do not use credit reports in hiring, discussed why positions do not require credit checks. Table 1 List of respondents No.  Industry or sector  Respondent’s title†  Example of job discussed‡  1  Manufacturing  Human resources manager  Human resources administrator  2  Financial services  Employment officer  Bank teller  3  Real estate  Senior vice president  Property manager  4  Staffing  Recruiter  Accountant  5  Financial services  Recruiting administrator  Mortgage loan officer  6  Staffing  Managing director  Administrative assistant  7  Higher education  Recruitment consultant  Chief technology officer  8  Retail  Store manager  Cashier  9  Manufacturing  Senior corporate recruiter  Salesperson  10  Technology  Staffing lead  Software development engineer  11  Real estate  Property manager  Maintenance worker  12  Higher education  Vice president  College president  13  Retail  Human resources manager  Buyer  14  Retail  Recruiter  Database administrator  15  Staffing  Recruitment specialist  Receptionist  16  Staffing  Sourcing specialist  Administrative assistant  17  Financial services  Recruiter  Assistant branch manager  18  Financial services  Human resources manager  Benefits and payroll specialist  19  Retail  Human resources officer  Sales associate  20  Retail  Corporate recruiter  Store manager  21  Retail  Human resources officer  Sales associate  22  Staffing  Recruiter  Forklift operator  23  Financial services and technology  Senior technical recruiter  Software tester  24  Manufacturing  Human resources assistant  Salesperson  25  Financial services  Human resources manager  Loan officer  26  Financial services  Corporate recruiter  Loan officer  27  Staffing  Account recruiting manager  Production tech  28  Hotels  General manager  Director of sales  29  Financial services  Senior vice president, human resources  Mortgage loan originator  30  Retail  Director of human resources  Accountant  31  Technology management  Director of staffing  Software engineer  32  Retail  Corporate recruiting manager  Store manager  33  Retail  Human resources generalist  Baker  34  Government  Human resources staffing manager  Financial examiner  35  Manufacturing  Human resources manager  Accountant  36  Financial services  Vice president, administration  Bank teller  37  Hotels  General manager  Housekeeper  38  Technology  Human resources manager  Warehouse worker  39  Non-profit  Employment coordinator  Budget manager  40  Real estate  Senior vice president, human resources  Sales consultant  41  Staffing  Account manager  Scientist  42  Retail  Owner  Assistant store manager  43  Higher education  Assistant director of employment  Dean  44  Information technology  Hiring manager  Call center operator  45  Information management  Director of executive recruiting  Head of revenue management  46  Information management  Senior recruiter  Security analyst  47  Food services  Recruiter  Executive chef  48  Financial services and technology  Human resources director  Client services representative  49  Food services  Director of human resources  Assistant restaurant manager  50  Financial services  Human resources officer  Bank teller  51  Food services  Restaurant manager  Cashier  52  Insurance  Senior recruiting specialist  Call center operator  53  Background screening  Strategy manager  Mortgage loan originator  54  Background screening  Chief marketing officer  Bank teller  55  Background screening  Vice president of marketing  Truck driver  56  Background screening  Owner  Call center operator  57  Legal services  Employment lawyer  Corporate executive  No.  Industry or sector  Respondent’s title†  Example of job discussed‡  1  Manufacturing  Human resources manager  Human resources administrator  2  Financial services  Employment officer  Bank teller  3  Real estate  Senior vice president  Property manager  4  Staffing  Recruiter  Accountant  5  Financial services  Recruiting administrator  Mortgage loan officer  6  Staffing  Managing director  Administrative assistant  7  Higher education  Recruitment consultant  Chief technology officer  8  Retail  Store manager  Cashier  9  Manufacturing  Senior corporate recruiter  Salesperson  10  Technology  Staffing lead  Software development engineer  11  Real estate  Property manager  Maintenance worker  12  Higher education  Vice president  College president  13  Retail  Human resources manager  Buyer  14  Retail  Recruiter  Database administrator  15  Staffing  Recruitment specialist  Receptionist  16  Staffing  Sourcing specialist  Administrative assistant  17  Financial services  Recruiter  Assistant branch manager  18  Financial services  Human resources manager  Benefits and payroll specialist  19  Retail  Human resources officer  Sales associate  20  Retail  Corporate recruiter  Store manager  21  Retail  Human resources officer  Sales associate  22  Staffing  Recruiter  Forklift operator  23  Financial services and technology  Senior technical recruiter  Software tester  24  Manufacturing  Human resources assistant  Salesperson  25  Financial services  Human resources manager  Loan officer  26  Financial services  Corporate recruiter  Loan officer  27  Staffing  Account recruiting manager  Production tech  28  Hotels  General manager  Director of sales  29  Financial services  Senior vice president, human resources  Mortgage loan originator  30  Retail  Director of human resources  Accountant  31  Technology management  Director of staffing  Software engineer  32  Retail  Corporate recruiting manager  Store manager  33  Retail  Human resources generalist  Baker  34  Government  Human resources staffing manager  Financial examiner  35  Manufacturing  Human resources manager  Accountant  36  Financial services  Vice president, administration  Bank teller  37  Hotels  General manager  Housekeeper  38  Technology  Human resources manager  Warehouse worker  39  Non-profit  Employment coordinator  Budget manager  40  Real estate  Senior vice president, human resources  Sales consultant  41  Staffing  Account manager  Scientist  42  Retail  Owner  Assistant store manager  43  Higher education  Assistant director of employment  Dean  44  Information technology  Hiring manager  Call center operator  45  Information management  Director of executive recruiting  Head of revenue management  46  Information management  Senior recruiter  Security analyst  47  Food services  Recruiter  Executive chef  48  Financial services and technology  Human resources director  Client services representative  49  Food services  Director of human resources  Assistant restaurant manager  50  Financial services  Human resources officer  Bank teller  51  Food services  Restaurant manager  Cashier  52  Insurance  Senior recruiting specialist  Call center operator  53  Background screening  Strategy manager  Mortgage loan originator  54  Background screening  Chief marketing officer  Bank teller  55  Background screening  Vice president of marketing  Truck driver  56  Background screening  Owner  Call center operator  57  Legal services  Employment lawyer  Corporate executive  † I have slightly changed some respondents’ job titles to preserve confidentiality. ‡ Some respondents, whose companies do not use credit reports in hiring, discussed why positions do not require credit checks. 5. Findings and analysis 5.1 Approaching the credit report Respondents in the current sample overwhelmingly thought credit reports could offer insight into a job candidate’s moral character, and, relatedly, the candidate’s likelihood of stealing or otherwise misbehaving on the job, especially with respect to money. Respondents who did not consult credit reports said either that they had other ways of assessing applicants’ trustworthiness and money-savvy, or that they did not believe credit history was a clear indicator of character. Some respondents, especially those in heavily regulated industries like financial services, also gave a second reason for considering credit reports: to demonstrate to others, including regulators, investors and business partners, that they had done due diligence in vetting candidates. Credit reports thus functioned as an institutionally legible mode of establishing trustworthiness.8 Employers, both in this sample and in general, pay background screening companies or credit bureaus to pull credit reports.9 As demonstrated in Figure 1, the credit reports hiring professionals receive are long and complicated, showing which accounts are late, in collections, and written off as uncollectable, as well as any instances of bankruptcy, foreclosure, tax lien, wage garnishment, repossession, unpaid child support or money-related civil judgments. Credit reports present an array of numerical fields, though they fall short of the full quantification of a credit score. None of the respondents in my sample received credit scores, since it is standard practice in the background screening industry not to sell them to employers (Clemans, 2013).10 When I asked respondents how they learned to interpret credit reports, nearly all said they picked it up on the job, sometimes with advice from colleagues, but typically not. Other respondents cited the importance of ‘judgment’ and ‘professional experience.’ Six respondents said they took guidance from background screening companies or the trade group SHRM, but this advice was about when to run a credit check (e.g. if a job involved handling money) or how to legally deny a person a job because of one. No respondent gave an example of a professional or educational resource for learning how to look at a credit report and decide whether a candidate should be hired. A manager at a background screening company said employers would like such advice, but companies do not offer it for fear of legal liability, a fear related to the fact that there is no clear evidence that credit reports accurately predict workplace behavior. Hiring professionals therefore had to decide for themselves how to translate credit history into hiring decisions. About one-fifth of respondents (n = 12) described sets of rules to guide the use of data from a credit report, often in the form of a table or ‘matrix,’ to indicate, for example, the permissible number of debts in collection. These guidelines helped take complicated credit reports and distill them into a decision by relying on the processes of categorization and comparison. That is to say, at least some hiring professionals started out with a calculative (though not fully quantitative) approach, designing ways of sorting and ranking candidates based on credit history in order to hold each person to the same standards. In the absence of known mathematical correlations between parts of a credit report and workplace outcomes, hiring professionals created rules and standards by mobilizing intuitions about what qualified as a ‘bad’ credit record. For example, a human resources manager at one bank described a rule that the bank should not hire a candidate if he or she was behind on more than $20 000 in loans. A group of bank employees arrived at that figure based on what they had seen on job–candidate credit reports in the past and discussions about what they each thought was ‘reasonable.’ By contrast, employees at another bank contemplated, but ultimately rejected, having a dollar threshold. Instead, hiring professionals there decided that being delinquent on three or more accounts, no matter the amount, was a more important indicator of a problem. Even among firms in the same industry, rules varied greatly. This variation in standard-setting was enabled by employers generally not thinking about credit data in the probabilistic way that most lenders do. Only one respondent claimed there was a mathematical correlation between personal financial distress and bad workplace behavior (specifically, stealing customers’ credit card information). One other respondent, a vice president of human resources at a property management company, said that credit–workplace correlations had never been established, which was why his company did not run credit checks. Instead of invoking correlations, all other respondents, including those using written standards, explained the value of credit reports by deploying logical arguments rooted in lay beliefs about human behavior. As the owner of a string of hardware stores said: ‘If you can loan somebody money and they meet their obligations, then they are probably going to meet their obligations to you as a responsible employee.’ Employers assumed that people typically behave the same way from one situation to the next (absent exceptional circumstances), and so behaving well as a borrower would likely translate into behaving well as a worker. Because respondents relied on generalized beliefs about how people behave, they understood the connection between credit history and workplace behavior holistically. Broad patterns in a credit report were imagined to go with broad patterns of behavior at work. Therefore, when it came time to operationalize the relationship for the purposes of making hiring decisions, respondents came up with a wide array of ideas about what to pay attention to. While most respondents emphasized that they were looking for ‘red flags,’ such as ‘patterns’ of unpaid loans, especially those that were long past due or in collections, there were often sharp differences in the specifics, as I explore in detail below.11 Surprisingly, then, even though hiring professionals found much to pay attention to in credit reports, they consistently reached for more. Time and again, they sought out stories to contextualize loan non-repayment and other behavior. Credit reports contain data points stripped of personal and social context, such as a person’s attitude toward loan repayment and the presence of life events like divorce and job loss. Without fail, hiring professionals saw these details as crucial for understanding what a bad credit report meant. This was true both for hiring professionals with sets of rules to follow and for those without written guidelines. All respondents gave examples of times when it might be forgivable to default on debts. Making sense of a credit report therefore posed a great challenge: to tell if this was one of those times. 5.2 The practice of moral storytelling To address that challenge, hiring professionals turned to storytelling. They did this in two ways. First, they inferred stories about a person’s life from the credit report. Second, they contacted job candidates to see if they could tell a story about their financial problems in a redeeming way. I call this process moral storytelling. It is moral in the sense that it rests on conceptions of right and wrong which dictate how one ought to behave and feel (Smith, 2009), and it is storytelling in that it is structured around real or imagined sequences of events and associated mental states (Bruner, 1990). The goal of moral storytelling was to tease apart whether credit report problems were the result of the person or the situation (Ross and Nisbett, 1991). That is to say, whether a bad credit report indicated a job candidate had a poor moral character and would likely act in bad faith again (the person), or if given different circumstances, one might reasonably expect the candidate to act differently (the situation). Even employers who began with written standards almost invariably turned to moral storytelling by the end of the decision-making process by calling job candidates who failed to meet standards and asking them to explain their credit problems.12 This suggests that when the use of data rests on common-sense assumptions rather than mathematically validated empirics, the system can only take a decision-maker so far. Unlike lenders, hiring professionals do not have outside evidence that particular combinations of delinquencies accurately predict future behavior. The context-free data that appear on a credit report therefore did not tell hiring professionals everything they needed to know. To make a decision, they also sought to understand why the numbers looked as they did. The first way hiring professionals did this was to make inferences about the events in a job candidate’s life based on the types and amounts of unpaid debts present on a credit report. If hiring professionals understood non-payment as resulting from an unintentional or justified sequence of events, then they assigned blame to the situation and concluded that the candidate could be hired. Hiring professionals did this by mobilizing stories about the structural causes of credit problems, and by imagining events in a debtor–creditor relationship such that the creditor was seen as a rightful non-payer. By contrast, if hiring professionals understood non-payment as resulting from an intentional or unjustified sequence of events, then they attributed blame to the person and eliminated the candidate from contention for the job. Hiring professionals did this by understanding certain debts as sacred and particularly bad to default on, by attributing moral weight to the amount of outstanding debt, and by casting certain sorts of spending as frivolous. One frequently cited indication that the situation, not the job candidate, was to blame was if the debt had to do with medical treatment. Medical debt is not always labeled as such on a credit report, but when it was, employers generally assumed that people had been overwhelmed by unexpected but necessary bills; that the structure of the US healthcare system frequently left people with large debts they could not legitimately be expected to quickly pay off in full. Some respondents also imagined job candidates with unpaid medical debt as rightful non-payers. As a recruiter at a window manufacturer explained: ‘We typically won’t hold [delinquent medical debt] against you. You might just be working it out with your healthcare, trying to get insurance to pay.’ The moral ambiguity of unpaid medical debt was so great that one company taking a calculative approach baked it into the rules. A recruiter for a financial data firm said that while company policy was not to hire anyone with more than two accounts in default, medical debts did not count toward the total. Other sorts of unpaid debt could also lead hiring professionals to mobilize structural and rightful non-payer explanations in order to attribute blame to the situation. A vice president of human resources at a bank explained that she was less judgmental of credit problems if a student loan appeared on the report, relying on a structural account of non-payment: ‘You go to school and you come out and owe $80 000 and you’re making $12 an hour. How can you pay those loans back when the interest rates are so high?… It’s really hard to find a job nowadays.’ Meanwhile, the owner of a background check outfit showed empathy for individuals with debts at electronics retailers like Best Buy by imagining wronged consumers as rightful non-payers, explaining: ‘Some people will buy a TV and it will break and they’re like I’m not paying it… Okay, they’re angry, they bought something, it broke.’13 At other times, clues on a credit report led hiring professionals to assign blame for unpaid debt to the person rather that to the situation. Even in the face of situational disadvantage, some types of financial obligations remained sacred in the eyes of hiring professionals, which made non-payment a reason to reject a job candidate. At one fast-food company, the director of human resources said that after the most recent recession she grew more sympathetic about late credit card payments, but that she would still never hire a job candidate who was behind on child support, since providing for one’s children should always be a top priority: ‘If you don’t have a job, get a job, you know? Figure it out, take care of your child.’ Forms of extreme non-payment, such as unpaid debts worth hundreds of thousands of dollars, also often eliminated job candidates, underscoring Livne’s (2014) point that quantities, not just categories, of money can be moralized. At times bankruptcy also registered as a form of extreme non-payment. A hiring professional at one bank explained why declaring bankruptcy was so bad, saying: ‘Bankruptcy means you’re walking into this court and saying I’m not going to pay any of these bills, forgive them for me… People are looking for the easy way out.’14 Finally, hiring professionals assigned blame for unpaid debt to the person rather than the situation when they viewed the spending that led to the debt as frivolous. This they inferred from the merchant associated with the account or card; for example, a cell-phone company or lingerie store. The head of human resources at the fast-food company was particularly critical when a candidate defaulted on a credit card issued by an electronics retailer: ‘If it was like a Walmart thing, I can say okay at Walmart you can buy food, you can buy clothes. If it’s a Best Buy thing, I mean do you really need electronics?’ The difference between this reaction and that of the background check outfit owner above is a theoretically suggestive one I explore in the next section. Strikingly, the practice of storytelling surfaced time and again as hiring professionals described interpreting specific credit reports. Respondents practically always recounted their thinking as narrative. An explanation of why bankruptcy was so bad involved envisioning a person walking into court and asking a judge to erase the past. Forgiveness for a late medical payment included imagining a conversation between the job candidate and his insurance company. To understand why hiring professionals would rely on stories, we return to the challenge they face: telling, from a context-free credit report, whether unpaid debts indicate something about a candidate’s character or life circumstances. Storytelling helps fill in details necessary for making that distinction. Stories, especially those that take the form of justifications and excuses, contextualize unexpected or deviant behavior in a way that allows the offending party to be seen as normal once more (Scott and Lyman, 1968). As Bruner (1990, pp. 49–50) writes: ‘The function of the story is to find an intentional state that mitigates or at least makes comprehensible a deviation from a canonical cultural pattern.’ Through stories, hiring professionals found reasons to forgive. Perhaps unsurprisingly, then, the second way that hiring professionals assigned moral meaning to instances of unpaid debt was to listen to the stories job candidates themselves told. Respondents described two moments when candidates might offer an account: at the end of interviewing, if a hiring professional told a candidate that a background check was the next step in the hiring process, and once a check was underway, if there was a problem with a candidate’s credit report. When credit reports revealed a problem, hiring professionals typically called the candidate to ask for an explanation.15 To tease apart whether delinquent debt was justified or not—the result of the person or the situation—hiring professionals deployed many of the same tools they used in inferring stories from credit reports. Nearly, all respondents talked about job candidates explaining they failed to repay loans because of job loss or divorce, and many relayed stories about economic downturn, the high cost of medical treatment and the difficulty of paying back student loans while working low-salary jobs. Respondents were generally sympathetic to these situations and discussed times when they hired someone who had been in such circumstances. This, however, rested on job candidates’ willingness to disclose intimate details of their personal lives. As a recruiter at a retailer hiring for an executive assistant position said: ‘I had someone who felt I was prying into their business and didn’t want to share, so I couldn’t move forward. A lot of people think it’s prying into their personal information. I can’t make an exception for you.’ To determine the veracity of the stories job candidates told, hiring professionals sometimes asked for documentation—divorce decrees, letters from creditors, proof of credit counseling—but more often than not they relied on their impressions of whether a candidate was telling the truth. In this way, what mattered was not only what a candidate said, but also how he or she said it. One bank recruiter explained the importance of seeming ‘genuine,’ saying: ‘If it sounds legitimate, fine, but if you’re talking over yourself and continuously backtracking where I’m like, but wait tell me a little bit more about this, and then your story sounds completely different, that just doesn’t work for me.’ A managing director of a staffing agency described one woman who had defaulted on student loans and did not get a job as an administrative assistant in a human resources department because she sounded ‘indifferent.’ He also described a woman who got a job at a consulting firm after properly explaining her loan delinquencies. ‘They respected her presentation of it,’ he said. ‘She could have been emotional but was right up to the line of emotional. She was very thoughtful in the way she delivered it.’ As scholars of self-presentation have observed, a convincing presentation solidifies the believability of the story a person tells (Garfinkel, 1956; Goffman, 1959). This careful moral redemption dance that hiring professionals expected job candidates to perform at times included one final component: offering a story at the right time. A handful of hiring professionals described telling job candidates at the end of interviewing that a background check would be the next step. Those with credit problems who sat silent in this moment were later deemed untrustworthy. A hardware store owner described one candidate for assistant manager who, upon hearing there would be a background check, told the owner that he had started his own business, and when it failed to take off, got behind on his credit card and mortgage payments. When the store owner later saw the credit report, he ‘was able to say, yep, okay, here it is, I see why he fell behind in his credit cards.’ Asked what his reaction would have been had the candidate not volunteered the story about the failed business, he said: ‘I probably wouldn’t hire this person because he might want to steal from me.’ 5.3 Moral mismatch and other hiring off-ramps The literature that looks for correlations between credit history and workplace outcomes and finds few implies that one sort of ‘error’ which might arise from the use of credit reports in hiring is excluding job candidates who pose no threat, and gaining a false sense of security about those who might. The data I have presented in this paper imply that other sorts of errors could also occur, even if hiring professionals are right in thinking that the key to using credit history is narratively teasing apart whether unpaid debt is the result of the person or the situation. For instance, employers’ use of the names of creditors to morally locate unpaid debt is problematic since mislabeling can easily occur. One respondent described an applicant who was rejected for a job because he had tried to pay his medical bills by putting them on his credit cards. When he fell behind on payments, the delinquencies registered as credit card, not medical, debt. Similarly, many store-branded credit cards can be used to make purchases elsewhere, obscuring what was actually bought. Another sort of error that can occur is that job candidates may be rejected from jobs simply because they do not wish to discuss intimate details of their personal lives, like their marital status or medical history. Here, a perfectly acceptable moral story may exist but not be volunteered. A more fundamental source of error that can arise from the process of moral storytelling is a sort of moral mismatch: a disconnect between a job candidate’s behavior and a hiring professional’s understanding of that behavior based on differences in social position. Consider, again, the opposite interpretations of a delinquent charge on a Best Buy credit card. Depending on the hiring professional, a late payment indicated either a frivolous spending spree or a principled stand against paying for a faulty product. In one case, blame was assigned to the person and in the other, to the situation. How could this be? The difference might be idiosyncratic. Yet it does feel relevant that a male hiring professional imagined an electronic device breaking and cut a job candidate some slack, while a female hiring professional thought about how electronics are frivolous purchases compared with essentials like food and clothing and judged a job candidate harshly. The nature of the data in this study can only suggest that hiring professionals use moral stories in ways that reflect their social locations and culturally situated understandings of when it is (and is not) acceptable for a person to fall behind on debt payments. The idea warrants further testing, although extant research lends support to the premise, with scholars of both narrative and moral judgment finding that social background, such as gender, race and class, shape what people see as legitimate (Lamont, 1992; Hitlin and Vaisey, 2010; Polletta et al., 2011). The fact that the male hiring professional who overlooked the Best Buy credit card default was then strongly critical of a female candidate who defaulted on a credit card issued by the lingerie shop Victoria’s Secret takes on greater significance in light of other research pointing to a connection between social location and conceptions of frivolous spending (Wherry, 2008). Job candidates may benefit from these dynamics when their life experiences mirror those of hiring professionals and the people they know, since such experiences provide fodder for moral storytelling. For example, a vice president of human resources at a bank who discussed going easy on people whose credit reports showed student loans along with delinquencies talked at length about her college-aged son and his friends and their experiences finding jobs in a slow economic recovery. Or, as a recruiter at another bank more pointedly said: ‘I have a sensitive spot for individuals with student loans because I’m one of them [laughs]… Especially if [the lender] Sallie Mae is on there. I know how much of a beast Sallie Mae is.’ Yet even absent personal experience, a hiring professional’s social position, such as class, can color beliefs about how the world works and consequently how he or she assigns blame for unpaid debt to the person or the situation. For example, a middle-class head hunter described three finalists for a school presidency. Two were women with credit problems, one from a divorce and one from a mistaken foreclosure proceeding related to the late-2000s housing crisis. The head hunter asked each candidate for a written account of what had transpired, and found the stories ‘compelling,’ seeing no reason to prevent the hire in either case. But the chair of the school’s board of trustees, a banker, felt otherwise, and refused to hire either. As the head hunter later explained: ‘Someone like him who comes from generation after generation of inherited wealth has no idea how hard it is to maintain a good credit rating, but he sat in judgment of these women post-2008, and he held that against them.’ Beyond moral mismatch, the data reveal one other major way moral storytelling can short circuit, deflecting people from being hired even when they have redeeming accounts. Credit reports often contain mistakes.16 This serves as a roadblock, especially when hiring professionals use credit reports not simply to learn about a job candidate, but also to signal to external constituents, such as regulators, business partners and investors, that they have conducted due diligence. For example, an employment officer at a bank described a candidate for a teller position whose credit report incorrectly included delinquent loans taken out by a similarly named person. The employment officer knew the report was wrong, but she would not hire the candidate until the report was fixed. As she explained: ‘They were working on it, but we needed documentation because at the end of the day we have external auditors that come in and audit everything we do.’ The need to produce a flaw-free record for inspection mattered just as much as the morally redeeming story. Indeed, most respondents described dealing with credit reports mistakes, including those stemming from identity theft. Some respondents said that if a job candidate could prove they had repaid a loan shown to be in delinquency, then the hiring process could proceed. Others, however, insisted that the credit report itself be fixed, a process that could take months. In such situations, hiring professionals rarely held jobs open, but they seldom saw this as problematic, saying that candidates could just apply again later. Respondents were very concerned about the possibility of hiring a morally compromised person, but less so about failing to hire a morally pure one. 6. Discussion When hiring professionals look at credit reports, they see information about a job candidate’s financial past presented in a rationalized way. The particularities of individual situations are gone, which lets lenders more easily compare people, often in mathematically sophisticated ways. Like other bureaucratic documents, credit reports have undergone a ‘deliberate stripping away of context’ which ‘detaches and obliterates social relationships’ so that ‘different events are rendered similar by virtue of the standardized categories that are imposed on them’ (Espeland, 1993, p. 299). This is deeply problematic for hiring professionals. The contemporary credit report rests on the premise that de-contextualized data about how a person has behaved can shed light on how he will behave in the future, but this approach fails employers trying to repurpose repayment data into workplace expectations. With no evidence about how parts of a credit report mathematically connect to workplace outcomes, hiring professionals struggle to understand what credit history means since the same numerical patterns might arise from different circumstances. Credit reports commensurate different lived experiences of debt and default, leaving no distinction between delinquencies that hiring professionals consider to be legitimate and those they deem unforgivable. Hiring professionals therefore rely on moral storytelling, a practice rooted in traditional, character-based understandings of credit history (Carruthers and Espeland, 1998; Carruthers, 2005). When a job candidate who has defaulted on loans offers a morally redeeming account of why events have played out as they have, the candidate demonstrates knowledge of how things ought to be and indicates connection to the moral order (Scott and Lyman, 1968; Bruner, 1990; Harvey et al., 1990). Moral stories are so useful for disentangling whether loan non-repayment stems from circumstance or character flaw that hiring professionals even construct them on job candidates’ behalf, imagining the events and intentions that sit behind particular sorts of unpaid debt. It would be easy to assume that hiring professionals use credit reports in a calculative manner, but this paper has shown that efforts to categorize and compare job candidates only go so far. Without known correlations between credit data and employment outcomes, storytelling kicks in as a way to fill in the gaps inherent in credit reports. The specific ways employers use moral stories to interpret tarnished credit history as either forgivable or not are patterned along lines that echo observations from the sociological study of money and debt. When hiring professionals attribute loan delinquencies to frivolous spending (Wherry, 2008), the betrayal of sacred debts (Polletta and Tufail, 2014), or extreme forms of behavior as reflected by the large amounts of money at stake (Livne, 2014), they assign responsibility to the candidate (the person) and steer away from making a job offer. When hiring professionals attribute non-payment to structural factors that cause bad credit (McCormack, 2014) or to a debtor rightfully withholding money from a creditor (Tach and Greene, 2014), they assign responsibility to the situation and move forward with hiring. Economic and moral life remain entangled, with different categories of money carrying different normative valences (Zelizer, 1994) and the nature of relationships playing a key role in determining appropriate behavior (Zelizer, 2012; Wherry, 2016). The twist in how moral storytelling plays out is that when hiring professionals judge whether job candidates have morally redeeming reasons for not repaying debts, they do so from a particular position in the social structure, which means their decisions reflect a particular moral view of the world (Lamont, 1992; Lempert and Monsma, 1994). While the data presented in this study are only suggestive, it seems that hiring professionals are especially, forgiving of situations that are easy for them to imagine personally experiencing. Moral storytelling is thus a relational process, one in which social distance helps determine how empathetic a hiring professional is likely to be (Park, 1924; Bogardus, 1937; Bourdieu, 1989). When there is more distance between a hiring professional and a job candidate—whether that distance arises from differences in gender, class or some other social location—it is less likely that the hiring professional will forgive unpaid debt and grant the candidate a job. While I do not observe job candidates accounting for bad credit, existing literature suggests that candidates with social backgrounds similar to hiring professionals may also be better at crafting accounts of financial difficulty that register as forgivable (Orbuch, 1997; Polletta et al., 2011; Lareau, 2015). 6.1 Broader implications In showing that the effect of bad credit on employment is contingent on moral storytelling, this study contributes to a growing body of literature about how the allocation of economic opportunity hinges not just on what information decision-makers have about people, but also on the way decision-makers create meaning from that information (Rivera, 2012; Lamont et al., 2014). Stevens (2007), for example, shows that college admissions officers use details from application packets to construct life stories about students in order to decide who to admit, and in ways that systematically favor students with starting socio-economic advantage—a process that parallels the one described in this paper. More generally, the role of moral storytelling in hiring implies that to fully understand the reproduction of economic disadvantage, scholars may need to pinpoint how macro-level trends, such as increasing reliance on personal borrowing (Krippner, 2011; Prasad, 2012), filter through organizational gatekeepers and the meaning they assign to specific situations. Americans frequently have trouble repaying their loans (Porter, 2012), with nearly a third of credit reports showing at least one debt in collections (Avery et al., 2003; Ratcliffe et al., 2014). Fourcade and Healy (2013) explicate how the spread of credit history into non-lending realms has the potential to both reproduce disadvantage across economic domains and create new forms of inequality (see also, Rona-Tas, 2017). While this work focuses on the role of actuarial techniques, such as credit scoring, in this paper I show how market-influenced ‘classification situations’ might also take shape less mechanically (Fourcade and Healy, 2013, p. 561). In the case of hiring, how the credit market splits and sorts gets refracted through hiring professionals’ socially positioned conceptions of personal financial life. When hiring professionals say they use ‘judgment’ to determine if a bad credit report will scuttle a job candidacy, they are speaking to a cultural mechanism (Lamont et al., 2014) that completes the loop from market situation to unequal life outcomes. While this research cannot say how much moral storytelling and its attendant biases would persist if there were clear mathematical correlations between credit data and employee behavior to rely on, the use of credit scores in insurance pricing—where scores are both highly predictive and morally contentious (Rona-Tas, 2017)—suggests that scoring need not entirely crowd out moral thinking. Furthermore, even if credit data were shown to mathematically predict workplace outcomes, there might be other reasons to limit their use (e.g. the potential for cumulative disadvantage across credit and labor markets). 6.2 Limitations and future research While this paper shows how hiring professionals interpret credit reports, its purposive design may limit generalizability. I interviewed respondents who hire for professional and non-professional positions across industry and organization size and consistently found that moral storytelling links credit history to hiring decisions, but it is possible that a more rules-bound process occurs in other contexts. Notably, all but one respondent discussed considering credit history during the final phase of hiring, to evaluate a single or short list of candidates. If credit history were used as a preliminary screen for a large field of applicants, then I would expect a less individualized approach.17 Since trade-group surveys show that nearly all firms run credit checks toward the end of the hiring process (Society for Human Resource Management, 2012), I do not believe this poses a major threat to generalizability.18 Additionally, even if credit checks were used earlier in the hiring process, they could still not be informed by empirically established correlations (since none exist), which means the practice would still be subject to biases, though they might be applied more consistently. One direction for future research is to quantify the patterns identified in this paper, especially the concept of moral mismatch. Policymakers have expressed concern that credit checks may disproportionately impact certain groups of people with worse credit on average, such as African-Americans, but policy debate has not included the possibility that credit checks may be problematic for another reason: that hiring professionals understand the ‘badness’ of a credit report differently based on whose credit report it is.19 Quantitative research might also explore if certain candidates (such as those applying for more senior positions) are held to higher standards; that is to say, whether the dynamics described in this paper especially prevent access to particular types of jobs. Future research might also investigate the strategies of job candidates with bad credit. While the current study does not provide a line of sight into the job-candidate experience, a few hiring professionals mentioned that upon learning there would be a credit check, some candidates withdrew their applications. If candidates with bad credit self-select out of certain jobs, then another sort of ‘error’ may be introduced—people who would have gotten through the process of moral storytelling not getting a shot to do so. A final question for future research is how employers decide to consult credit reports in the first place. While we know that credit bureaus began marketing credit reports as a hiring screen in the late 1980s, no research has systematically studied the adoption of the practice. Such a study could shed light on the widespread use of credit checks, despite there being no clear evidence that they prevent workplace problems. Institutionalists argue that organizations often adopt practices not because they achieve some internal purpose but because they signal to outsiders that a firm conforms to social expectations (Meyer and Rowan, 1977; DiMaggio and Powell, 1983). The data in this study suggest that at least some firms operate in an institutional environment where credit reports signal to external stakeholders that employers have performed due diligence. In such a system, a shared belief that credit checks mitigate risk may be more important than any actual link between credit history and workplace behavior. If checking credit reports is in fact what Power (1999, p. 123) calls a ‘shallow ritual of verification,’ then we might be especially concerned about moral mismatch and other hiring off-ramps that leave people without jobs they otherwise deserve. Footnotes 1 While the federal Fair Credit Reporting Act lets employers look at the credit history of job applicants, since 2007, 11 states and some cities have passed laws limiting the practice. Lawmakers in dozens of other states and Congress have introduced legislation to do so (National Conference of State Legislatures, 2015). 2 The article does not identify the type of government organization studied, although two of the four co-authors work for the Federal Bureau of Prisons. 3 Some employers began using credit files much earlier. Stuart (2003) describes credit bureaus convincing the US Civil Service Commission to buy credit reports in the 1930s. These reports likely contained much more than the rationalized financial data we see today. 4 While credit bureaus discuss this connection qualitatively, they have not produced evidence of a mathematical correlation. In an Oregon State Senate hearing, one credit bureau representative said he knew of no such evidence (Martin, 2010). 5 Different cultural and legalistic outlooks seem to be responsible for employment credit checks rarely being used outside the USA, although they are used in the UK (Dowling, 2010; Mayer Brown, 2016). 6 I focused on the Boston area so that I could interview in person, which tends to produce better data (Weiss, 1994). I started with large and fast-growing firms since individuals who hire infrequently might have difficulty answering questions (K. Neckerman, personal communication). 7 This comports with survey data from SHRM. 8 A hiring professional at a bank explained that the Federal Deposit Insurance Corporation required hiring ‘financially responsible’ individuals, and that while she did not have to use credit reports to do this, they were the best way to ‘defend’ her decision-making. 9 Employers must get job candidates’ permission to pull credit reports, but employers may eliminate candidates who refuse and generally do. 10 Some respondents volunteered that it wouldn’t make sense to look at credit scores anyway, since scores can drop based on a person simply applying for more credit and other events unrelated to loan repayment. 11 Surveys from the Society for Human Resource Management (2012) find that employers’ decisions are most influenced by accounts in debt collection, although they do not address how hiring professionals use the information. 12 Only two respondents said they did not contact job candidates who failed written standards. This comports with surveys from the Society for Human Resource Management (2012), which find that about 80% of employers who review credit reports sometimes hire people despite financial problems. 13 Surrounding circumstances at times undermined the usefulness of credit reports altogether. A general manager at a hotel said that checking the credit of housekeepers made no sense, even though they had access to guest rooms and valuables, because the hotel did not pay them well. The manager assumed many employees would have bad credit from the difficulty of making ends meet on low pay (not moral failing). 14 Court cases send conflicting messages about the legality of private employers considering bankruptcy in hiring (Shepard, 2012). Some respondents said it was illegal to do so. 15 Some hiring professionals contacted all candidates with credit problems. Others said a candidate might not be given a chance to explain if it were easy to hire someone else. Few said they never ask for explanations. This pattern fits survey results from SHRM. 16 A Federal Trade Commission (2012) survey found 26% of Americans identified at least one error in a credit report. 17 In the one case where credit was a preliminary screen, the hiring professional had less personal flexibility to take into account extenuating circumstances, although the rules he applied reflected earlier moralization (e.g. medical accounts weren’t counted). 18 In a 2012 survey, SHRM found 58% of employers conduct credit checks after making a contingent job offer and 33% conduct them after an interview but before a job offer (n = 171). Figures from a 2010 survey were 57% and 30%, respectively (n = 199). 19 Some econometric papers suggest that credit checks may help African-Americans with good credit by making discriminatory employers more willing to hire them (Bartik and Nelson, 2016; Clifford and Shoag, 2016), but this leaves unaddressed how African-Americans with bad credit fare relative to other candidates with bad credit. Acknowledgements For helpful comments on earlier versions of this work, the author thanks Michel Anteby, Jason Beckfield, Bart Bonikowski, Bruce Carruthers, Barry Cohen, Frank Dobbin, Alexandra Feldberg, Roberto Fernandez, Adam Goldstein, Christopher Jencks, Carly Knight, Michèle Lamont, Rourke O’Brien, Devah Pager, Kim Pernell-Gallagher, Andrew Weaver and Christopher Winship, as well as participants of the Society for the Advancement of Socio-Economics’s 2014 annual meeting, the Harvard Inequality Seminar, the Harvard Sociology Qualifying Paper Seminar and the Law and Society Association’s 2015 annual meeting. The author especially thanks Jason Beckfield and Frank Dobbin for guidance throughout the project. Alexandra Feldberg graciously provided help creating the demonstration employment credit report. Funding This work was supported by the Center for American Political Studies at Harvard University. References Avery R., Calem P., Canner G. 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The art of deciding with data: evidence from how employers translate credit reports into hiring decisions

Socio-Economic Review , Volume Advance Article – Aug 30, 2017

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Abstract

Abstract About half of US employers consider personal credit history when hiring, a practice that connects individuals’ prospects for employment to their financial pasts. Yet little is known about how employers translate credit reports, complicated financial documents, into hiring decisions. Using interviews with 57 hiring professionals, this paper offers the first in-depth look at how employers move from document to decision. Faced with the context-free numbers of a credit report, and without predictively valid credit scores to fall back on, hiring professionals struggle to make sense of financial data without knowing the details of job candidates’ lives. They therefore reach beyond credit reports, both by inferring events that led to delinquent debt and by testing to see if candidates can offer morally redeeming accounts. A process of moral storytelling re-inflates credit reports with social meaning and prevents people with bad credit from getting jobs. This process carries implications for the reproduction of economic disadvantage since judgments about when it is and is not legitimate to have unpaid debt seem to at least partly depend on social background. 1. Introduction Consumer credit reports have spread far beyond lending institutions. In the USA, organizations consult credit reports when making decisions about whether to grant individuals access to a wide range of economic resources, including rental housing, insurance, utilities and—most controversially—jobs (Shepard, 2012; Traub, 2013).1 About half of American employers at least sometimes consider credit history in deciding whom to hire (Society for Human Resource Management, 2012), a practice that ties financial history to prospects for employment. Yet how hiring professionals make sense of credit reports remains a mystery (Bryan and Palmer, 2012). Different from credit scores, credit reports are long documents with detailed data about individual credit card accounts, mortgages, student loans, medical debts, money-related court judgments and more. Figure 1 shows the sort of report employers see. While economic sociologists and surveillance scholars have drawn attention to the increasing reach of credit records into everyday life (Marron, 2009; Fourcade and Healy, 2013; Roderick, 2014; Rona-Tas, 2017), this work generally assumes that the rationalized understandings and calculative technologies of financial firms, like risk-based credit scores, carry over to non-lending realms. This comports with organizations’ increasing reliance on metrics for making decisions (Espeland and Sauder, 2007; Espeland and Stevens, 2008; Timmermans and Epstein, 2010), although a lack of clear evidence about whether credit history mathematically predicts workplace behavior complicates this possibility (Weaver, 2015). By contrast, literature demonstrating the irrepressibly social and moral meaning of economic life (Zelizer, 1994, 2011, 2012; Fourcade and Healy, 2007; Wherry, 2008; Livne, 2014) opens the door for another possibility: that hiring professionals might draw on more traditional understandings of credit history as a mark of moral character (Carruthers, 2009; Graeber, 2012). Employers could quantify and compare job candidates or evaluate them morally as individuals, two very different mechanisms, with different implications, for linking credit data to hiring outcomes. Figure 1. View largeDownload slide View largeDownload slide Demonstration employment credit report. Note: This demonstration of the sort of document employers see is based on employment credit reports from background screening companies. These reports often include elaborate codes. I have spelled out categories here for ease of reading. Figure 1. View largeDownload slide View largeDownload slide Demonstration employment credit report. Note: This demonstration of the sort of document employers see is based on employment credit reports from background screening companies. These reports often include elaborate codes. I have spelled out categories here for ease of reading. Research on the aggregate effects of credit report use underscores the importance of understanding how credit history shapes employment outcomes. Credit reports can prevent people from getting jobs, and they can interact with other traits, such as race, to exacerbate or offset existing inequalities (Thorne, 2007; Maroto, 2012; Bartik and Nelson, 2016; Bos et al., 2016; Clifford and Shoag, 2016). Yet this largely quantitative literature stops short of exploring how employers move from raw credit-report data to decisions. Some 80% of employers that review credit reports sometimes hire people despite financial problems (Society for Human Resource Management, 2012). This suggests that the connection between credit history and hiring outcomes is contingent. What it is contingent on has been, until now, unclear. In this paper, I draw from interviews with 57 hiring professionals to study how employers translate credit reports into hiring decisions. I find that rather than simply use the standardized fields of credit reports to categorize and compare job candidates, hiring professionals selectively draw details from credit reports and turn to narrative in order to judge whether a person who has become delinquent on loans has acted immorally. In some cases, hiring professionals try to rely on the numerical fields of credit reports, but find themselves unable to do so fully. Faced with data about financial behavior but not social context, hiring professionals feel compelled to reach beyond the credit report, both by inferring the events that led to unpaid debt and by asking candidates to recount them. A process of moral storytelling connects credit history to hiring decisions. Job candidates at a disadvantage are those whose patterns of unpaid debt or explanations for lack of payment do not resonate with hiring professionals as morally legitimate, judgments that seem to at least partly hinge on social background. Whether a person with bad credit gets a job is contingent on a particular hiring professional’s understanding of the structural causes of bad credit, what counts as frivolous spending, types of debts that are sacred and must always be repaid, amounts of debt that are extreme and times in creditor–debtor relationships when it is okay to withhold payment. These findings demonstrate that even with robust data available, storytelling and moral meaning can remain indispensable links in deciding who gets what. Credit reports give hiring professionals copious data about job candidates’ financial pasts, but they do not explain the context in which financial events transpired, nor do they provide a score or other metric that mathematically links credit data to workplace outcomes. Hiring professionals therefore turn to narrative and moral judgment to move from document to hiring decision. Considering that social position shapes cultural know-how, like how to tell convincing stories (Polletta et al., 2011; Lareau, 2015), and understandings of morally upright behavior (Lamont, 1992; Hitlin and Vaisey, 2010), these findings carry important implications for the reproduction of economic disadvantage. 2. The systematization of credit history Given what we know about the sociology of credit, how might we expect hiring professionals to use credit reports? How a person repays borrowed money has long been seen as a moral matter, with timely repayment taken as a sign of trustworthiness (Carruthers, 2009; Graeber, 2012). Before credit bureaus circulated information about financial pasts, lenders relied on a potential borrower’s reputation within a community to decide whether he could be trusted with money (Carruthers and Espeland, 1998; Carruthers, 2005). Expectations of loan repayment and moral character have been so tightly tangled historically that when commercial credit agencies first arose in the USA in the mid-19th century, they tracked not only whether companies repaid borrowed funds, but also the details of proprietors’ lives, such as if they were hard-working and well-liked, or prone to drinking, gambling and philandering (Sandage, 2005; Lauer, 2008). Over time, commercial credit agencies, and the consumer credit bureaus that followed, worked to rationalize the presentation and interpretation of the information they gathered (Lauer, 2010). As agencies and bureaus created set categories for recording the financial behavior of companies and people, they funneled information into a form well-suited to mathematical models of probability and risk, moving away from individualized, character-based understandings of credit history (Guseva and Rona-Tas, 2001; Marron, 2009; cf. Moulton, 2007). In the late 19th century, commercial agencies developed ordinal credit ratings (Cohen, 2012; Carruthers, 2013), and in the early 20th century, consumer credit bureaus pioneered points systems for predicting who would repay (Gandy, 1993). In the 1950s, the company Fair Isaac (FICO) began to convince banks and retailers to extend consumer credit based on ‘scorecards,’ the predecessor of the FICO credit score (Poon, 2007). Calculation replaced moral judgment as users of credit history learned to sort and rank individuals into risk pools which predicted the likelihood of repayment. Lenders ostensibly no longer cared whether borrowers were trustworthy in any generalizable sense, only if their credit scores were high or low (Leyshon and Thrift, 1999; Marron, 2007). A crucial step along the way was taking the messy details of individual instances of borrowing, repayment and default, and fitting them into standard, de-contextualized categories of data, such as those that appear on a credit report. Hirschman et al. (2016) describe quantification as a multi-stage process, one that includes categorization, classification and commensuration, or the ‘transformation of different qualities into a common metric’ (Espeland and Stevens, 1998, p. 314). Fourcade and Healy (2017, p. 289) write that the ‘composite device’ of a credit score ‘depend[s] in the first instance on choices about the way credit events are defined and measured.’ A loan payment that is 90 days late because a person lost his job and a loan payment that is 90 days late because a person has a lackadaisical attitude toward financial obligations look exactly the same on a credit report, even though they might be qualitatively different in important ways. Although sociologists have yet to empirically study the use of credit reports in hiring, some have theorized about the migration of credit history into non-lending realms. These scholars evoke rationalized and de-personalized modes of sense-making similar to that deployed by lenders, referring to ‘scoring’(Fourcade and Healy, 2013), ‘rating’(Rona-Tas, 2017) and other computational processes. Indeed, in recent decades, ratings, rankings and formalized standards have spread throughout organizations of all types as accepted—even expected—modes of analysis and decision-making (Espeland and Sauder, 2007; Espeland and Stevens, 2008; Timmermans and Epstein, 2010). Such practices are often sold on the pragmatic promise of more dispassionate decision-making and coordination of activity, though they also can serve to signal to stakeholders that an organization is behaving legitimately, whether or not there is any material benefit (Power, 2007; Timmermans and Epstein, 2010). In hiring, the rigidity of bureaucratic rules and standards is at times celebrated for its ability to remove personal discretion from decisions and the bias it can introduce (Porter, 1995; Bielby, 2000). Following from these bodies of work, one possibility for how employers use credit history is that they adopt the lending industry’s practices of commensuration and comparison in order to take the information in a credit report and make predictions about the behavior of would-be employees. In such an approach to credit data, decisions would be made according to explicit standards or rules. Information about people would be reduced to forms—such as hierarchical categories or scores—that made it easy to consistently decide which individuals are ‘better’ than others by abstracting away from the particularities of individual situations. One complication to this possibility is that existing research provides few convincing correlations between personal financial data and employee behavior. Using nationally representative panel data, Weaver (2015) finds no link between credit outcomes, such as being late on a mortgage payment or rejected for credit, and productivity. Bryan and Palmer (2012) study a randomly selected sample of employees at a financial services firm and find no consistent relationship between credit report data—late payments, debts in collection and charge-offs—and job performance or termination. Drawing from a self-selected, non-random sample of university students, employees and alumni, Bernerth et al. (2012) find no correlation between credit scores and supervisors’ reports of bad workplace behavior, although the authors find a relationship between credit scores and supervisors’ assessment of task performance and ‘organizational citizenship,’ such as helping co-workers. Oppler et al. (2008) use a random sample of employees from a government agency, and find a correlation between self-reported loan delinquency and bankruptcy on the one hand, and ‘counterproductive work behaviors,’ such as failure to pay debts, misuse of credit cards and theft, on the other hand.2 It is difficult, though, to know what to make of this correlation, since certain independent and dependent variables are so similar (e.g. loan delinquency is an explanation while failure to pay debts is an outcome), and the authors note a surprisingly high rate of counterproductive work behaviors, casting doubt on generalizability. A lack of established predictive validity has not, however, impeded employer use of credit reports. This is ironic considering that credit reports were marketed and adopted after a 1988 law made it harder for employers to use polygraph tests because of validity concerns (Fuchsberg, 1990; Rona-Tas, 2017).3 Credit bureaus and background screening outfits portray credit reports as a way to identify which job candidates are likely to steal and otherwise be dishonest.4 This is reflected in employers’ explanations of why they use the reports, the best evidence of which comes from a set of low-response-rate surveys from the Society for Human Resource Management (SHRM), a trade group. In those surveys, the most chosen reason for using credit reports is ‘to reduce/prevent theft and embezzlement, other criminal activity.’ Another top reason is ‘to assess the overall trustworthiness of the job candidate’ (Society for Human Resource Management, 2012). Employers equate bad credit with an untrustworthy, irresponsible disposition. The strength of that belief may lend authority to credit report data, even without documented mathematical correlation. Predictive validity may also be beside the point if credit reports serve a different function. In the SHRM surveys, another oft-chosen reason for checking credit is ‘to reduce legal liability for negligent hiring,’ an outward-facing rationale that mimics a function of commercial credit ratings, which were used to signal sound decision-making long before there was evidence of their predictive validity (Carruthers, 2013). In hiring, what may matter more than a proven correlation is a broadly shared perception that credit reports are a sensible, defensible screening mechanism—perhaps tied to credit bureaus’ institutional role as arbiters of consumer financial standing. As in Power’s (1999) ‘audit society,’ certifying workers as safe by checking credit reports may serve as a ‘ritual of verification,’ a way to establish institutional legitimacy, regardless of the effect checking has on actual workplace outcomes.5 3. The enduring morality of economic life Even in the absence of predictive validity, employers could still approach credit reports with a calculative mindset, commensurating and comparing in unvalidated ways. Yet other sociological works suggest a second option: that hiring professionals might instead cling to traditional and personalized understandings of the particular ways in which credit history marks moral character. In their effort to rationalize lending decisions, credit bureaus may have formally stripped financial information of rich social context, but sociologists have shown time and again that money and markets carry moral valence and social meaning, even when they seem to be commodified (Zelizer, 1994, 2012; Fourcade and Healy, 2007; Wherry, 2012). Moral weight and social meaning can come from the form money takes (Zelizer, 1994), its source (Sykes et al., 2015) or the nature of relationships between economic actors (Zelizer, 2012). A credit report shows the timeliness of loan repayments, but also reveals which firms people have borrowed from, what they have borrowed for (as with a car loan) and the debts they prioritize repaying on time (Avery et al., 2003). Hiring professionals might use any of these details in determining what a credit report says about a job candidates’ moral standing. Indeed, Americans often distinguish between ‘good debt,’ like borrowing to buy a home, and ‘bad debt,’ like carrying a balance on a credit card (Kalousova and Burgard, 2013; Wherry, 2016). Sometimes people see debt as a self-empowering investment (Dwyer et al., 2012) and at other times as a shameful liability (Hodson et al., 2014). Even among delinquent debts there is room for variation, with context like the type of debt and nature of the creditor relationship affecting how people morally locate the act of non-payment. Studying debt collectors at work, Polletta and Tufail (2014) find that borrowers want to be absolved of debts they see as unfair, but often refuse to pay less than they owe on those tied to symbolically worthy spending, such as education or to providers of personally meaningful services, such as doctors—reflecting Zelizer’s (1989, 1994) foundational insight that people use money to mark the meaning of relationships. Similarly, Tach and Greene (2014) find that financially strapped borrowers have an easier time defaulting when they feel they were misled about the terms of a loan or treated poorly by a lender, but insist on paying debts that re-inforce a positive identity, like being upwardly mobile. From an employer’s perspective, how a person spends money could also be telling. Sociologists have long observed that spending patterns serve to designate social boundaries (Veblen, [1899] 1973; Bourdieu, 1984). Wherry (2008) extends these observations and theorizes that consumption does not simply categorize people, but also identifies them in morally imbued ways that rest partly on a person’s social position. Whether a purchase is seen as, say, foolish, frugal or frivolous, depends both on the price paid and the identity of the purchaser. When the US mortgage crisis of the late 2000s primarily affected working-class racial minorities, loan defaults were attributed to foolish borrowing, but once problems spread to richer white homeowners, there emerged a national narrative to lay blame on structural financial forces over which individuals had no control (Harvey, 2010; Wherry, 2012; on the foreclosure crisis and blame, also see McCormack, 2014). In the same vein, Hurst (2012) finds that bankruptcy judges treat debtors differently based on their class standing: those with advanced degrees are lectured to be ambitious in their careers to make more money, while those with no college education are admonished to spend less and have humbler life expectations. A very different line of research—about how organizations select individuals—also offers reason to think that employers might forgo calculative approaches to credit reports in favor of more personalized and culturally situated modes of understanding financial pasts. Many of the ways hiring professionals pick employees (e.g. with interviews) rest on mathematically unvalidated beliefs about how to identify good candidates (Dana et al., 2013). At times employers impose bureaucratic rules, tests and other standards to select workers more rationally, although even when rules guide action, decision-makers often re-introduce discretion, either intentionally or as a result of unconscious in-group bias (Gorman, 2005; Castilla, 2011; Dobbin et al., 2015). Having gone to a similar university as a job candidate, for example, can lead a hiring professional to especially value that candidate’s degree (Rivera, 2012). Employers (and other gatekeepers, like college admissions officers) often understand information about individuals through the dual prisms of culture and personal experience, harnessing tools such as narratives, symbols and emotional reactions to decide whether a person belongs (Moss and Tilly, 2001; Stevens, 2007; Rivera, 2015a). Even bank employees sometimes use subjective impressions of loan applicants and the stories they tell in deciding whether to lend money (Moulton, 2007). Therefore, a second possibility for how hiring professionals use credit reports is that they avoid de-personalized, calculative processes and instead make sense of credit history by assigning social and moral meaning to individual situations of borrowing and default. In this approach to credit data, decisions would be made by focusing on the particularities of the person at hand and according to employers’ understandings of acceptable and unacceptable behavior. Information about candidates would be understood through forms, such as stories, that leave substantial room for discretion and exception. In translating raw information into meaning, hiring professionals would draw on their own experiences, beliefs and moral distinctions. 4. Data and methods To understand how employers use credit history in hiring decisions, I conducted in-depth interviews with individuals who hire, including recruiters, human resources officers and corporate managers. Interviews are well-suited for understanding how people interpret the world around them and take actions in line with those interpretations (Weiss, 1994). Interviews with employers show that the way hiring professionals assign meaning to information can significantly shape hiring decisions (Kirschenman and Neckerman, 1991; Miller and Rosenbaum, 1997; Moss and Tilly, 2001; Rivera, 2012). Since this is the first research to examine how employers use credit reports, I used purposive sampling to adjust the mix of respondents as I learned about the phenomenon under study (Glaser and Strauss, 2007; Charmaz, 2012). From January to December 2014, I interviewed 57 hiring professionals, including 21 who evaluate credit reports and 17 who work alongside those who do. Hiring is typically a collaborative process (Rivera, 2015b), and different individuals often conduct interviews and pull information from administrative records like credit reports. The diversity of the sample allows me to understand this process as a whole. The remaining 19 respondents either discussed organizational policies regarding credit reports, work at background screening companies that sell credit reports to employers and have insight into the practices of many firms, or, in one case, advise companies as an employment lawyer. Five of these respondents discussed why their companies do not use credit reports, which helped put into analytical relief the arguments of those whose companies do. About half of respondents were female. The strength of purposive sampling is its ability to establish the contours of an unfamiliar process, but it does not produce a representative set of cases, and I cannot estimate frequencies in a larger population with the data. In addition to theoretical motivations, a key practical concern drove how I gathered respondents: there is no extant list of employers that use credit reports. I began with a list of large (Fortune 1000) and fast-growing (Inc. 5000) firms in the Boston area, and approached those likely to use credit reports.6 I assessed this by identifying industries described in the press as common users of credit reports in hiring, by searching for online job ads mentioning credit checks and by reading web sites where job seekers discuss applying for jobs at specific companies. As I combed these sources for information about companies on the list, I added other firms, including some outside the Boston area. I also searched the networking site LinkedIn.com for hiring professionals who listed experience evaluating credit reports. I contacted potential respondents either by phone, by email, through LinkedIn or in person at a firm’s headquarters or store. Individuals who hire tend to be busy professionals, and it often took multiple efforts at making contact—including showing up at places of employment—to get respondents to agree to interviews. Whenever possible, I asked an acquaintance for an introduction to increase the quality of the data, since hiring professionals are less likely to frankly share information with a stranger (Bewley, 1999). I presented myself as a researcher working on a study of how companies hire. Interviews covered the entire hiring process. About two-thirds of respondents brought up credit checks on their own. I recorded the interviews, which typically lasted between 1 and 2.5 h. To analyze the data, I began with a two-part memoing process (Miles and Huberman, 1994; Lofland et al., 2006). After each interview, I wrote a memo to capture salient aspects of the interview and to identify potentially meaningful concepts and practices. As I accumulated data, I began a second set of memos, to develop themes that cut across interviews. I imported transcripts into the software program MAXQDA to more formally assign codes and test ideas I developed inductively. To make theoretical sense of observed patterns, I returned to the literature, and then iterated between the data and extant research. The respondents I interviewed hire for many sorts of positions. The jobs I heard about included bank teller, hardware store manager, accountant, mortgage loan originator, window salesman, parking-lot attendant, computer software tester, fast-food restaurant assistant manager, call-center operator, bookkeeper, corporate vice president, executive chef, apartment handyman and human resources assistant. Table 1 presents a list of respondents’ titles and industries, as well as examples of jobs discussed. These jobs represent a mix of professional and non-professional positions, although the sampling strategy could have missed jobs for which hiring professionals evaluate credit history differently from the ways I present below, a limitation I discuss at the end of this paper. A few hiring professionals pulled credit reports for all job positions (largely to avoid claims of discrimination), but in most companies only certain positions required credit checks, typically those involving access to money, financial records or sensitive information, or which carried managerial or fiduciary responsibility.7 Table 1 List of respondents No.  Industry or sector  Respondent’s title†  Example of job discussed‡  1  Manufacturing  Human resources manager  Human resources administrator  2  Financial services  Employment officer  Bank teller  3  Real estate  Senior vice president  Property manager  4  Staffing  Recruiter  Accountant  5  Financial services  Recruiting administrator  Mortgage loan officer  6  Staffing  Managing director  Administrative assistant  7  Higher education  Recruitment consultant  Chief technology officer  8  Retail  Store manager  Cashier  9  Manufacturing  Senior corporate recruiter  Salesperson  10  Technology  Staffing lead  Software development engineer  11  Real estate  Property manager  Maintenance worker  12  Higher education  Vice president  College president  13  Retail  Human resources manager  Buyer  14  Retail  Recruiter  Database administrator  15  Staffing  Recruitment specialist  Receptionist  16  Staffing  Sourcing specialist  Administrative assistant  17  Financial services  Recruiter  Assistant branch manager  18  Financial services  Human resources manager  Benefits and payroll specialist  19  Retail  Human resources officer  Sales associate  20  Retail  Corporate recruiter  Store manager  21  Retail  Human resources officer  Sales associate  22  Staffing  Recruiter  Forklift operator  23  Financial services and technology  Senior technical recruiter  Software tester  24  Manufacturing  Human resources assistant  Salesperson  25  Financial services  Human resources manager  Loan officer  26  Financial services  Corporate recruiter  Loan officer  27  Staffing  Account recruiting manager  Production tech  28  Hotels  General manager  Director of sales  29  Financial services  Senior vice president, human resources  Mortgage loan originator  30  Retail  Director of human resources  Accountant  31  Technology management  Director of staffing  Software engineer  32  Retail  Corporate recruiting manager  Store manager  33  Retail  Human resources generalist  Baker  34  Government  Human resources staffing manager  Financial examiner  35  Manufacturing  Human resources manager  Accountant  36  Financial services  Vice president, administration  Bank teller  37  Hotels  General manager  Housekeeper  38  Technology  Human resources manager  Warehouse worker  39  Non-profit  Employment coordinator  Budget manager  40  Real estate  Senior vice president, human resources  Sales consultant  41  Staffing  Account manager  Scientist  42  Retail  Owner  Assistant store manager  43  Higher education  Assistant director of employment  Dean  44  Information technology  Hiring manager  Call center operator  45  Information management  Director of executive recruiting  Head of revenue management  46  Information management  Senior recruiter  Security analyst  47  Food services  Recruiter  Executive chef  48  Financial services and technology  Human resources director  Client services representative  49  Food services  Director of human resources  Assistant restaurant manager  50  Financial services  Human resources officer  Bank teller  51  Food services  Restaurant manager  Cashier  52  Insurance  Senior recruiting specialist  Call center operator  53  Background screening  Strategy manager  Mortgage loan originator  54  Background screening  Chief marketing officer  Bank teller  55  Background screening  Vice president of marketing  Truck driver  56  Background screening  Owner  Call center operator  57  Legal services  Employment lawyer  Corporate executive  No.  Industry or sector  Respondent’s title†  Example of job discussed‡  1  Manufacturing  Human resources manager  Human resources administrator  2  Financial services  Employment officer  Bank teller  3  Real estate  Senior vice president  Property manager  4  Staffing  Recruiter  Accountant  5  Financial services  Recruiting administrator  Mortgage loan officer  6  Staffing  Managing director  Administrative assistant  7  Higher education  Recruitment consultant  Chief technology officer  8  Retail  Store manager  Cashier  9  Manufacturing  Senior corporate recruiter  Salesperson  10  Technology  Staffing lead  Software development engineer  11  Real estate  Property manager  Maintenance worker  12  Higher education  Vice president  College president  13  Retail  Human resources manager  Buyer  14  Retail  Recruiter  Database administrator  15  Staffing  Recruitment specialist  Receptionist  16  Staffing  Sourcing specialist  Administrative assistant  17  Financial services  Recruiter  Assistant branch manager  18  Financial services  Human resources manager  Benefits and payroll specialist  19  Retail  Human resources officer  Sales associate  20  Retail  Corporate recruiter  Store manager  21  Retail  Human resources officer  Sales associate  22  Staffing  Recruiter  Forklift operator  23  Financial services and technology  Senior technical recruiter  Software tester  24  Manufacturing  Human resources assistant  Salesperson  25  Financial services  Human resources manager  Loan officer  26  Financial services  Corporate recruiter  Loan officer  27  Staffing  Account recruiting manager  Production tech  28  Hotels  General manager  Director of sales  29  Financial services  Senior vice president, human resources  Mortgage loan originator  30  Retail  Director of human resources  Accountant  31  Technology management  Director of staffing  Software engineer  32  Retail  Corporate recruiting manager  Store manager  33  Retail  Human resources generalist  Baker  34  Government  Human resources staffing manager  Financial examiner  35  Manufacturing  Human resources manager  Accountant  36  Financial services  Vice president, administration  Bank teller  37  Hotels  General manager  Housekeeper  38  Technology  Human resources manager  Warehouse worker  39  Non-profit  Employment coordinator  Budget manager  40  Real estate  Senior vice president, human resources  Sales consultant  41  Staffing  Account manager  Scientist  42  Retail  Owner  Assistant store manager  43  Higher education  Assistant director of employment  Dean  44  Information technology  Hiring manager  Call center operator  45  Information management  Director of executive recruiting  Head of revenue management  46  Information management  Senior recruiter  Security analyst  47  Food services  Recruiter  Executive chef  48  Financial services and technology  Human resources director  Client services representative  49  Food services  Director of human resources  Assistant restaurant manager  50  Financial services  Human resources officer  Bank teller  51  Food services  Restaurant manager  Cashier  52  Insurance  Senior recruiting specialist  Call center operator  53  Background screening  Strategy manager  Mortgage loan originator  54  Background screening  Chief marketing officer  Bank teller  55  Background screening  Vice president of marketing  Truck driver  56  Background screening  Owner  Call center operator  57  Legal services  Employment lawyer  Corporate executive  † I have slightly changed some respondents’ job titles to preserve confidentiality. ‡ Some respondents, whose companies do not use credit reports in hiring, discussed why positions do not require credit checks. Table 1 List of respondents No.  Industry or sector  Respondent’s title†  Example of job discussed‡  1  Manufacturing  Human resources manager  Human resources administrator  2  Financial services  Employment officer  Bank teller  3  Real estate  Senior vice president  Property manager  4  Staffing  Recruiter  Accountant  5  Financial services  Recruiting administrator  Mortgage loan officer  6  Staffing  Managing director  Administrative assistant  7  Higher education  Recruitment consultant  Chief technology officer  8  Retail  Store manager  Cashier  9  Manufacturing  Senior corporate recruiter  Salesperson  10  Technology  Staffing lead  Software development engineer  11  Real estate  Property manager  Maintenance worker  12  Higher education  Vice president  College president  13  Retail  Human resources manager  Buyer  14  Retail  Recruiter  Database administrator  15  Staffing  Recruitment specialist  Receptionist  16  Staffing  Sourcing specialist  Administrative assistant  17  Financial services  Recruiter  Assistant branch manager  18  Financial services  Human resources manager  Benefits and payroll specialist  19  Retail  Human resources officer  Sales associate  20  Retail  Corporate recruiter  Store manager  21  Retail  Human resources officer  Sales associate  22  Staffing  Recruiter  Forklift operator  23  Financial services and technology  Senior technical recruiter  Software tester  24  Manufacturing  Human resources assistant  Salesperson  25  Financial services  Human resources manager  Loan officer  26  Financial services  Corporate recruiter  Loan officer  27  Staffing  Account recruiting manager  Production tech  28  Hotels  General manager  Director of sales  29  Financial services  Senior vice president, human resources  Mortgage loan originator  30  Retail  Director of human resources  Accountant  31  Technology management  Director of staffing  Software engineer  32  Retail  Corporate recruiting manager  Store manager  33  Retail  Human resources generalist  Baker  34  Government  Human resources staffing manager  Financial examiner  35  Manufacturing  Human resources manager  Accountant  36  Financial services  Vice president, administration  Bank teller  37  Hotels  General manager  Housekeeper  38  Technology  Human resources manager  Warehouse worker  39  Non-profit  Employment coordinator  Budget manager  40  Real estate  Senior vice president, human resources  Sales consultant  41  Staffing  Account manager  Scientist  42  Retail  Owner  Assistant store manager  43  Higher education  Assistant director of employment  Dean  44  Information technology  Hiring manager  Call center operator  45  Information management  Director of executive recruiting  Head of revenue management  46  Information management  Senior recruiter  Security analyst  47  Food services  Recruiter  Executive chef  48  Financial services and technology  Human resources director  Client services representative  49  Food services  Director of human resources  Assistant restaurant manager  50  Financial services  Human resources officer  Bank teller  51  Food services  Restaurant manager  Cashier  52  Insurance  Senior recruiting specialist  Call center operator  53  Background screening  Strategy manager  Mortgage loan originator  54  Background screening  Chief marketing officer  Bank teller  55  Background screening  Vice president of marketing  Truck driver  56  Background screening  Owner  Call center operator  57  Legal services  Employment lawyer  Corporate executive  No.  Industry or sector  Respondent’s title†  Example of job discussed‡  1  Manufacturing  Human resources manager  Human resources administrator  2  Financial services  Employment officer  Bank teller  3  Real estate  Senior vice president  Property manager  4  Staffing  Recruiter  Accountant  5  Financial services  Recruiting administrator  Mortgage loan officer  6  Staffing  Managing director  Administrative assistant  7  Higher education  Recruitment consultant  Chief technology officer  8  Retail  Store manager  Cashier  9  Manufacturing  Senior corporate recruiter  Salesperson  10  Technology  Staffing lead  Software development engineer  11  Real estate  Property manager  Maintenance worker  12  Higher education  Vice president  College president  13  Retail  Human resources manager  Buyer  14  Retail  Recruiter  Database administrator  15  Staffing  Recruitment specialist  Receptionist  16  Staffing  Sourcing specialist  Administrative assistant  17  Financial services  Recruiter  Assistant branch manager  18  Financial services  Human resources manager  Benefits and payroll specialist  19  Retail  Human resources officer  Sales associate  20  Retail  Corporate recruiter  Store manager  21  Retail  Human resources officer  Sales associate  22  Staffing  Recruiter  Forklift operator  23  Financial services and technology  Senior technical recruiter  Software tester  24  Manufacturing  Human resources assistant  Salesperson  25  Financial services  Human resources manager  Loan officer  26  Financial services  Corporate recruiter  Loan officer  27  Staffing  Account recruiting manager  Production tech  28  Hotels  General manager  Director of sales  29  Financial services  Senior vice president, human resources  Mortgage loan originator  30  Retail  Director of human resources  Accountant  31  Technology management  Director of staffing  Software engineer  32  Retail  Corporate recruiting manager  Store manager  33  Retail  Human resources generalist  Baker  34  Government  Human resources staffing manager  Financial examiner  35  Manufacturing  Human resources manager  Accountant  36  Financial services  Vice president, administration  Bank teller  37  Hotels  General manager  Housekeeper  38  Technology  Human resources manager  Warehouse worker  39  Non-profit  Employment coordinator  Budget manager  40  Real estate  Senior vice president, human resources  Sales consultant  41  Staffing  Account manager  Scientist  42  Retail  Owner  Assistant store manager  43  Higher education  Assistant director of employment  Dean  44  Information technology  Hiring manager  Call center operator  45  Information management  Director of executive recruiting  Head of revenue management  46  Information management  Senior recruiter  Security analyst  47  Food services  Recruiter  Executive chef  48  Financial services and technology  Human resources director  Client services representative  49  Food services  Director of human resources  Assistant restaurant manager  50  Financial services  Human resources officer  Bank teller  51  Food services  Restaurant manager  Cashier  52  Insurance  Senior recruiting specialist  Call center operator  53  Background screening  Strategy manager  Mortgage loan originator  54  Background screening  Chief marketing officer  Bank teller  55  Background screening  Vice president of marketing  Truck driver  56  Background screening  Owner  Call center operator  57  Legal services  Employment lawyer  Corporate executive  † I have slightly changed some respondents’ job titles to preserve confidentiality. ‡ Some respondents, whose companies do not use credit reports in hiring, discussed why positions do not require credit checks. 5. Findings and analysis 5.1 Approaching the credit report Respondents in the current sample overwhelmingly thought credit reports could offer insight into a job candidate’s moral character, and, relatedly, the candidate’s likelihood of stealing or otherwise misbehaving on the job, especially with respect to money. Respondents who did not consult credit reports said either that they had other ways of assessing applicants’ trustworthiness and money-savvy, or that they did not believe credit history was a clear indicator of character. Some respondents, especially those in heavily regulated industries like financial services, also gave a second reason for considering credit reports: to demonstrate to others, including regulators, investors and business partners, that they had done due diligence in vetting candidates. Credit reports thus functioned as an institutionally legible mode of establishing trustworthiness.8 Employers, both in this sample and in general, pay background screening companies or credit bureaus to pull credit reports.9 As demonstrated in Figure 1, the credit reports hiring professionals receive are long and complicated, showing which accounts are late, in collections, and written off as uncollectable, as well as any instances of bankruptcy, foreclosure, tax lien, wage garnishment, repossession, unpaid child support or money-related civil judgments. Credit reports present an array of numerical fields, though they fall short of the full quantification of a credit score. None of the respondents in my sample received credit scores, since it is standard practice in the background screening industry not to sell them to employers (Clemans, 2013).10 When I asked respondents how they learned to interpret credit reports, nearly all said they picked it up on the job, sometimes with advice from colleagues, but typically not. Other respondents cited the importance of ‘judgment’ and ‘professional experience.’ Six respondents said they took guidance from background screening companies or the trade group SHRM, but this advice was about when to run a credit check (e.g. if a job involved handling money) or how to legally deny a person a job because of one. No respondent gave an example of a professional or educational resource for learning how to look at a credit report and decide whether a candidate should be hired. A manager at a background screening company said employers would like such advice, but companies do not offer it for fear of legal liability, a fear related to the fact that there is no clear evidence that credit reports accurately predict workplace behavior. Hiring professionals therefore had to decide for themselves how to translate credit history into hiring decisions. About one-fifth of respondents (n = 12) described sets of rules to guide the use of data from a credit report, often in the form of a table or ‘matrix,’ to indicate, for example, the permissible number of debts in collection. These guidelines helped take complicated credit reports and distill them into a decision by relying on the processes of categorization and comparison. That is to say, at least some hiring professionals started out with a calculative (though not fully quantitative) approach, designing ways of sorting and ranking candidates based on credit history in order to hold each person to the same standards. In the absence of known mathematical correlations between parts of a credit report and workplace outcomes, hiring professionals created rules and standards by mobilizing intuitions about what qualified as a ‘bad’ credit record. For example, a human resources manager at one bank described a rule that the bank should not hire a candidate if he or she was behind on more than $20 000 in loans. A group of bank employees arrived at that figure based on what they had seen on job–candidate credit reports in the past and discussions about what they each thought was ‘reasonable.’ By contrast, employees at another bank contemplated, but ultimately rejected, having a dollar threshold. Instead, hiring professionals there decided that being delinquent on three or more accounts, no matter the amount, was a more important indicator of a problem. Even among firms in the same industry, rules varied greatly. This variation in standard-setting was enabled by employers generally not thinking about credit data in the probabilistic way that most lenders do. Only one respondent claimed there was a mathematical correlation between personal financial distress and bad workplace behavior (specifically, stealing customers’ credit card information). One other respondent, a vice president of human resources at a property management company, said that credit–workplace correlations had never been established, which was why his company did not run credit checks. Instead of invoking correlations, all other respondents, including those using written standards, explained the value of credit reports by deploying logical arguments rooted in lay beliefs about human behavior. As the owner of a string of hardware stores said: ‘If you can loan somebody money and they meet their obligations, then they are probably going to meet their obligations to you as a responsible employee.’ Employers assumed that people typically behave the same way from one situation to the next (absent exceptional circumstances), and so behaving well as a borrower would likely translate into behaving well as a worker. Because respondents relied on generalized beliefs about how people behave, they understood the connection between credit history and workplace behavior holistically. Broad patterns in a credit report were imagined to go with broad patterns of behavior at work. Therefore, when it came time to operationalize the relationship for the purposes of making hiring decisions, respondents came up with a wide array of ideas about what to pay attention to. While most respondents emphasized that they were looking for ‘red flags,’ such as ‘patterns’ of unpaid loans, especially those that were long past due or in collections, there were often sharp differences in the specifics, as I explore in detail below.11 Surprisingly, then, even though hiring professionals found much to pay attention to in credit reports, they consistently reached for more. Time and again, they sought out stories to contextualize loan non-repayment and other behavior. Credit reports contain data points stripped of personal and social context, such as a person’s attitude toward loan repayment and the presence of life events like divorce and job loss. Without fail, hiring professionals saw these details as crucial for understanding what a bad credit report meant. This was true both for hiring professionals with sets of rules to follow and for those without written guidelines. All respondents gave examples of times when it might be forgivable to default on debts. Making sense of a credit report therefore posed a great challenge: to tell if this was one of those times. 5.2 The practice of moral storytelling To address that challenge, hiring professionals turned to storytelling. They did this in two ways. First, they inferred stories about a person’s life from the credit report. Second, they contacted job candidates to see if they could tell a story about their financial problems in a redeeming way. I call this process moral storytelling. It is moral in the sense that it rests on conceptions of right and wrong which dictate how one ought to behave and feel (Smith, 2009), and it is storytelling in that it is structured around real or imagined sequences of events and associated mental states (Bruner, 1990). The goal of moral storytelling was to tease apart whether credit report problems were the result of the person or the situation (Ross and Nisbett, 1991). That is to say, whether a bad credit report indicated a job candidate had a poor moral character and would likely act in bad faith again (the person), or if given different circumstances, one might reasonably expect the candidate to act differently (the situation). Even employers who began with written standards almost invariably turned to moral storytelling by the end of the decision-making process by calling job candidates who failed to meet standards and asking them to explain their credit problems.12 This suggests that when the use of data rests on common-sense assumptions rather than mathematically validated empirics, the system can only take a decision-maker so far. Unlike lenders, hiring professionals do not have outside evidence that particular combinations of delinquencies accurately predict future behavior. The context-free data that appear on a credit report therefore did not tell hiring professionals everything they needed to know. To make a decision, they also sought to understand why the numbers looked as they did. The first way hiring professionals did this was to make inferences about the events in a job candidate’s life based on the types and amounts of unpaid debts present on a credit report. If hiring professionals understood non-payment as resulting from an unintentional or justified sequence of events, then they assigned blame to the situation and concluded that the candidate could be hired. Hiring professionals did this by mobilizing stories about the structural causes of credit problems, and by imagining events in a debtor–creditor relationship such that the creditor was seen as a rightful non-payer. By contrast, if hiring professionals understood non-payment as resulting from an intentional or unjustified sequence of events, then they attributed blame to the person and eliminated the candidate from contention for the job. Hiring professionals did this by understanding certain debts as sacred and particularly bad to default on, by attributing moral weight to the amount of outstanding debt, and by casting certain sorts of spending as frivolous. One frequently cited indication that the situation, not the job candidate, was to blame was if the debt had to do with medical treatment. Medical debt is not always labeled as such on a credit report, but when it was, employers generally assumed that people had been overwhelmed by unexpected but necessary bills; that the structure of the US healthcare system frequently left people with large debts they could not legitimately be expected to quickly pay off in full. Some respondents also imagined job candidates with unpaid medical debt as rightful non-payers. As a recruiter at a window manufacturer explained: ‘We typically won’t hold [delinquent medical debt] against you. You might just be working it out with your healthcare, trying to get insurance to pay.’ The moral ambiguity of unpaid medical debt was so great that one company taking a calculative approach baked it into the rules. A recruiter for a financial data firm said that while company policy was not to hire anyone with more than two accounts in default, medical debts did not count toward the total. Other sorts of unpaid debt could also lead hiring professionals to mobilize structural and rightful non-payer explanations in order to attribute blame to the situation. A vice president of human resources at a bank explained that she was less judgmental of credit problems if a student loan appeared on the report, relying on a structural account of non-payment: ‘You go to school and you come out and owe $80 000 and you’re making $12 an hour. How can you pay those loans back when the interest rates are so high?… It’s really hard to find a job nowadays.’ Meanwhile, the owner of a background check outfit showed empathy for individuals with debts at electronics retailers like Best Buy by imagining wronged consumers as rightful non-payers, explaining: ‘Some people will buy a TV and it will break and they’re like I’m not paying it… Okay, they’re angry, they bought something, it broke.’13 At other times, clues on a credit report led hiring professionals to assign blame for unpaid debt to the person rather that to the situation. Even in the face of situational disadvantage, some types of financial obligations remained sacred in the eyes of hiring professionals, which made non-payment a reason to reject a job candidate. At one fast-food company, the director of human resources said that after the most recent recession she grew more sympathetic about late credit card payments, but that she would still never hire a job candidate who was behind on child support, since providing for one’s children should always be a top priority: ‘If you don’t have a job, get a job, you know? Figure it out, take care of your child.’ Forms of extreme non-payment, such as unpaid debts worth hundreds of thousands of dollars, also often eliminated job candidates, underscoring Livne’s (2014) point that quantities, not just categories, of money can be moralized. At times bankruptcy also registered as a form of extreme non-payment. A hiring professional at one bank explained why declaring bankruptcy was so bad, saying: ‘Bankruptcy means you’re walking into this court and saying I’m not going to pay any of these bills, forgive them for me… People are looking for the easy way out.’14 Finally, hiring professionals assigned blame for unpaid debt to the person rather than the situation when they viewed the spending that led to the debt as frivolous. This they inferred from the merchant associated with the account or card; for example, a cell-phone company or lingerie store. The head of human resources at the fast-food company was particularly critical when a candidate defaulted on a credit card issued by an electronics retailer: ‘If it was like a Walmart thing, I can say okay at Walmart you can buy food, you can buy clothes. If it’s a Best Buy thing, I mean do you really need electronics?’ The difference between this reaction and that of the background check outfit owner above is a theoretically suggestive one I explore in the next section. Strikingly, the practice of storytelling surfaced time and again as hiring professionals described interpreting specific credit reports. Respondents practically always recounted their thinking as narrative. An explanation of why bankruptcy was so bad involved envisioning a person walking into court and asking a judge to erase the past. Forgiveness for a late medical payment included imagining a conversation between the job candidate and his insurance company. To understand why hiring professionals would rely on stories, we return to the challenge they face: telling, from a context-free credit report, whether unpaid debts indicate something about a candidate’s character or life circumstances. Storytelling helps fill in details necessary for making that distinction. Stories, especially those that take the form of justifications and excuses, contextualize unexpected or deviant behavior in a way that allows the offending party to be seen as normal once more (Scott and Lyman, 1968). As Bruner (1990, pp. 49–50) writes: ‘The function of the story is to find an intentional state that mitigates or at least makes comprehensible a deviation from a canonical cultural pattern.’ Through stories, hiring professionals found reasons to forgive. Perhaps unsurprisingly, then, the second way that hiring professionals assigned moral meaning to instances of unpaid debt was to listen to the stories job candidates themselves told. Respondents described two moments when candidates might offer an account: at the end of interviewing, if a hiring professional told a candidate that a background check was the next step in the hiring process, and once a check was underway, if there was a problem with a candidate’s credit report. When credit reports revealed a problem, hiring professionals typically called the candidate to ask for an explanation.15 To tease apart whether delinquent debt was justified or not—the result of the person or the situation—hiring professionals deployed many of the same tools they used in inferring stories from credit reports. Nearly, all respondents talked about job candidates explaining they failed to repay loans because of job loss or divorce, and many relayed stories about economic downturn, the high cost of medical treatment and the difficulty of paying back student loans while working low-salary jobs. Respondents were generally sympathetic to these situations and discussed times when they hired someone who had been in such circumstances. This, however, rested on job candidates’ willingness to disclose intimate details of their personal lives. As a recruiter at a retailer hiring for an executive assistant position said: ‘I had someone who felt I was prying into their business and didn’t want to share, so I couldn’t move forward. A lot of people think it’s prying into their personal information. I can’t make an exception for you.’ To determine the veracity of the stories job candidates told, hiring professionals sometimes asked for documentation—divorce decrees, letters from creditors, proof of credit counseling—but more often than not they relied on their impressions of whether a candidate was telling the truth. In this way, what mattered was not only what a candidate said, but also how he or she said it. One bank recruiter explained the importance of seeming ‘genuine,’ saying: ‘If it sounds legitimate, fine, but if you’re talking over yourself and continuously backtracking where I’m like, but wait tell me a little bit more about this, and then your story sounds completely different, that just doesn’t work for me.’ A managing director of a staffing agency described one woman who had defaulted on student loans and did not get a job as an administrative assistant in a human resources department because she sounded ‘indifferent.’ He also described a woman who got a job at a consulting firm after properly explaining her loan delinquencies. ‘They respected her presentation of it,’ he said. ‘She could have been emotional but was right up to the line of emotional. She was very thoughtful in the way she delivered it.’ As scholars of self-presentation have observed, a convincing presentation solidifies the believability of the story a person tells (Garfinkel, 1956; Goffman, 1959). This careful moral redemption dance that hiring professionals expected job candidates to perform at times included one final component: offering a story at the right time. A handful of hiring professionals described telling job candidates at the end of interviewing that a background check would be the next step. Those with credit problems who sat silent in this moment were later deemed untrustworthy. A hardware store owner described one candidate for assistant manager who, upon hearing there would be a background check, told the owner that he had started his own business, and when it failed to take off, got behind on his credit card and mortgage payments. When the store owner later saw the credit report, he ‘was able to say, yep, okay, here it is, I see why he fell behind in his credit cards.’ Asked what his reaction would have been had the candidate not volunteered the story about the failed business, he said: ‘I probably wouldn’t hire this person because he might want to steal from me.’ 5.3 Moral mismatch and other hiring off-ramps The literature that looks for correlations between credit history and workplace outcomes and finds few implies that one sort of ‘error’ which might arise from the use of credit reports in hiring is excluding job candidates who pose no threat, and gaining a false sense of security about those who might. The data I have presented in this paper imply that other sorts of errors could also occur, even if hiring professionals are right in thinking that the key to using credit history is narratively teasing apart whether unpaid debt is the result of the person or the situation. For instance, employers’ use of the names of creditors to morally locate unpaid debt is problematic since mislabeling can easily occur. One respondent described an applicant who was rejected for a job because he had tried to pay his medical bills by putting them on his credit cards. When he fell behind on payments, the delinquencies registered as credit card, not medical, debt. Similarly, many store-branded credit cards can be used to make purchases elsewhere, obscuring what was actually bought. Another sort of error that can occur is that job candidates may be rejected from jobs simply because they do not wish to discuss intimate details of their personal lives, like their marital status or medical history. Here, a perfectly acceptable moral story may exist but not be volunteered. A more fundamental source of error that can arise from the process of moral storytelling is a sort of moral mismatch: a disconnect between a job candidate’s behavior and a hiring professional’s understanding of that behavior based on differences in social position. Consider, again, the opposite interpretations of a delinquent charge on a Best Buy credit card. Depending on the hiring professional, a late payment indicated either a frivolous spending spree or a principled stand against paying for a faulty product. In one case, blame was assigned to the person and in the other, to the situation. How could this be? The difference might be idiosyncratic. Yet it does feel relevant that a male hiring professional imagined an electronic device breaking and cut a job candidate some slack, while a female hiring professional thought about how electronics are frivolous purchases compared with essentials like food and clothing and judged a job candidate harshly. The nature of the data in this study can only suggest that hiring professionals use moral stories in ways that reflect their social locations and culturally situated understandings of when it is (and is not) acceptable for a person to fall behind on debt payments. The idea warrants further testing, although extant research lends support to the premise, with scholars of both narrative and moral judgment finding that social background, such as gender, race and class, shape what people see as legitimate (Lamont, 1992; Hitlin and Vaisey, 2010; Polletta et al., 2011). The fact that the male hiring professional who overlooked the Best Buy credit card default was then strongly critical of a female candidate who defaulted on a credit card issued by the lingerie shop Victoria’s Secret takes on greater significance in light of other research pointing to a connection between social location and conceptions of frivolous spending (Wherry, 2008). Job candidates may benefit from these dynamics when their life experiences mirror those of hiring professionals and the people they know, since such experiences provide fodder for moral storytelling. For example, a vice president of human resources at a bank who discussed going easy on people whose credit reports showed student loans along with delinquencies talked at length about her college-aged son and his friends and their experiences finding jobs in a slow economic recovery. Or, as a recruiter at another bank more pointedly said: ‘I have a sensitive spot for individuals with student loans because I’m one of them [laughs]… Especially if [the lender] Sallie Mae is on there. I know how much of a beast Sallie Mae is.’ Yet even absent personal experience, a hiring professional’s social position, such as class, can color beliefs about how the world works and consequently how he or she assigns blame for unpaid debt to the person or the situation. For example, a middle-class head hunter described three finalists for a school presidency. Two were women with credit problems, one from a divorce and one from a mistaken foreclosure proceeding related to the late-2000s housing crisis. The head hunter asked each candidate for a written account of what had transpired, and found the stories ‘compelling,’ seeing no reason to prevent the hire in either case. But the chair of the school’s board of trustees, a banker, felt otherwise, and refused to hire either. As the head hunter later explained: ‘Someone like him who comes from generation after generation of inherited wealth has no idea how hard it is to maintain a good credit rating, but he sat in judgment of these women post-2008, and he held that against them.’ Beyond moral mismatch, the data reveal one other major way moral storytelling can short circuit, deflecting people from being hired even when they have redeeming accounts. Credit reports often contain mistakes.16 This serves as a roadblock, especially when hiring professionals use credit reports not simply to learn about a job candidate, but also to signal to external constituents, such as regulators, business partners and investors, that they have conducted due diligence. For example, an employment officer at a bank described a candidate for a teller position whose credit report incorrectly included delinquent loans taken out by a similarly named person. The employment officer knew the report was wrong, but she would not hire the candidate until the report was fixed. As she explained: ‘They were working on it, but we needed documentation because at the end of the day we have external auditors that come in and audit everything we do.’ The need to produce a flaw-free record for inspection mattered just as much as the morally redeeming story. Indeed, most respondents described dealing with credit reports mistakes, including those stemming from identity theft. Some respondents said that if a job candidate could prove they had repaid a loan shown to be in delinquency, then the hiring process could proceed. Others, however, insisted that the credit report itself be fixed, a process that could take months. In such situations, hiring professionals rarely held jobs open, but they seldom saw this as problematic, saying that candidates could just apply again later. Respondents were very concerned about the possibility of hiring a morally compromised person, but less so about failing to hire a morally pure one. 6. Discussion When hiring professionals look at credit reports, they see information about a job candidate’s financial past presented in a rationalized way. The particularities of individual situations are gone, which lets lenders more easily compare people, often in mathematically sophisticated ways. Like other bureaucratic documents, credit reports have undergone a ‘deliberate stripping away of context’ which ‘detaches and obliterates social relationships’ so that ‘different events are rendered similar by virtue of the standardized categories that are imposed on them’ (Espeland, 1993, p. 299). This is deeply problematic for hiring professionals. The contemporary credit report rests on the premise that de-contextualized data about how a person has behaved can shed light on how he will behave in the future, but this approach fails employers trying to repurpose repayment data into workplace expectations. With no evidence about how parts of a credit report mathematically connect to workplace outcomes, hiring professionals struggle to understand what credit history means since the same numerical patterns might arise from different circumstances. Credit reports commensurate different lived experiences of debt and default, leaving no distinction between delinquencies that hiring professionals consider to be legitimate and those they deem unforgivable. Hiring professionals therefore rely on moral storytelling, a practice rooted in traditional, character-based understandings of credit history (Carruthers and Espeland, 1998; Carruthers, 2005). When a job candidate who has defaulted on loans offers a morally redeeming account of why events have played out as they have, the candidate demonstrates knowledge of how things ought to be and indicates connection to the moral order (Scott and Lyman, 1968; Bruner, 1990; Harvey et al., 1990). Moral stories are so useful for disentangling whether loan non-repayment stems from circumstance or character flaw that hiring professionals even construct them on job candidates’ behalf, imagining the events and intentions that sit behind particular sorts of unpaid debt. It would be easy to assume that hiring professionals use credit reports in a calculative manner, but this paper has shown that efforts to categorize and compare job candidates only go so far. Without known correlations between credit data and employment outcomes, storytelling kicks in as a way to fill in the gaps inherent in credit reports. The specific ways employers use moral stories to interpret tarnished credit history as either forgivable or not are patterned along lines that echo observations from the sociological study of money and debt. When hiring professionals attribute loan delinquencies to frivolous spending (Wherry, 2008), the betrayal of sacred debts (Polletta and Tufail, 2014), or extreme forms of behavior as reflected by the large amounts of money at stake (Livne, 2014), they assign responsibility to the candidate (the person) and steer away from making a job offer. When hiring professionals attribute non-payment to structural factors that cause bad credit (McCormack, 2014) or to a debtor rightfully withholding money from a creditor (Tach and Greene, 2014), they assign responsibility to the situation and move forward with hiring. Economic and moral life remain entangled, with different categories of money carrying different normative valences (Zelizer, 1994) and the nature of relationships playing a key role in determining appropriate behavior (Zelizer, 2012; Wherry, 2016). The twist in how moral storytelling plays out is that when hiring professionals judge whether job candidates have morally redeeming reasons for not repaying debts, they do so from a particular position in the social structure, which means their decisions reflect a particular moral view of the world (Lamont, 1992; Lempert and Monsma, 1994). While the data presented in this study are only suggestive, it seems that hiring professionals are especially, forgiving of situations that are easy for them to imagine personally experiencing. Moral storytelling is thus a relational process, one in which social distance helps determine how empathetic a hiring professional is likely to be (Park, 1924; Bogardus, 1937; Bourdieu, 1989). When there is more distance between a hiring professional and a job candidate—whether that distance arises from differences in gender, class or some other social location—it is less likely that the hiring professional will forgive unpaid debt and grant the candidate a job. While I do not observe job candidates accounting for bad credit, existing literature suggests that candidates with social backgrounds similar to hiring professionals may also be better at crafting accounts of financial difficulty that register as forgivable (Orbuch, 1997; Polletta et al., 2011; Lareau, 2015). 6.1 Broader implications In showing that the effect of bad credit on employment is contingent on moral storytelling, this study contributes to a growing body of literature about how the allocation of economic opportunity hinges not just on what information decision-makers have about people, but also on the way decision-makers create meaning from that information (Rivera, 2012; Lamont et al., 2014). Stevens (2007), for example, shows that college admissions officers use details from application packets to construct life stories about students in order to decide who to admit, and in ways that systematically favor students with starting socio-economic advantage—a process that parallels the one described in this paper. More generally, the role of moral storytelling in hiring implies that to fully understand the reproduction of economic disadvantage, scholars may need to pinpoint how macro-level trends, such as increasing reliance on personal borrowing (Krippner, 2011; Prasad, 2012), filter through organizational gatekeepers and the meaning they assign to specific situations. Americans frequently have trouble repaying their loans (Porter, 2012), with nearly a third of credit reports showing at least one debt in collections (Avery et al., 2003; Ratcliffe et al., 2014). Fourcade and Healy (2013) explicate how the spread of credit history into non-lending realms has the potential to both reproduce disadvantage across economic domains and create new forms of inequality (see also, Rona-Tas, 2017). While this work focuses on the role of actuarial techniques, such as credit scoring, in this paper I show how market-influenced ‘classification situations’ might also take shape less mechanically (Fourcade and Healy, 2013, p. 561). In the case of hiring, how the credit market splits and sorts gets refracted through hiring professionals’ socially positioned conceptions of personal financial life. When hiring professionals say they use ‘judgment’ to determine if a bad credit report will scuttle a job candidacy, they are speaking to a cultural mechanism (Lamont et al., 2014) that completes the loop from market situation to unequal life outcomes. While this research cannot say how much moral storytelling and its attendant biases would persist if there were clear mathematical correlations between credit data and employee behavior to rely on, the use of credit scores in insurance pricing—where scores are both highly predictive and morally contentious (Rona-Tas, 2017)—suggests that scoring need not entirely crowd out moral thinking. Furthermore, even if credit data were shown to mathematically predict workplace outcomes, there might be other reasons to limit their use (e.g. the potential for cumulative disadvantage across credit and labor markets). 6.2 Limitations and future research While this paper shows how hiring professionals interpret credit reports, its purposive design may limit generalizability. I interviewed respondents who hire for professional and non-professional positions across industry and organization size and consistently found that moral storytelling links credit history to hiring decisions, but it is possible that a more rules-bound process occurs in other contexts. Notably, all but one respondent discussed considering credit history during the final phase of hiring, to evaluate a single or short list of candidates. If credit history were used as a preliminary screen for a large field of applicants, then I would expect a less individualized approach.17 Since trade-group surveys show that nearly all firms run credit checks toward the end of the hiring process (Society for Human Resource Management, 2012), I do not believe this poses a major threat to generalizability.18 Additionally, even if credit checks were used earlier in the hiring process, they could still not be informed by empirically established correlations (since none exist), which means the practice would still be subject to biases, though they might be applied more consistently. One direction for future research is to quantify the patterns identified in this paper, especially the concept of moral mismatch. Policymakers have expressed concern that credit checks may disproportionately impact certain groups of people with worse credit on average, such as African-Americans, but policy debate has not included the possibility that credit checks may be problematic for another reason: that hiring professionals understand the ‘badness’ of a credit report differently based on whose credit report it is.19 Quantitative research might also explore if certain candidates (such as those applying for more senior positions) are held to higher standards; that is to say, whether the dynamics described in this paper especially prevent access to particular types of jobs. Future research might also investigate the strategies of job candidates with bad credit. While the current study does not provide a line of sight into the job-candidate experience, a few hiring professionals mentioned that upon learning there would be a credit check, some candidates withdrew their applications. If candidates with bad credit self-select out of certain jobs, then another sort of ‘error’ may be introduced—people who would have gotten through the process of moral storytelling not getting a shot to do so. A final question for future research is how employers decide to consult credit reports in the first place. While we know that credit bureaus began marketing credit reports as a hiring screen in the late 1980s, no research has systematically studied the adoption of the practice. Such a study could shed light on the widespread use of credit checks, despite there being no clear evidence that they prevent workplace problems. Institutionalists argue that organizations often adopt practices not because they achieve some internal purpose but because they signal to outsiders that a firm conforms to social expectations (Meyer and Rowan, 1977; DiMaggio and Powell, 1983). The data in this study suggest that at least some firms operate in an institutional environment where credit reports signal to external stakeholders that employers have performed due diligence. In such a system, a shared belief that credit checks mitigate risk may be more important than any actual link between credit history and workplace behavior. If checking credit reports is in fact what Power (1999, p. 123) calls a ‘shallow ritual of verification,’ then we might be especially concerned about moral mismatch and other hiring off-ramps that leave people without jobs they otherwise deserve. Footnotes 1 While the federal Fair Credit Reporting Act lets employers look at the credit history of job applicants, since 2007, 11 states and some cities have passed laws limiting the practice. Lawmakers in dozens of other states and Congress have introduced legislation to do so (National Conference of State Legislatures, 2015). 2 The article does not identify the type of government organization studied, although two of the four co-authors work for the Federal Bureau of Prisons. 3 Some employers began using credit files much earlier. Stuart (2003) describes credit bureaus convincing the US Civil Service Commission to buy credit reports in the 1930s. These reports likely contained much more than the rationalized financial data we see today. 4 While credit bureaus discuss this connection qualitatively, they have not produced evidence of a mathematical correlation. In an Oregon State Senate hearing, one credit bureau representative said he knew of no such evidence (Martin, 2010). 5 Different cultural and legalistic outlooks seem to be responsible for employment credit checks rarely being used outside the USA, although they are used in the UK (Dowling, 2010; Mayer Brown, 2016). 6 I focused on the Boston area so that I could interview in person, which tends to produce better data (Weiss, 1994). I started with large and fast-growing firms since individuals who hire infrequently might have difficulty answering questions (K. Neckerman, personal communication). 7 This comports with survey data from SHRM. 8 A hiring professional at a bank explained that the Federal Deposit Insurance Corporation required hiring ‘financially responsible’ individuals, and that while she did not have to use credit reports to do this, they were the best way to ‘defend’ her decision-making. 9 Employers must get job candidates’ permission to pull credit reports, but employers may eliminate candidates who refuse and generally do. 10 Some respondents volunteered that it wouldn’t make sense to look at credit scores anyway, since scores can drop based on a person simply applying for more credit and other events unrelated to loan repayment. 11 Surveys from the Society for Human Resource Management (2012) find that employers’ decisions are most influenced by accounts in debt collection, although they do not address how hiring professionals use the information. 12 Only two respondents said they did not contact job candidates who failed written standards. This comports with surveys from the Society for Human Resource Management (2012), which find that about 80% of employers who review credit reports sometimes hire people despite financial problems. 13 Surrounding circumstances at times undermined the usefulness of credit reports altogether. A general manager at a hotel said that checking the credit of housekeepers made no sense, even though they had access to guest rooms and valuables, because the hotel did not pay them well. The manager assumed many employees would have bad credit from the difficulty of making ends meet on low pay (not moral failing). 14 Court cases send conflicting messages about the legality of private employers considering bankruptcy in hiring (Shepard, 2012). Some respondents said it was illegal to do so. 15 Some hiring professionals contacted all candidates with credit problems. Others said a candidate might not be given a chance to explain if it were easy to hire someone else. Few said they never ask for explanations. This pattern fits survey results from SHRM. 16 A Federal Trade Commission (2012) survey found 26% of Americans identified at least one error in a credit report. 17 In the one case where credit was a preliminary screen, the hiring professional had less personal flexibility to take into account extenuating circumstances, although the rules he applied reflected earlier moralization (e.g. medical accounts weren’t counted). 18 In a 2012 survey, SHRM found 58% of employers conduct credit checks after making a contingent job offer and 33% conduct them after an interview but before a job offer (n = 171). Figures from a 2010 survey were 57% and 30%, respectively (n = 199). 19 Some econometric papers suggest that credit checks may help African-Americans with good credit by making discriminatory employers more willing to hire them (Bartik and Nelson, 2016; Clifford and Shoag, 2016), but this leaves unaddressed how African-Americans with bad credit fare relative to other candidates with bad credit. Acknowledgements For helpful comments on earlier versions of this work, the author thanks Michel Anteby, Jason Beckfield, Bart Bonikowski, Bruce Carruthers, Barry Cohen, Frank Dobbin, Alexandra Feldberg, Roberto Fernandez, Adam Goldstein, Christopher Jencks, Carly Knight, Michèle Lamont, Rourke O’Brien, Devah Pager, Kim Pernell-Gallagher, Andrew Weaver and Christopher Winship, as well as participants of the Society for the Advancement of Socio-Economics’s 2014 annual meeting, the Harvard Inequality Seminar, the Harvard Sociology Qualifying Paper Seminar and the Law and Society Association’s 2015 annual meeting. The author especially thanks Jason Beckfield and Frank Dobbin for guidance throughout the project. Alexandra Feldberg graciously provided help creating the demonstration employment credit report. Funding This work was supported by the Center for American Political Studies at Harvard University. References Avery R., Calem P., Canner G. 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