Do Job Applicant Credit Histories Predict Performance Appraisal Ratings or Termination Decisions?

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1 The Psychologist-Manager Journal, 15: , 2012 Copyright The Society of Psychologists in Management ISSN: print / online DOI: / Do Job Applicant Credit Histories Predict Performance Appraisal Ratings or Termination Decisions? Laura Koppes Bryan University of West Florida Jerry K. Palmer Eastern Kentucky University In this study the authors investigated the criterion validity of job applicant credit report history in predicting subsequent performance appraisal ratings and termination decisions for 178 employees at a large financial services corporation. Predictors extracted from applicant credit reports, such as number of times late with payments, had no relationship with either performance appraisal ratings or termination decisions. The authors therefore recommend caution in using credit report data for making selection decisions. Since World War I, U.S. companies have widely implemented various tools, such as tests, interviews, references and letters of recommendations, and college transcripts, in an attempt to predict future employee behavior. In the field of industrial and organizational (I-O) psychology, these tools are known as predictors, which This research was conducted when the first author was the Director and Associate Professor of Industrial and Organizational Psychology at Eastern Kentucky University. The authors thank the support of numerous media reporters, attorney offices, and U.S. Equal Employment Opportunity Commission staff who encouraged them to publish this research. They also thank Adrienne Bauer, Steve Kass, Steve Vodanovich, and two anonymous reviewers for their input and reviews. The authors are listed in alphabetical order; both contributed to conducting the research and preparing this manuscript. Variations of the results were previously presented at two national conferences (Palmer & Koppes, 2003, 2004). Correspondence should be sent to Jerry K. Palmer, Psychology Department, Eastern Kentucky University, 521 Lancaster Avenue, Richmond, KY

2 EMPLOYEE CREDIT HISTORIES 107 are designed to measure abilities, skills, intelligence, personality characteristics, biographical data, and other factors deemed necessary to perform a particular job. These predictors are commonly used in determining whether to offer an applicant a job, that is, in the selection of employees (Borman & Hanson, 1997; Gatewood & Feild, 2001; Hough & Oswald, 2000; Ryan, McFarland, Baron, & Page, 1999; Schmitt, Cortina, Ingerick, & Weichmann, 2003). In recent years, employers and researchers have given considerable attention to measuring integrity or honesty of prospective employees for predicting performance, counterproductive work behaviors (CWBs), or turnover (e.g., Berry, Sackett, & Wiemann, 2007; Camara & Schneider, 1994; Fine, Horowitz, Weigler, & Basis, 2010; Wanek, 1999). Similarly, an interest in conscientiousness (being dependable and hardworking), one of the five broad dimensions of the Big Five model of personality (Costa & McCrae, 1988), has emerged as a predictor of job performance (Barrick & Mount, 1991). To measure these constructs of honesty/integrity and conscientiousness, there is evidence that many U.S. organizations are relying on personal financial credit history reports because of assumptions that low credit history scores imply bad credit and financial irresponsibility, which are assumed to be indicators of dishonesty or theft (i.e., CWBs) and lack of dependability (i.e., poor conscientiousness), resulting in poor performance and termination in the workplace. Recent industry surveys suggest that the use of credit history reports in employee selection is taking place on a large scale in the hopes of minimizing dishonesty and poor job performance (Denston, 2010; Esen, 2004; Hollinger & Davis, 2002). Despite an all-time high in the use of credit checks (Rosenberg, 2007), credit history as a valid predictor of employee performance has received little or no empirical research investigation. While there may be arguments for why an applicant s credit history might predict subsequent behavior on the job (e.g., dishonesty, irresponsibility), we know of no published empirical study examining the validity of credit history in predicting worker behavior. In the present study we sought to address this problem; we investigated the relationship between applicant credit history and subsequent performance appraisal ratings and termination decisions. This research is especially pertinent given the dramatic rise in bankruptcy petitions, mortgage delinquencies, and real estate foreclosures (Anderson, 2008; Dugas, 2009; Howley, 2010). THE LEGALITIES OF PREDICTORS Organizations can legally use predictors to make selection decisions and do so without worry unless the predictor results in adverse impact against a protected class (Gatewood & Feild, 2001; Gutman, Koppes, & Vodanovich, 2011). Federal protected classes include race, skin color, sex, religion, age, national

3 108 KOPPES BRYAN AND PALMER origin, and disability. Adverse impact would occur if the predictor causes the percentage of minority applicants hired to be less than four fifths of the percentage of majority applicants hired. If a predictor or employment decision causes adverse impact, the organization must be able to demonstrate that the predictor or employment decision is either: (a) a Bona Fide Occupational Qualification (BFOQ), (b) a business necessity, or (c) job related (Uniform Guidelines on Employee Selection Procedures, 1978). In recent years, the Equal Employment Opportunity Commission (EEOC) acknowledged that credit checks could be discriminatory when used for selecting applicants, leading to the potential for adverse impact (Equal Employment Opportunity Commission, 2007). The EEOC suggested that, historically, individuals who received bad credit checks were of lower socio-economic status, often associated with members of protected groups of the Civil Rights Act 1964 (Equal Employment Opportunity Commission, 2007). Additionally, the questionable validity of credit histories could lead to poor prediction, exacerbating possible discriminatory decisions. USES OF CREDIT HISTORY DATA Despite several cautions (e.g., Howie & Shapero, 2002), some government regulations (e.g., Fair Credit Reporting Act, 2002), and debate in several state legislatures on whether to prohibit the use of credit history ( Insurance Representatives Oppose Bill, 1999), credit history has been used by various companies to determine whether a person is given a loan, to set insurance rates, and as an aid in employee selection ( Consumer Alert: Can t Get Insurance?, 1997; Muchinsky & Skilling, 1992). This practice is based on the assumption that credit history predicts the likelihood a person will make loan payments in a timely manner, the likelihood a person will file an insurance claim, and the likelihood the employee will effectively perform a job and not leave the company. Unlike the case with credit history as a predictor of employee behavior, there is evidence for the validity of credit history at predicting insurance losses and loan repayment. For example, a study by one large insurance company found that people with evidence of serious financial instability incurred about 40% more auto insurance losses (Kent & Baum, 2000, p. 86) and according to Consumer Reports ( Consumer Alert, 1997), some insurance companies use credit reports as the only factor in determining whether to approve a policy. Musulin (2002) stated that The link between credit score and loss propensity is often found to be stronger than most of the variables traditionally used in auto insurance, like driving record, age, miles driven, or type of vehicle (p. 33). Musulin noted two possible reasons: (a) people who are in financial trouble may be more likely to exaggerate their loss claims, and (b) that people who are responsible about managing their finances are probably responsible behind the wheel (p. 33). This second reason implies

4 EMPLOYEE CREDIT HISTORIES 109 a link between responsibly managing finances and being a responsible individual (e.g., conscientiousness). There is also empirical evidence to support the notion that credit history predicts loan repayment (Barron, Elliehausen, & Staten, 2000; Muchinsky & Skilling, With regard to employee selection, Barth (2002) stated that use of credit history for selection decisions may be appropriate for jobs in which employees have access to money or financial accounts because of an assumption that credit reports measure responsibility to manage finances. With this rationale, the use of credit history to select employees may be frequent. For example, the Federal Trade Commission (Federal Trade Commission, 2002) stated that for sensitive positions, use of credit history in employee selection is not uncommon. The Fair Credit Reporting Act (2002) 15 U.S.C et seq. states that employers may obtain credit reports for employment purposes, such as hiring, promotion, reassignment, or retention. To obtain an individual s credit report for employment reasons, the employer is required to disclose its intent to the individual. The disclosure must be a clear and conspicuous written document that addresses only the employer s intent to obtain the report. The employer is to notify the employee prior to accessing the report, and the employee must provide his/her consent in writing (Zachary, 2006). Morris and McDaniel (1995) noted that the lack of a credit history for younger applicants presents a problem for many companies that want to screen applicants for jobs involving positions of trust, implying that credit reports are measures of trustworthiness. In the next section, we provide some explanations for employers using credit histories for hiring or selection decisions. Credit History and Employee Selection Hollinger and Davis (2002) conducted a national retail security survey in 2002 to provide loss prevention professionals with current data regarding the prevalence of employee theft and other sources of financial loss. Survey responses were received from 118 retail companies, representing a wide range of industries. They found that 13 types of pre-employment integrity screening measures were being utilized by a significant number of companies, and that 40.7% of respondents reported using credit history checks. The Society for Human Resource Management (SHRM) Workplace Violence Survey (Esen, 2004) found that the use of credit history checks is rising. In 1996, 19% of respondents reported using credit checks in the selection of employees; in 2003, this number increased to 35%. This pattern was found regardless of organization size. As cited in Denston (2010), SHRM reported in 2006 that 43% of employers conducted credit histories as a component of background checks for new workers. Denston (2010) noted that as recent as 2009, the SHRM membership survey found that 47% of respondents limit their use of credit background checks to job candidates for certain types of positions, 40% do not conduct this type of background check on any job

5 110 KOPPES BRYAN AND PALMER candidates, and 13% conduct credit checks on all candidates (p. 6). In addition, some background checking companies, in web advertisements, state that they will include a credit check as part of the employee screening services they offer, and some others offer Pre-Employment Credit Reports, a credit history report tailored for use in selecting employees. In 2007, the U.S. EEOC reviewed the prevalence of employee credit checks and acknowledged that an increasing number of employers are screening out job applicants for having a negative credit history (Equal Employment Opportunity Commission, 2007, p. 1). It is beyond the scope of this article to provide a literature review of research on employee selection, particularly with regard to predictors or antecedents of job performance (e.g., Campbell, McCloy, Oppler, & Sager, 1993; Schmitt et al., 2003; Viswesvaran & Ones, 2000), counterproductive work behaviors (e.g., Hollinger & Clark, 1983; Sackett, 2002; Sackett & Devore, 2001), and termination from the job or turnover (e.g., Cavanaugh, Boswell, Roehling, & Boudreau, 2000; Chen & Spector, 1992). For purposes of this study, we offer three assumptions for the use of credit history in employment selection that stems from this literature. The first assumption is that a credit report may reflect an individual s history for displaying conscientiousness, such as dependability, responsibility, meeting deadlines or obligations, and non-procrastination. Several studies have demonstrated the relationship between conscientiousness and job performance (e.g., Borman, Penner, Allen, & Motowidlo, 2001). Building upon research conducted by Schmidt, Hunter, and Outerbridge (1986), Borman, White, Pulakos, and Oppler (1991) found that dependability had a direct effect on knowledge, number of disciplinary actions, and job performance. Other studies have linked (negative associations) conscientiousness with dysfunctional behaviors in organizations, including disciplinary problems, theft, and absenteeism (Hogan & Ones, 1997; Sackett & Wanek, 1996). The second assumption is that credit history may reflect integrity or honesty of a prospective employee. As noted earlier, measures of integrity, in recent years, have been implemented in the context of selection decisions (e.g., Berry et al., 2007; Sackett & Wanek, 1996) to predict an employee s likelihood of engaging in CWBs, such as cheating, stealing, sabotage, or more recently known as antisocial behaviors, incivility, and deviant workplace behaviors (Motowidlo, 2003; Sackett, 2002). Additionally, Mantell (1994) suggested that credit history along with other factors, such as driving records, military, and criminal history, are ways to detect violence-prone individuals. The third assumption is that lower credit history scores may be an indicator of financial distress, which could lead to CWBs. Spector (1997) noted that CWBs such as fraud, sabotage, bribery, and withdrawal behaviors, may result from frustrations experienced by individuals. For example, an employee who is having financial difficulties might be tempted to steal from the company (theft

6 EMPLOYEE CREDIT HISTORIES 111 or fraud). In addition, it might be the case that an employee in financial trouble may be more likely to quit her/his job when another, better paying job opportunity arises. Correcting for measurement error in the predictors and sample error, Griffeth, Hom, and Gaertner (2000) reported effect sizes ranging from.10 to.20 between stressors and actual turnover behavior. In addition to these assumptions, an argument for use of credit history in employee selection might be to avoid a negligent hiring claim (Howie & Shapero, 2002), an action that employers want to avoid. Implicit in this argument is the first three assumptions presented above; low credit scores reflect poor conscientiousness, a lack of integrity, and financial distress, which constitutes a higher likelihood to exhibit CWBs (e.g., steal or embezzle), threatening the company s clients and employees in addition to the company itself. Another argument is that a credit history report, unlike integrity tests and measures, could be viewed as a more objective assessment of past behavior, rather than a measurement of past behavior as recalled and reported by the applicant him/herself. Credit history reports do not allow for intentional distortion by the applicant. For example, applicants cannot distort if they had frequently been late in making payments or had not paid their debt at all. As noted earlier, should managers use any of this information in making a hiring decision, the action falls under the jurisdiction of the Civil Rights Act (1964; 1991) as well as other relevant legislation and case law (Gutman, Koppes, & Vodanovich, 2011). Case law with respect to the use of assorted information obtained during a background check, such as arrest records and wage garnishment, is very clear that this information must be valid if it results in adverse impact (Green v. Missouri Pacific Railroad, 1975; Wallace v. Debron, 1974). The EEOC, basing conclusions on poverty and census statistics, has stated that use of credit history data would probably result in adverse impact against minorities (Equal Employment Opportunity Commission, 2007; Joel, 1996). The EEOC is especially concerned because it reports that certain minority groups (African-Americans) have bad records at a 21% higher rate than Whites (Equal Employment Opportunity Commission, 2007, p. 2). Arnoldy (2007) reported on a 2004 study conducted by the Texas Department of Insurance involving 2 million people that Blacks have an average credit score roughly 10% 35% worse than Whites. Hispanics have scores 5% 25% worse than Whites (para. 6). Consequently, credit checks as a selection tool could be racially discriminatory, which clearly violates Title VII of the Civil Rights Act. THE CREDIT REPORT It isn t clear what part of the credit report companies use to make hiring decisions. A credit report contains several items, such as the number of negative accounts,

7 112 KOPPES BRYAN AND PALMER the number of times late on payments, the number of accounts that have required collection action, as well as other information. Insurance underwriters have used past repayment history, amount of credit owed, length of credit history, new credit (recently opened accounts), and type of credit (e.g., credit cards, mortgages), among other information, to compute insurance rates ( How Your Credit History Affects, 2002). The exact methods for combining this information, however, are unknown to all but those who do the underwriting ( How Your Credit History Affects, 2002). We are not aware of any study that has empirically examined the content or construct validity of credit reports, although the assumptions described above are made and underlie the frequent use of credit reports as a selection tool. PURPOSE There is a need for examination of the validity of credit history, and in the present study we sought to do that. Although we recognize the importance of determining the construct validity of credit reports, we had the opportunity to investigate the criterion validity of credit history data in predicting employee performance appraisal ratings and termination decisions for a large company 1. This opportunity is rare because companies typically do not provide access to selection data, performance evaluations, and termination decisions to researchers. Based on the rationale presented earlier, we hypothesized that applicants with good credit reports will, after being hired, receive positive performance appraisal ratings and will be less likely to be terminated from their jobs. Additionally, we were interested in determining whether credit history data could distinguish (a) between those employees who leave for negative reasons (negative terminations; e.g., asked or required to leave or quit) and other employees who leave for nonnegative reasons (non-negative terminations; e.g., family relocation), (b) between the negative terminations and those employees who remained on the job, and (c) between the non-negative terminations and those employees who remained on the job. METHOD Participants For the present study, personnel records of 178 employees spanning six company locations and holding jobs falling within a financial services and collections job category were included of a large financial services organization. The original 1 The name of the organization is withheld throughout this article per the organization s request.

8 EMPLOYEE CREDIT HISTORIES 113 dataset consisted of 200 randomly selected employee files. Twenty-two of the employee files had credit reports with no data (no credit history), resulting in 178 files with predictors (credit history data). Of these, 60 were former employees (terminated employees). Performance appraisal scores existed for 141 of the 178 employees files. Demographic data were not provided to the researchers; however, based on company population figures, we estimated that the average age of the sample was 35 years and 70% of the employees in the sample were female. Procedures Performance ratings, termination decisions, and credit history reports for the sampled employees were collected from their personnel records. Performance ratings and termination data were stored in electronic form (Microsoft Excel database), with employee names replaced by random numbers; no personal information was contained in the file. Credit reports were in printed form (see Appendix 2 ), with personal information removed and replaced by the random numbers. This information was then provided to the researchers, who used the random numbers to match performance data with credit reports. A small number of the credit reports were missing pages or were unreadable. Thus, the analyses were computed on varying sample sizes, which are indicated in our tables. Due to the archival nature of this data, consent forms were not obtained; the study was approved as exempt by the university s Institutional Review Board. Variables Several pieces of information were available on the credit reports. For purposes of this study, the following variables were extracted from employees credit reports; we included only data covering the two years immediately preceding employment at the company. The data included: The number of positive accounts in the credit history (e.g., never late ). The total number of accounts that were not positive (negative accounts). The number of times, across all accounts, that payment had been 30 days late. The number of times, across all accounts, that payment had been 60 days late. 2 The credit report example in the Appendix reveals the types of items in a report, and most credit reports may list several accounts spanning multiple pages. In this hypothetical example we attempted to present a credit report with at least one of several types of accounts or data sources.

9 114 KOPPES BRYAN AND PALMER The number of times, across all accounts, that payment had been 90 days late. The number of times, across all accounts, that payment had been 120 days late. The total number of times, across all accounts, payment had been late. The number of accounts that had been turned over to collections. The number of accounts that had been written off plus those requiring litigation (charge-offs). The highest amount ever owed by the subject. The maximum credit amount approved for the subject. The amount owed by the subject when the credit report was generated. The amount past due when the credit report was generated. Whether or not any of the subject s payments were past due. (Yes/No) The amount the subject was paying, monthly, when the credit report was generated. Additionally, we computed the following variables as transformations of the original variables. This computation was done because we felt that it might be possible that the non-transformed variables might not adequately assess the constructs (e.g., conscientiousness; integrity) that credit history might reflect. The variables include: The total number of accounts (positive + negative). The percentage of all accounts that were negative (negative total). The average number of times late per late account. The average number of times late for all accounts. Predictors To test the hypotheses, we selected specific predictors that we believed conceptually captured the constructs in the rationale. These included: The number of positive accounts in the credit history (e.g., never late ). The total number of accounts that were not positive (negative accounts). The number of times, across all accounts, that payment had been 30 days late. The number of times, across all accounts, that payment had been 60 days late. The number of times, across all accounts, that payment had been 90 days late. The number of times, across all accounts, that payment had been 120 days late.

10 EMPLOYEE CREDIT HISTORIES 115 The total number of times, across all accounts, payment had been late. The number of accounts that had been turned over to collections. The number of accounts that had been written off plus those requiring litigation (charge-offs). The total number of accounts (positive + negative). The percentage of all accounts that were negative (negative total). The average number of times late per late account. The average number of times late for all accounts. Again, only accounts that were active during the two years immediately preceding employment at the company were used. Criteria The performance appraisal ratings served as performance criteria; these ratings were on a 5-point scale (4 = Outstanding, 3 = Exceeds Expectations, 2 = Meets Expectations, 1 = Below Expectations, 0 = Unacceptable). The ratings were supervisory evaluations of overall performance. Termination decisions served as turnover criteria, which included whether the employee was active or no longer employed with the company. In addition, three types of termination decisions were included in the analyses: (a) employees who left on negative terms (i.e., negative terminations), (b) employees who left on nonnegative terms (non-negative terminations), and (c) non-terms, employees who remained on the job (i.e., non-terminations). RESULTS The means and standard deviations of the performance ratings and the variables extracted from employees reports are presented in Table 1. Table 2 contains the descriptive statistics for the credit history predictors and three types of termination decisions. Correlational analyses were conducted between the credit history predictors and performance ratings and are included in Table 3. The logistic regression analyses examined the relationships between predictors and the three types of termination decisions. The analyses are presented in Table 4. Performance Appraisal Ratings With regard to correlations between the predictors and performance ratings, none of the credit history predictors were correlated with performance ratings in the expected direction. Virtually all of the correlations between the predictors and performance ratings were near zero. The only significant correlation with

11 116 KOPPES BRYAN AND PALMER TABLE 1 Descriptive Statistics for Performance Rating and Credit History Variables Variable M SD N Performance rating Number of positive accounts Number of negative accounts Number of times 30 days late Number of times 60 days late Number of times 90 days late Number of times 120 days late Total number of times late Collection accounts Number of charge offs and litigation actions Highest amount ever owed $ 45, $ 77, Maximum credit approved $ 15, $ 57, Current amount owed $ 38, $ 70, Current amount past due $ 1, $ 3, Whether any payments were past due (yes/no) Amount paying monthly $ 1, $ 8, Total number of accounts Percent of accounts that were negative Average times late per late account Average times late across all accounts performance occurred between the number of times 30 days late with performance ratings. In other words, employees who had a higher number of 30-day late payments were slightly more likely to receive higher performance ratings. This correlation was in the opposite direction of what was hypothesized. Further investigation of the data revealed this result may have been skewed by two outliers: two individuals who had the largest number of 30-day late instances also received Outstanding and Meets Expectations ratings. In sum, there was virtually no relationship between credit history and performance ratings, thus, our hypothesis was not supported. Termination Decisions For termination decisions, we examined whether credit history data could (a) distinguish between those employees who leave for negative reasons (negative terminations; e.g., asked or required to leave or quit) and other employees who leave for non-negative reasons (non-negative terminations; e.g., family relocation), (b) distinguish between the negative terminations and those employees who remained on the job, and (c) distinguish between the non-negative terminations and those employees who remained on the job. Logistic regression analyses examined

12 EMPLOYEE CREDIT HISTORIES 117 TABLE 2 Descriptive Statistics for Termination Decisions and Credit History Predictors Negative Terms Non-Negative Terms Non-Terms Predictor M SD N M SD N M SD N Number of positive accounts Number of negative accounts Number of 30-day late instances Number of 60-day late instances Number of 90-day late instances Number of 120-day late instances Total number of times late Accounts requiring collection action Charge off accounts Total number of accounts Percentage of accounts negative Avg. times late per late account Avg. times late across all accounts Note: Statistics reflect consumer credit activity over an approximately two-year period. TABLE 3 Correlations between Credit History Predictors and Performance Ratings Predictor Performance Ratings Number of positive accounts.075 (N = 106) Number of negative accounts.092 (N = 105) Number of times 30 days late.188 (N = 106) Number of times 60 days late.076 (N = 106) Number of times 90 days late.105 (N = 106) Number of times 120 days late.043 (N = 106) Total number of times late.150 (N = 106) Collection accounts.009 (N = 106) Number of charge offs and litigation actions.047 (N = 106) Total number of accounts.115 (N = 105) Percent of accounts that were negative.074 (N = 103) Average times late per late account.075 (N = 103) Average times late across all accounts.158 (N = 100) Note: p <.05 the relationships between the predictors and these three types of termination decisions, which are presented in Table 4. Only five of the 39 analyses were significant, and one was marginally significant. Four of the five significant correlations

13 TABLE 4 Logistic Regressions for Credit History Predictors and Termination Decisions Negative Terms v. Non-Terms Negative Terms v. Non-Negative Terms Non-Negative Terms v. Non-Terms Predictor χ 2 Sig. R 2 N χ 2 Sig. R 2 N χ 2 Sig. R 2 N Number of positive accounts Number of negative accounts 5.78 p < Number of 30-day late instances Number of 60-day late instances Number of 90-day late instances Number of 120-day late instances p < Total number of times late p < Accounts requiring collection action Charge off accounts Total number of accounts Percentage of accounts negative Avg. times late per late account p < p < Avg. times late across all accounts p < Note: df = 1 for all tests. R 2 = Nagelkerke R 2 118

14 EMPLOYEE CREDIT HISTORIES 119 were for predictors of negative terms vs. non-negative terms. However, this finding should be interpreted with great caution. The latter three significant predictors (total times late, avg. times late per late account, avg. times late across all accounts) are composite variables, combining and/or transforming other predictors that are also included in the table. Because of this, it is likely that the significance of the three composite variables is due to the inclusion of the single, significant predictor (120 day late instances). 3 The non-significant analyses cannot be attributed to differences of group sample size percentages. Terminated employees comprised between 22% and 52% of the sample for all analyses. In sum, only a few of the measures of credit history used for the present sample had any validity in the prediction of termination data. This number is sufficiently small that chance is probably the best explanation, thus, our hypothesis was not supported. DISCUSSION We hypothesized that applicants with good credit history reports will, after being hired, receive more positive performance appraisal ratings and will be less likely to be terminated from their jobs, which was not fully supported. It s important to note here that this particular company did not require the use of credit reports for hiring. Each specific manager had the discretion of using the information. In fact, we inquired about the employees who had horrible credit reports and were hired; we were told that the manager probably did not look at the information or ignored it. Anecdotally, we noticed some handwritten notes next to the bad credit information, such as lost job, or spouse spent all the money, suggesting that employees were questioned about these reports during the selection interview, consequently, the explanations justified a hiring. Based on the results of this study and sample, we caution that credit history data should not be used to select employees unless the company demonstrates evidence that such data have validity for predicting employee behavior. To the best of our knowledge, we conclude that no published empirical evidence for the criterion validity of credit history exists. We thus strongly call for further research on credit history as a tool used in selection decisions. In the present sample, credit history data had no criterion validity in predicting either performance ratings or termination. The only significant correlation with performance occurred between number of times 30 days late with performance ratings, which we believe was skewed by two outliers (i.e., employees who received outstanding 3 Subsequent analyses on the composite variables calculated the same way as before but excluding 120 day late instances, confirmed that this was the case. Exclusion of 120 day late instances resulted in the composite predictors being non-significant.

15 120 KOPPES BRYAN AND PALMER ratings). As noted previously, only a few of the measures of credit history were correlated with termination decisions; however, this number is sufficiently small that chance is probably the best explanation. Adverse Impact and Credit History As stated earlier in this article, employers can use any test or predictor, provided the test or predictor does not result in adverse impact against a protected class. If the test or predictor does result in adverse impact, the employer must demonstrate the test or predictor to be job related. If the test or predictor is not job related and does result in adverse impact, the employer would almost certainly lose any litigation brought against it by a party contesting the test or predictor s use (Gutman et al., 2011). For the present study, the existence of adverse impact is unknown because demographic information race, age, gender, etcetera was not provided to the researchers. There is reason to believe that credit history data probably does result in adverse impact, which is the position of the EEOC, based on demographic and census data (Joel, 1996). In a study of college juniors and seniors, Allen and Jover (1997) found that while 44% of African-American respondents had received a delinquency notice, only about 28% of Caucasian respondents had. In an analysis of 220,000 Federal Housing Administration (FHA) insured loans, the FHA found that African-American respondents had a default rate about twice that of Caucasian respondents (Poltenson, 1996). Even some proponents of the use of credit history to determine lending decisions (e.g., Poltenson, 1996) have admitted that the use of credit history may be the next civil-rights confrontation. More recently, in a report to an EEOC meeting of Employment Testing and Screening, Adam T. Klein explained that The correlation between credit record and race is only exacerbated by the fact that various credit problems correlate with race (Equal Employment Opportunity Commission, 2007, p. 2). These credit problems include certain jobs that are a minus on an individual s credit history which are typically held by racial minorities (e.g., migratory work, low paying service), higher rate of bankruptcy filings by African-Americans, and lending discrimination in which African-Americans are more likely to be denied loans, however, when they are approved for loans, they are more likely to receive loans with worse terms than Caucasians. Furthermore, we might expect credit history to discriminate against immigrants who have not resided in their new country long enough to accumulate any credit history. As reported by Thomas Frank in USA Today (Frank, 2009), a 2007 Freddie Mac study found that 48% of Blacks had bad credit records compared with 34% of Hispanics and 27% of Whites (para. 12). Already, several parties have attempted to restrict or ban the use of credit history data by banks and insurance companies. For example, opponents gained a

16 EMPLOYEE CREDIT HISTORIES 121 victory in 2002 when the Washington State Legislature passed legislation restricting the use of credit history information by insurance companies. As noted in the report to the legislature, the Washington State Legislature passed one of the toughest credit scoring laws in the nation (The Office of the Insurance Commissioner, 2003, p. 3). Politicians in other states, among them Maryland, North Carolina, Nebraska, Minnesota, Arizona, and Vermont, have tried without, or with limited, success to limit the use of credit history in insurance underwriting. Karen Aho recently reported on MSN Money that in 11 states, legislators have set restrictions for the use of credit history for employment decisions (Aho, 2009). Five states have challenged employer credit checks (Frank, 2009). Predicting the likelihood of filing a claim and predicting employee work performance and turnover are quite different matters. The insurance and banking industries have produced data supporting their arguments, but even if credit history data predict the likelihood of filing a claim, there is no evidence that the data predict work performance and turnover, and the results of the present study indicate it does not. One possible reason credit history may predict filing a claim (assuming it does) but not predict work performance and turnover is that there may be much to gain (and may be little to lose) by filing an insurance claim, whereas there is much to lose (one s job) by performing poorly at work. This consequence is true whether a person has a good credit history or a poor one; very few people want to lose their job. In other words, filing a claim would be very tempting to a person who is in debt or financial trouble, but performing poorly might not be a temptation. There is another argument, in addition to the legal one, for not using a non-valid test or predictor. Use of a non-valid test to select employees and predict their performance is practically no different than selecting employees randomly. Predictors can be expensive to develop and implement (Guion, 1998). Consequently, using a non-valid predictor wastes company financial resources. Credit Reports and Accuracy With the national trend to look at credit history for employment purposes, one serious limitation that needs to be considered is the prevalence of credit report inaccuracies. In fact, an alarming number of credit reports contain serious errors that can negatively affect consumers. The National Association of State Public Interest Research Groups (PIRG; Cassady & Mierzwinski, 2004; Golinger & Mierzwinski, 1998) collected credit report accuracy responses from 154 adults in 30 states, asking participants to obtain their credit report from one of the big three credit bureaus Equifax, Experian, and Trans-Union and complete a survey regarding the accuracy of information on the report. Results of the research were categorized into four types of errors: serious (those that could result in the denial of credit), inaccurate personal information, missing accounts,

17 122 KOPPES BRYAN AND PALMER and incorrectly open accounts. Twenty-five percent of those surveyed indicated serious errors on their credit report; 54% reported inaccurate personal information; 8% reported missing accounts; and 30% reported incorrectly open accounts. Overall, 79% of credit reports surveyed contained errors of some kind (Cassady & Mierzwinski, 2004). Why is such a large rate of mistakes being made on credit reports? Cassady and Mierzwinski (2004) listed several possibilities: (a) inaccurate reporting of demographic information by creditor, (b) failure of credit bureau to maintain adequate software, (c) inaccurate reporting of a consumer s account by creditor, (d) information mixed together by the credit bureau, and (e) lack of an adequate system for purging obsolete information. Some responses to the prevalence of mistakes have been taken, with little reported success. For instance, in 1996, Congress reformed the Fair Credit Reporting Act to place reporting accountability for accuracy on the information furnishers (creditors) and imposed the Operation Busy Signal, which required credit bureaus to have adequate staffing to handle consumer complaints. The Fair and Accurate Credit Transactions Act of 2003 (Fair and Accurate Credit Transactions Act, 2003) requires the Federal Trade Commission to track consumer complaints and complete a report (every 10 years) on accuracy of credit reports. Although some responses from regulators and the U.S. Congress have been made to improve credit report accuracy, recent survey results clearly show that this serious problem continues to prevail. STUDY LIMITATIONS Four limitations of the present study deserving notice are: (a) construct validity of the predictor (items in the credit reports), (b) construct validity of the performance and turnover criteria, (c) reliability of the predictor and criteria, and (d) external validity of the findings. One, the constructs that credit histories are purported to measure are conscientiousness or dependability and integrity or honesty; however, as stated earlier, we did not assess construct validity in this study, but proposed these constructs as the rationale for using credit histories. Two, we cannot be sure that the overall performance ratings and termination decisions captured the constructs of conscientiousness and integrity, but again, relied on the research to provide arguments for the use of credit histories. We strongly call for more research on the construct and criterion validity of credit history to be conducted. We are especially concerned about the assumptions between credit history and integrity, honesty, and conscientiousness, with no theoretical underpinnings. Three, the overall performance rating criterion as well as the predictors all constituted single numbers. Thus, there was no way to estimate the reliabilities for ratings and all credit history variables. As discussed in the conclusion below, there are factors that might profoundly change a credit report in a short amount of

18 EMPLOYEE CREDIT HISTORIES 123 time, thus reducing their reliability. Additionally, the organization did not provide any specific information about the development of the criterion and/or training of supervisors on rating employee performance. This information might provide some insight into the quality of this criterion. Four, the data were collected in one type of organization for a specific job category, thus, the external validity is limited with regard to generalizing the findings to other types of organizations and jobs. CONCLUSION The results raise the question: exactly what is a credit report measuring? Those who favor using credit history data as a predictor suggest that it might measure responsibility, the ability to meet deadlines, dependability (i.e., conscientiousness), integrity, and related employee characteristics. There may be sometruthtothis conceptually. However, credit history data likely also reflects many events outside a person s control. These might include, but are certainly not limited to, the effects of divorce, death, and accidents on a person s finances and ability to meet bill deadlines, past youthful naivety, as well as economic shocks (e.g., layoffs) for which an employee could not predict or prepare for. While intuitively a credit history is measuring responsibility and the ability to meet deadlines, in practice this measure is probably contaminated by many other factors outside an employee s control, as well as inaccuracies. Thus, our data indicate there is no benefit from using credit history to predict employee performance or turnover. This research has significant policy implications in light of the current economic crisis in which Bankruptcy Filings Rise to 6,000 a Day as Job Losses Take a Toll (Dugas, 2009, June) and the number of mortgage delinquencies and foreclosures continue to climb (Howley, 2010). REFERENCES Aho, K. (2009). Is bad credit a character flaw? MSN Money. Retrieved from moneycentral.msn.com/banking/yourcreditrating/does-bad-credit-make-you-a-bad-person.aspx? page=1 Allen, J. L., & Jover, M. A. (1997). Credit card behaviors of university students: Ethnic differences. Consumer Interests Annual, 43, 162. Anderson,J. (2008).Filings for bankruptcy up 18%in February.The New York Times. Retrieved from Arnoldy, B. (2007). The spread of the credit check as a civil rights issue. Minorities are starting to fight employers over the use of credit history in hiring. Christian Science Monitor. Retrieved from Barrick, M. R., & Mount, M. K. (1991). The big five personality dimension and job performance: A meta-analysis. Personnel Psychology, 44, 1 26.

19 124 KOPPES BRYAN AND PALMER Barron, J. M., Elliehausen, G., & Staten, M. E. (2000). Monitoring the household sector with aggregate credit bureau data. Business Economics, 35(1), Barth, S. C. (2002). STEM the tide by selecting the right employee. Lodging Hospitality, 57(13), 12. Berry, C. M., Sackett, P. R., & Wiemann, S. (2007). A review of recent developments in integrity test research. Personnel Psychology, 60, Borman, W. C., & Hanson, M. A. (1997). Personnel selection. Annual Review of Psychology, 48, Borman, W. C., Penner, L. A., Allen, T. D., & Motowidlo, S. J. (2001). Personality predictors of citizenship performance. International Journal of Selection and Assessment, 9, Borman, W. C., White. L. A., Pulakos, E. D., & Oppler, S. H. (1991). Models of supervisory job performance ratings. Journal of Applied Psychology, 76, Camara, W. J., & Schneider, D. L. (1994). Integrity tests: Facts and unresolved issues. American Psychologist, 49, Campbell, J. P., McCloy, R. A., Oppler, S. H., & Sager, C. E. (1993). A theory of performance. In N. Schmitt & W. C. Borman (Eds.), Personnel selection in organizations (pp ). San Francisco: Jossey Bass. Cassady, M., & Mierzwinski, E. (2004). Mistakes do happen: A look at errors in consumer credit reports. Washington, DC: National Association of State PIRGs. Cavanaugh, M. A., Boswell, W. R., Roehling, M. V., & Boudreau, J. W. (2000). An empirical examination of self-reported work stress among U.S. managers. Journal of Applied Psychology, 85, Chen, P. Y, & Spector, P. E. (1992). Relationships of work stressors with aggression, withdrawal, theft and substance use: An exploratory study. Journal of Occupational and Organizational Psychology, 65, Civil Rights Act of 1964, Title VII, 42 U.S.C. 2000e. Civil Rights Act of 1991, 42, U.S.C. 1981A. Consumer Alert: Can t Get Insurance? Maybe it s Because of Your Credit Report. (1997). Consumer Reports, 62(2), p. 8. Costa, P. T., & McCrae, R. R. (1988). From catalogue to classification: Murray s needs and the fivefactor model. Journal of Personality and Social Psychology, 55, Denston, C. P. (2010, February 25). Statement of Colleen Parker Denston, Government Affairs Director Maryland Society for Human Resource Management. Presentation to the Senate Finance Committee. Retrieved from Documents/MD%20SHRM%20testimony%20RE%20credit%20reports%20SENATE.pdf Dugas, C. (2009). Bankruptcy filings rise to 6,000 a day as job losses take toll. USA Today. Retrieved from Equal Employment Opportunity Commission. (2007, May 16). Statement of Adam T. Klein, Esq. Meeting on Employment Testing and Screening of the U.S. Equal Employment Opportunity Commission. Retrieved from Esen, E. (2004). SHRM workplace violence survey. Alexandria, VA.: Society for Human Resource Management. Fair and Accurate Credit Transactions Act (2003). Federal Register, 69(121). Fair Credit Reporting Act. (2002). 15 U.S.C et seq. Federal Trade Commission (2002).Using consumer reports: What employers need to know. Retrieved from Fine, S., Horowitz, I., Weigler, H., & Basis, L. (2010). Is good character good enough? The effects of situational variables on the relationship between integrity and counterproductive work behaviors. Human Resource Management Review, 20(1), Frank, T. (2009). 5 states challenge employer credit checks. USA Today. Retrieved from usatoday.com/money/perfi/credit/ creditchecks_n.htm?poe=click-refer

20 EMPLOYEE CREDIT HISTORIES 125 Gatewood, R. D., & Feild, H. S. (2001). Human resource selection (5th ed.). New York: Harcourt. Golinger, J., & Mierzwinski, E. (1998). Mistakes do happen: Credit report errors mean consumers lose. Washington, DC: National Association of State PIRGs. Green v. Missouri Pacific Railroad (CA8 1975). 523 F.2d Griffeth, R. W., Hom, P. W., & Gaertner, S. (2000). A meta-analysis of antecedents and correlates of employee turnover: Update, moderator tests, and research implications for the next millennium. Journal of Management, 26, Guion,R.M. (1998).Assessment, measurement, and prediction for personnel decisions. Hillsdale, NJ: Erlbaum. Gutman, A., Koppes, L. L., & Vodanovich, S. (2011). EEO law and personnel practices (3rd ed.). New York: Taylor & Francis. Hogan, J., & Ones, D. S. (1997). Conscientiousness and integrity at work. In R. Hogan, J. A. Johnson, & S. R. Briggs (Eds.), Handbook of personality psychology (pp ). San Diego: Academic Press. Hollinger, R. C., & Clark, J. P. (1983). Theft by employees. Lexington, MA: Lexington. Hollinger, R. C., & Davis, J. L. (2002) national retail security survey final report. Gainesville: University of Florida. Hough, L. M., & Oswald, F. L. (2000). Personnel selection. Annual Review of Psychology, 51, Howie, R. M., & Shapero, L. A. (2002). Pre-employment criminal background checks: Why employers should look before they leap. Employee Relations Law Journal, 28, Howley, K. (2010). Prime U.S. mortgage foreclosures hit record as unemployment hurts finances. Bloomberg.com. Retrieved from Industry Representatives Oppose Bill in Vermont to Bar use of Credit Reports in Underwriting. (1999). Insurance Advocate, 110(6), 27. How Your Credit History Affects Your Auto and Home Insurance Premiums. (2002). Insure.com. Retrieved from Joel, L. G., III. (1996). Every employee s guide to the law. New York: Pantheon. Kent, S. E., & Baum, P. A. (2000). Credit check. Best s Review, December, Mantell, M. (1994). Ticking bombs: Defusing violence in the work-place. Burr Ridge, IL: Irwin. Morris, S. B., & McDaniel, M. A. (1995). Vanity-motivated overspending: Personal screening for positions of trust. Educational and Psychological Measurement, 55, Motowidlo, S. J. (2003). Job performance. In W. C. Borman, D. R. Ilgen, & R. J. Klimoski (Vol. Eds.), Industrial and organizational psychology, Vol. 12, Handbook of psychology (pp ). Hoboken, NJ: John Wiley & Sons, Inc. Muchinsky, P. M., & Skilling, N. J. L. (1992). Utility analysis of five weighting methods for making consumer loan decisions. Educational and Psychological Measurement, 52, Musulin, R. T. (2002, August 16). Let s give credit where credit is due. National Underwriter, 106(34), 33. Palmer, J. K., & Koppes, L. L. (2003, August). Further investigation of credit history as a predictor of employee turnover. Paper presented at the Annual Meeting of the American Psychological Society, Atlanta, GA. Palmer, J. K., & Koppes, L. L. (2004, April). Investigation of credit history validity at predicting performance and turnover. Paper presented at the Annual Conference of the Society for Industrial and Organizational Psychology, Chicago, IL. Poltenson, N. (1996). Credit scoring: The next civil-rights confrontation. Business Journal Serving Southern Tier, CNY, Mohawk Valley, Finger Lakes, North, 10,4. Rosenberg, R. (2007). EEOC takes aim at employment credit reports. Asset Control. Retrieved from

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