prompt_front: You are given two reports by the government agency.



Question: Write a one-page summary of the report 1.



Report 2: The LDA requires lobbyists to register with the Secretary of the Senate and the Clerk of the House and to file quarterly reports disclosing their respective lobbying activities  Lobbyists are required to file their registrations and reports electronically with the Secretary of the Senate and the Clerk of the House through a single entry point  Registrations and reports must be publicly available in downloadable, searchable databases from the Secretary of the Senate and the Clerk of the House  No specific statutory requirements exist for lobbyists to generate or maintain documentation in support of the information disclosed in the reports they file  However, guidance issued by the Secretary of the Senate and the Clerk of the House recommends that lobbyists retain copies of their filings and documentation supporting reported income and expenses for at least 6 years after they file their reports  The LDA requires that the Secretary of the Senate and the Clerk of the House guide and assist lobbyists with the registration and reporting requirements and develop common standards, rules, and procedures for LDA compliance  The Secretary of the Senate and the Clerk of the House review the guidance semiannually  It was last revised January 31, 2017, to (among other issues) update the registration threshold to reflect changes in the Consumer Price Index, and clarify the identification of clients and covered officials and issues related to rounding income and expenses  The guidance provides definitions of LDA terms, elaborates on registration and reporting requirements, includes specific examples of different scenarios, and provides explanations of why certain scenarios prompt or do not prompt disclosure under the LDA  The offices of the Secretary of the Senate and the Clerk of the House told us they continue to consider information we report on lobbying disclosure compliance when they periodically update the guidance  In addition, they told us they e-mail registered lobbyists quarterly on common compliance issues and reminders to file reports by the due dates  The LDA defines a lobbyist as an individual who is employed or retained by a client for compensation, who has made more than one lobbying contact (written or oral communication to covered officials, such as a high ranking agency official or a Member of Congress made on behalf of a client), and whose lobbying activities represent at least 20 percent of the time that he or she spends on behalf of the client during the quarter  Lobbying firms are persons or entities that have one or more employees who lobby on behalf of a client other than that person or entity  Figure 1 provides an overview of the registration and filing process  Lobbying firms are required to register with the Secretary of the Senate and the Clerk of the House for each client if the firms receive or expect to receive more than $3,000 in income from that client for lobbying activities  Lobbyists are also required to submit an LD-2 quarterly report for each registration filed  The LD-2s contain information that includes: the name of the lobbyist reporting on quarterly lobbying activities; the name of the client for whom the lobbyist lobbied; a list of individuals who acted as lobbyists on behalf of the client during the reporting period; whether any lobbyists served in covered positions in the executive or legislative branch such as high-ranking agency officials or congressional staff positions, in the previous 20 years; codes describing general issue areas, such as agriculture and education; a description of the specific lobbying issues; houses of Congress and federal agencies lobbied during the reporting reported income (or expenses for organizations with in-house lobbyists) related to lobbying activities during the quarter (rounded to the nearest $10,000)  The LDA also requires lobbyists to report certain political contributions semiannually in the LD-203 report  These reports must be filed 30 days after the end of a semiannual period by each lobbying firm registered to lobby and by each individual listed as a lobbyist on a firm’s lobbying report  The lobbyists or lobbying firms must: list the name of each federal candidate or officeholder, leadership political action committee, or political party committee to which he or she contributed at least $200 in the aggregate during the semiannual period; report contributions made to presidential library foundations and presidential inaugural committees; report funds contributed to pay the cost of an event to honor or recognize an official who was previously in a covered position, funds paid to an entity named for or controlled by a covered official, and contributions to a person or entity in recognition of an official, or to pay the costs of a meeting or other event held by or in the name of a covered official; and certify that they have read and are familiar with the gift and travel rules of the Senate and House and that they have not provided, requested, or directed a gift or travel to a member, officer, or employee of Congress that would violate those rules  The Secretary of the Senate and the Clerk of the House, along with USAO, are responsible for ensuring LDA compliance  The Secretary of the Senate and the Clerk of the House notify lobbyists or lobbying firms in writing that they are not complying with the LDA reporting  Subsequently, they refer those lobbyists who fail to provide an appropriate response to USAO  USAO researches these referrals and sends additional noncompliance notices to the lobbyists or lobbying firms, requesting that they file reports or terminate their registration  If USAO does not receive a response after 60 days, it decides whether to pursue a civil or criminal case against each noncompliant lobbyist  A civil case could lead to penalties up to $200,000 for each violation, while a criminal case—usually pursued if a lobbyist’s noncompliance is found to be knowing and corrupt—could lead to a maximum of 5 years in prison  Generally, under the LDA, within 45 days of being employed or retained to make a lobbying contact on behalf of a client, the lobbyist must register by filing an LD-1 form with the Secretary of the Senate and the Clerk of the House  Thereafter, the lobbyist must file quarterly disclosure (LD-2) reports detailing the lobbying activities  Of the 3,433 new registrations we identified for the third and fourth quarters of 2016 and the first and second quarters of 2017, we matched 2,995 of them (87 2 percent) to corresponding LD-2 reports filed within the same quarter as the registration  These results are consistent with the findings we have reported in prior reviews  We used the House lobbyists’ disclosure database as the source of the reports  We also used an electronic matching algorithm that allows for misspellings and other minor inconsistencies between the registrations and reports  Figure 2 shows lobbyists filed disclosure reports as required for most new lobbying registrations from 2010 through 2017  For selected elements of lobbyists’ LD-2 reports that can be generalized to the population of lobbying reports, our findings have generally been consistent from year to year  Most lobbyists reporting $5,000 or more in income or expenses provided written documentation to varying degrees for the reporting elements in their disclosure reports  Figure 3 shows that for most LD-2 reports, lobbyists provided documentation for income and expenses for sampled reports from 2010 through 2017  However, in recent years our findings showed some variation in the estimated percentage of lobbyists who have reports with documentation for income and expense supporting lobbying activities  Specifically, our estimate for 2017 (99 percent) represents a statistically significant increase from 2016  Figure 4 shows that for some LD-2 reports, lobbyists did not round their income or expenses as the guidance requires  In 2017, we estimate 25 percent of reports did not round reported income or expenses according to the guidance  We have found that rounding difficulties have been a recurring issue on LD-2 reports from 2010 through 2017  As we previously reported, several lobbyists who listed expenses told us that based on their reading of the LD-2 form they believed they were required to report the exact amount  While this is not consistent with the LDA and the guidance, this may be a source of some of the confusion regarding rounding errors  In 2016, the guidance was updated to include an additional example about rounding expenses to the nearest $10,000  In 2017, 11 percent of lobbyists reported $10,000 or more in income or expenses  The LDA requires lobbyists to disclose lobbying contacts made with federal agencies on behalf of the client for the reporting period  This year, of the 98 LD-2 reports in our sample, 51 reports disclosed lobbying activities at federal agencies  Of those, lobbyists provided documentation for all lobbying activities at executive branch agencies for 34 LD-2 reports  Figures 5 through 8 show that lobbyists for most LD-2 reports provided documentation for selected elements of their LD-2 reports from 2010 through 2017  Lobbyists for an estimated 93 percent of LD-2 reports filed year-end 2016 for all lobbyists listed political contributions on the report as required  Figure 9 shows that lobbyists for most lobbying firms filed contribution reports as required in our sample from 2010 through 2017  All individual lobbyists and lobbying firms reporting lobbying activity are required to file LD-203 reports semiannually, even if they have no contributions to report, because they must certify compliance with the gift and travel rules  The LDA requires a lobbyist to disclose previously held covered positions in the executive or legislative branch, such as high ranking agency officials and congressional staff, when first registering as a lobbyist for a new client  This can be done either on a new LD-1 or on the quarterly LD- 2 filing when added as a new lobbyist  This year, we estimate that 15 percent of all LD-2 reports may not have properly disclosed previously held covered positions as required  As in our other reports, some lobbyists were still unclear about the need to disclose certain covered positions, such as paid congressional internships or certain executive agency positions  Figure 10 shows the extent to which lobbyists may not have properly disclosed one or more covered positions as required from 2010 through 2017  Lobbyists amended 15 of the 98 LD-2 disclosure reports in our original sample to change previously reported information after we contacted them  Of the 15 reports, 7 were amended after we notified the lobbyists of our review, but before we met with them  An additional 8 of the 15 reports were amended after we met with the lobbyists to review their documentation  We consistently find a notable number of amended LD-2 reports in our sample each year following notification of our review  This suggests that sometimes our contact spurs lobbyists to more closely scrutinize their reports than they would have without our review  Table 1 lists reasons lobbying firms in our sample amended their LD-1 or LD-2 reports  As part of our review, we compared contributions listed on lobbyists’ and lobbying firms’ LD-203 reports against those political contributions reported in the Federal Election Commission (FEC) database to identify whether political contributions were omitted on LD-203 reports in our sample  The sample of LD-203 reports we reviewed contained 80 reports with contributions and 80 reports without contributions  We estimate that overall for 2017, lobbyists failed to disclose one or more reportable contributions on 12 percent of reports  Additionally, ten LD-203 reports were amended in response to our review  For this element in prior reports, we reported an estimated minimum percentage of reports based on a one-sided 95 percent confidence interval rather than the estimated proportion as shown here  Estimates in the table have a maximum margin of error of 11 percentage points  The year to year differences are not statistically significant  Table 2 illustrates that from 2010 through 2017 most lobbyists disclosed FEC reportable contributions on their LD-203 reports as required  As part of our review, 88 different lobbying firms were included in our 2017 sample of LD-2 disclosure reports  Consistent with prior reviews, most lobbying firms reported that they found it “very easy” or “somewhat easy” to comply with reporting requirements  Of the 88 different lobbying firms in our sample, 34 reported that the disclosure requirements were “very easy,” 40 reported them “somewhat easy,” and 13 reported them “somewhat difficult” or “very difficult” (see figure 11)  Most lobbying firms we surveyed rated the definitions of terms used in LD-2 reporting as “very easy” or “somewhat easy” to understand with regard to meeting their reporting requirements  This is consistent with prior reviews  Figures 12 through 16 show what lobbyists reported as their ease of understanding the terms associated with LD-2 reporting requirements from 2012 through 2017  U S  Attorney’s Office (USAO) officials stated that they continue to have sufficient personnel resources and authority under the LDA to enforce reporting requirements  This includes imposing civil or criminal penalties for noncompliance  Noncompliance refers to a lobbyist’s or lobbying firm’s failure to comply with the LDA  However, USAO noted that the number of assigned personnel has decreased due to attrition  USAO officials stated that lobbyists resolve their noncompliance issues by filing LD-2, LD-203, or LD-2 amendments, or by terminating their registration, depending on the issue  Resolving referrals can take anywhere from a few days to years, depending on the circumstances  During this time, USAO creates summary reports from its database to track the overall number of referrals that are pending or become compliant as a result of the lobbyist receiving an e-mail, phone call, or noncompliance letter  Referrals remain in the pending category until they are resolved  The pending category is divided into the following areas: “initial research for referral,” “responded but not compliant,” “no response/waiting for a response,” “bad address,” and “unable to locate ” The USAO attempts to review and update all pending cases every six months  USAO focuses its enforcement efforts primarily on the “responded but not compliant” and the “no response/waiting for a response” groups  Officials told us that, if the USAO, after several unsuccessful attempts, has been unsuccessful in contacting the non-compliant firm or its lobbyist, USAO confers with both the Secretary of the Senate and the Clerk of the House to determine whether further action is needed  In the cases where the lobbying firm is repeatedly referred for not filing disclosure reports but does not appear to be actively lobbying, USAO suspends enforcement actions  USAO officials reported they will continue to monitor these firms and will resume enforcement actions if required  USAO received 3,213 referrals from both the Secretary of the Senate and the Clerk of the House for failure to comply with LD-2 reporting requirements cumulatively for filing years 2009 through 2015  Table 4 shows the number and status of the referrals received and the number of enforcement actions taken by USAO to bring lobbying firms into compliance  Enforcement actions include USAO attempts to bring lobbyists into compliance through letters, e-mails, and calls  About 45 percent (1,450 of 3,213) of the total referrals received are now compliant because lobbying firms either filed their reports or terminated their registrations  In addition, some of the referrals were found to be compliant when USAO received the referral  Therefore, no action was taken  This may occur when lobbying firms respond to the contact letters from the Secretary of the Senate and the Clerk of the House after USAO received the referrals  About 55 percent (1,752 of 3,213) of referrals are pending further action because USAO could not locate the lobbying firm, did not receive a response from the firm after an enforcement action, or plans to conduct additional research to determine if it can locate the lobbying firm  The remaining 11 referrals did not require action or were suspended because the lobbyist or client was no longer in business or the lobbyist was deceased  LD-203 referrals consist of two types: (1) LD-203(R) referrals represent lobbying firms that have failed to file LD-203 reports for their lobbying firm and (2) LD-203 referrals represent the lobbyists at the lobbying firm who have failed to file their individual LD-203 reports as required  USAO received 2,255 LD-203(R) referrals (cumulatively from 2009 through 2015) and 3,716 LD-203 referrals (cumulatively from 2009 through 2014 from the Secretary of the Senate and the Clerk of the House for lobbying firms and lobbyists for noncompliance with reporting requirements)  LD- 203 referrals are more complicated than LD-2 referrals because both the lobbying firm and the individual lobbyists within the firm are each required to file a LD-203  Lobbyists employed by a lobbying firm typically use the firm’s contact information and not the lobbyists’ personal contact information  This makes it difficult to locate a lobbyist who is not in compliance and may have left the firm  USAO officials reported that, while many firms have assisted USAO by providing contact information for lobbyists, they are not required to do so  According to officials, USAO has difficulty pursuing LD-203 referrals for lobbyists who have departed a firm without leaving forwarding contact information with the firm  While USAO utilizes web searches and online databases, including social media, to find these missing lobbyists, it is not always successful  Table 5 shows the status of LD-203 (R) referrals received and the number of enforcement actions taken by USAO to bring lobbying firms into compliance  A little more than 44 percent (998 of 2,255) of the lobbying firms referred by the Secretary of the Senate and Clerk of the House for noncompliance from calendar years 2009 through 2015 are now considered compliant because firms either filed their reports or terminated their registrations  About 56 percent (1,251 of 2,255) of the referrals are pending further action  Table 6 shows that USAO received 3,716 LD-203 referrals from the Secretary of the Senate and Clerk of the House for lobbyists who failed to comply with LD-203 reporting requirements for calendar years 2009 through 2014  It also shows the status of the referrals received and the number of enforcement actions taken by USAO to bring lobbyists into compliance  In addition, table 6 shows that about 47 percent (1,741 of 3,716) of the lobbyists had come into compliance by filing their reports or are no longer registered as a lobbyist  About 53 percent (1,966 of 3,716) of the referrals are pending further action because USAO could not locate the lobbyist, did not receive a response from the lobbyist, or plans to conduct additional research to determine if it can locate the lobbyist  Table 7 shows that USAO received LD-203 referrals from the Secretary of the Senate and the Clerk of the House for 4,991 lobbyists who failed to comply with LD-203 reporting requirements for any filing year from 2009 through 2014  It also shows the status of compliance for individual lobbyists listed on referrals to USAO  About 51 percent (2,526 of 4,991) of the lobbyists had come into compliance by filing their reports or are no longer registered as a lobbyist  About 50 percent (2,465 of 4,991) of the referrals are pending action because USAO could not locate the lobbyists, did not receive a response from the lobbyists, or plans to conduct additional research to determine if it can locate the lobbyists  USAO officials said that many of the pending LD-203 referrals represent lobbyists who no longer lobby for the lobbying firms affiliated with the referrals, even though these lobbying firms may be listed on the lobbyist’s LD-203 report  According to USAO officials, lobbyists and lobbying firms who repeatedly fail to file reports are labeled chronic offenders and referred to one of the assigned attorneys for follow-up  USAO also receives complaints regarding lobbyists who are allegedly lobbying but never filed an LD-203  USAO officials added that USAO monitors and investigates chronic offenders to ultimately determine the appropriate enforcement actions, which may include settlement or other civil actions  In regards to the four active cases involving chronic offenders they reported to us in 2016, USAO officials noted that the agency is investigating one case, negotiating a resolution that will include a civil penalty in another case, and closing two other investigations without further action  In addition, USAO is reviewing its records to identify additional chronic offenders for further action due to noncompliance  We provided a draft of this report to the Department of Justice for review and comment  The Department of Justice provided technical comments, which we incorporated as appropriate  We are sending copies of this report to the Attorney General, Secretary of the Senate, Clerk of the House of Representatives, and interested congressional committees and members  In addition, this report is available at no charge on the GAO website at http://www gao gov  If you or your staff have any questions about this report, please contact me at (202) 512-2717 or jonesy@gao gov  Contact points for our Offices of Congressional Relations and Public Affairs may be found on the last page of this report  GAO staff who made major contributions to this report are listed in appendix IV  Our objectives were to determine the extent to which lobbyists are able to demonstrate compliance with the Lobbying Disclosure Act of 1995, as amended (LDA) by providing documentation to support information contained on registrations and reports filed under the LDA; to identify challenges and potential improvements to compliance, if any; and to describe the resources and authorities available to the U S  Attorney’s Office for the District of Columbia (USAO), its role in enforcing LDA compliance, and the efforts it has made to improve LDA enforcement  We used information in the lobbying disclosure database maintained by the Clerk of the House of Representatives (Clerk of the House)  To assess whether these disclosure data were sufficiently reliable for the purposes of this report, we reviewed relevant documentation and consulted with knowledgeable officials  Although registrations and reports are filed through a single web portal, each chamber subsequently receives copies of the data and follows different data-cleaning, processing, and editing procedures before storing the data in either individual files (in the House) or databases (in the Senate)  Currently, there is no means of reconciling discrepancies between the two databases caused by the differences in data processing  For example, Senate staff told us during previous reviews they set aside a greater proportion of registration and report submissions than the House for manual review before entering the information into the database  As a result, the Senate database would be slightly less current than the House database on any given day pending review and clearance  House staff told us during previous reviews that they rely heavily on automated processing  In addition, while they manually review reports that do not perfectly match information on file for a given lobbyist or client, staff members approve and upload such reports as originally filed by each lobbyist, even if the reports contain errors or discrepancies (such as a variant on how a name is spelled)  Nevertheless, we do not have reasons to believe that the content of the Senate and House systems would vary substantially  Based on interviews with knowledgeable officials and a review of documentation, we determined that House disclosure data were sufficiently reliable for identifying a sample of quarterly disclosure reports (LD-2) and for assessing whether newly filed lobbyists also filed required reports  We used the House database for sampling LD-2 reports from the third and fourth quarters of 2016 and the first and second quarters of 2017, as well as for sampling year-end 2016 and midyear 2017 political contributions reports (LD-203)  We also used the database for matching quarterly registrations with filed reports  We did not evaluate the Offices of the Secretary of the Senate or the Clerk of the House, both of which have key roles in the lobbying disclosure process  However, we did consult with officials from each office  They provided us with general background information at our request  To assess the extent to which lobbyists could provide evidence of their compliance with reporting requirements, we examined a stratified random sample of 98 LD-2 reports from the third and fourth quarters of 2016 and the first and second quarters of 2017  The sample size of 98 LD-2 reports for this year’s review represents an increase from the sample size selected for the 2015 and 2016 reviews, and is a return to the sample size selected in reviews prior to 2015  We increased the sample size because, in 2016, we observed a change in the estimate of the percentage of reports that had documentation of income and expenses (83 percent down from 92 percent in 2015)  At that time, we were unable to state that this was a statistically significant change because, in part, the reduced sample size of 80 did not give us enough power to detect and report on the change of that size  We excluded reports with no lobbying activity or with income or expenses of less than $5,000 from our sampling frame  We drew our sample from 45,818 activity reports filed for the third and fourth quarters of 2016 and the first and second quarters of 2017 available in the public House database, as of our final download date for each quarter  Our sample of LD-2 reports was not designed to detect differences over time  However, we conducted tests of significance for changes from 2010 to 2017 for the generalizable elements of our review  We found that results were generally consistent from year to year and there were few statistically significant changes after using a Bonferroni adjustment to account for multiple comparisons  For this year’s review, we identified that the estimated change in the percent of LD-2 reports that provided written documentation for the income and expenses from 2016 to 2017 is notable  In recent years, our findings show some variation in the estimate percentage of reports with documentation  Specifically, our estimate for 2017 (99 percent) represents a statistically significant increase from 2016  These changes are identified in the report  The inability to detect significant differences from year to year in our results may be related to sampling error alone or the nature of our sample, which was relatively small and was designed only for cross-sectional analysis  Our sample is based on a stratified random selection and is only one of a large number of samples that we may have drawn  Because each sample could have provided different estimates, we express our confidence in the precision of our particular sample’s results as a 95 percent confidence interval  This interval would contain the actual population value for 95 percent of the samples that we could have drawn  The percentage estimates for LD-2 reports have 95 percent confidence intervals of within plus or minus 12 percentage points or fewer of the estimate itself  We contacted all the lobbyists and lobbying firms in our sample and, using a structured web-based survey, asked them to confirm key elements of the LD-2 and whether they could provide written documentation for key elements in their reports, including the amount of income reported for lobbying activities; the amount of expenses reported on lobbying activities; the names of those lobbyists listed in the report; the houses of Congress and federal agencies that they lobbied, and the issue codes listed to describe their lobbying activity  After reviewing the survey results for completeness, we interviewed lobbyists and lobbying firms to review the documentation they reported as having on their online survey for selected elements of their respective LD- 2 report  Prior to each interview, we conducted a search to determine whether lobbyists properly disclosed their covered position as required by the LDA  We reviewed the lobbyists’ previous work histories by searching lobbying firms’ websites, LinkedIn, Leadership Directories, Legistorm, and Google  Prior to 2008, lobbyists were only required to disclose covered official positions held within 2 years of registering as a lobbyist for the client  The Honest Leadership and Open Government Act of 2007 amended that time frame to require disclosure of positions held 20 years before the date the lobbyists first lobbied on behalf of the client  Lobbyists are required to disclose previously held covered official positions either on the client registration (LD-1) or on an LD-2 report  Consequently, those who held covered official positions may have disclosed the information on the LD-1 or a LD-2 report filed prior to the report we examined as part of our random sample  Therefore, where we found evidence that a lobbyist previously held a covered official position, and that information was not disclosed on the LD-2 report under review, we conducted an additional review of the publicly available Secretary of the Senate or Clerk of the House database to determine whether the lobbyist properly disclosed the covered official position on a prior report or LD-1  Finally, if a lobbyist appeared to hold a covered position that was not disclosed, we asked for an explanation at the interview with the lobbying firm to ensure that our research was accurate  In previous reports, we reported the lower bound of a 90 percent confidence interval to provide a minimum estimate of omitted covered positions and omitted contributions with a 95 percent confidence level  We did so to account for the possibility that our searches may have failed to identify all possible omitted covered positions and contributions  As we have developed our methodology over time, we are more confident in the comprehensiveness of our searches for these items  Accordingly, this report presents the estimated percentages for omitted contributions and omitted covered positions, rather than the minimum estimates  As a result, percentage estimates for these items will differ slightly from the minimum percentage estimates presented in prior reports  In addition to examining the content of the LD-2 reports, we confirmed whether the most recent LD-203 reports had been filed for each firm and lobbyist listed on the LD-2 reports in our random sample  Although this review represents a random selection of lobbyists and firms, it is not a direct probability sample of firms filing LD-2 reports or lobbyists listed on LD-2 reports  As such, we did not estimate the likelihood that LD-203 reports were appropriately filed for the population of firms or lobbyists listed on LD-2 reports  To determine if the LDA’s requirement for lobbyists to file a report in the quarter of registration was met for the third and fourth quarters of 2016 and the first and second quarters of 2017, we used data filed with the Clerk of the House to match newly filed registrations with corresponding disclosure reports  Using an electronic matching algorithm that includes strict and loose text matching procedures, we identified matching disclosure reports for 2,995, or 87 2 percent, of the 3,433 newly filed registrations  We began by standardizing client and lobbyist names in both the report and registration files (including removing punctuation and standardizing words and abbreviations, such as “company” and “CO”)  We then matched reports and registrations using the House identification number (which is linked to a unique lobbyist-client pair), as well as the names of the lobbyist and client  For reports we could not match by identification number and standardized name, we also attempted to match reports and registrations by client and lobbyist name, allowing for variations in the names to accommodate minor misspellings or typos  For these cases, we used professional judgment to determine whether cases with typos were sufficiently similar to consider as matches  We could not readily identify matches in the report database for the remaining registrations using electronic means  To assess the accuracy of the LD-203 reports, we analyzed stratified random samples of LD-203 reports from the 30,594 total LD-203 reports  The first sample contains 80 reports of the 9,474 reports with political contributions and the second contains 80 reports of the 20,335 reports listing no contributions  Each sample contains 40 reports from the year- end 2016 filing period and 40 reports from the midyear 2017 filing period  The samples from 2017 allow us to generalize estimates in this report to either the population of LD-203 reports with contributions or the reports without contributions to within a 95 percent confidence interval of within plus or minus 11 percentage points or fewer  Although our sample of LD- 203 reports was not designed to detect differences over time, we conducted tests of significance for changes from 2010 to 2017 and found no statistically significant differences after adjusting for multiple comparisons  While the results provide some confidence that apparent fluctuations in our results across years are likely attributable to sampling error, the inability to detect significant differences may also be related to the nature of our sample, which was relatively small and designed only for cross- sectional analysis  We analyzed the contents of the LD-203 reports and compared them to contribution data found in the publicly available Federal Elections Commission’s (FEC) political contribution database  We consulted with staff at FEC responsible for administering the database  We determined that the data are sufficiently reliable for the purposes of our reporting objectives  We compared the FEC-reportable contributions on the LD-203 reports with information in the FEC database  The verification process required text and pattern matching procedures so we used professional judgment when assessing whether an individual listed is the same individual filing an LD-203  For contributions reported in the FEC database and not on the LD-203 report, we asked the lobbyists or organizations to explain why the contribution was not listed on the LD-203 report or to provide documentation of those contributions  As with covered positions on LD-2 disclosure reports, we cannot be certain that our review identified all cases of FEC-reportable contributions that were inappropriately omitted from a lobbyist’s LD-203 report  We did not estimate the percentage of other non-FEC political contributions that were omitted because they tend to constitute a small minority of all listed contributions and cannot be verified against an external source  To identify challenges to compliance, we used a structured web-based survey and obtained the views from 88 different lobbying firms included in our sample on any challenges to compliance  The number of different lobbying firms is 88, which is less than our original sample of 98 reports because some lobbying firms had more than one LD-2 report included in our sample  We calculated responses based on the number of different lobbying firms that we contacted rather than the number of interviews  Prior to our calculations, we removed the duplicate lobbying firms based on the most recent date of their responses  For those cases with the same response date, the decision rule was to keep the cases with the smallest assigned case identification number  To obtain their views, we asked them to rate their ease with complying with the LD-2 disclosure requirements using a scale of “very easy,” “somewhat easy,” “somewhat difficult,” or “very difficult ” In addition, using the same scale we asked them to rate the ease of understanding the terms associated with LD-2 reporting requirements  To describe the resources and authorities available to the U S  Attorney’s Office for the District of Columbia (USAO) and its efforts to improve its LDA enforcement, we interviewed USAO officials  We obtained information on the capabilities of the system officials established to track and report compliance trends and referrals and on other practices established to focus resources on LDA enforcement  USAO provided us with reports from the tracking system on the number and status of referrals and chronically noncompliant lobbyists and lobbying firms  The mandate does not require us to identify lobbyists who failed to register and report in accordance with the LDA requirements, or determine for those lobbyists who did register and report whether all lobbying activity or contributions were disclosed  Therefore, this was outside the scope of our audit  We conducted this performance audit from April 2017 to March 2018 in accordance with generally accepted government auditing standards  Those standards require that we plan and perform the audit to obtain sufficient, appropriate evidence to provide a reasonable basis for our findings and conclusions based on our audit objectives  We believe that the evidence obtained provides a reasonable basis for our findings and conclusions based on our audit objectives  The random sample of lobbying disclosure reports we selected was based on unique combination of House ID, lobbyist, and client names (see table 8)  See table 9 for a list of the lobbyists and lobbying firms from our random sample of lobbying contribution reports with contributions  See table 10 for a list of the lobbyists and lobbying firms from our random sample of lobbying contribution reports without contributions  In addition to the contact named above, Clifton G  Douglas Jr  (Assistant Director), Shirley Jones (Assistant General Counsel) and Ulyana Panchishin (Analyst-In-Charge) supervised the development of this report  James Ashley, Ann Czapiewski, Krista Loose, Kathleen Jones, Amanda Miller, Sharon Miller, Stewart W  Small, and Kayla L  Robinson made key contributions to this report  Assisting with lobbyist file reviews were Justine Augeri, Matthew Bond, James A  Howard, Jesse Jordan, Sherrice Kerns, Dalton Matthew Lauderback, Alexandria Palmer, Alan Rozzi, Shane Spencer, Jessica Walker, Ralanda Winborn, and Kate Wulff  Lobbying Disclosure: Observations on Lobbyists’ Compliance with New Disclosure Requirements  GAO-08-1099  Washington, D C: September 30, 2008  2008 Lobbying Disclosure: Observations on Lobbyists’ Compliance with Disclosure Requirements  GAO-09-487  Washington, D C: April 1, 2009  2009 Lobbying Disclosure: Observations on Lobbyists’ Compliance with Disclosure Requirements  GAO-10-499  Washington, D C: April 1, 2010  2010 Lobbying Disclosure: Observations on Lobbyists’ Compliance with Disclosure Requirements  GAO-11-452  Washington, D C: April 1, 2011  2011 Lobbying Disclosure: Observations on Lobbyists’ Compliance with Disclosure Requirements  GAO-12-492  Washington, D C: March 30, 2012  2012 Lobbying Disclosure: Observations on Lobbyists’ Compliance with Disclosure Requirements  GAO-13-437  Washington, D C: April 1, 2013  2013 Lobbying Disclosure: Observations on Lobbyists’ Compliance with Disclosure Requirements  GAO-14-485  Washington, D C: May 28, 2014  2014 Lobbying Disclosure: Observations on Lobbyists’ Compliance with Disclosure Requirements  GAO-15-310  Washington, D C : March 26, 2015  2015 Lobbying Disclosure: Observations on Lobbyists’ Compliance with Disclosure Requirements  GAO-16-320  Washington, D C : March 24, 2016  2016 Lobbying Disclosure: Observations on Lobbyists’ Compliance with Disclosure Requirements  GAO-17-385  Washington, D C : March 31, 2017 
Report 2:  These product characteristics may proxy for some underlying manufacturing costs or even consumer preferences  Since firms may create gender-specific product attributes—scents like “sweet petals” and “pure sport” or razor head types and colors to differentiate products between genders—we did not always keep every product attribute constant when comparing prices  The idiosyncratic error term is represented by ε  All of our regressions are weighted, with the proportion of units sold for a particular item in that year as the weight  This is because, for personal care products, there are large differences in units sold of various product types and brands, and therefore it not useful to compare simple un- weighted average prices  For example, for one company the highest selling men’s deodorant stick sold almost 12 million units in 2016, and the highest selling women’s deodorant stick sold over 8 million units  The average units sold for underarm deodorants as a whole was just over 300,000 units, and 1,000 products out of a total of almost 3,000 products had less than 100 units sold in 2016  The linear model we used has the usual shortcomings of being subject to specification bias to the extent the relationship between price and each of the independent variables is not linear  The model also does not include complete data on costs, such as advertising and packaging, or consumers’ willingness to pay, both of which have an effect on the price differences  The model may thus also be subject to omitted variable bias  In addition, the model may have some endogeneity issues to the extent the product characteristics themselves are influenced by consumers’ willingness to pay for some of those product features  To reduce the impact of any model misspecifications or heteroscedasticity, we used the robust (or Huber-White sandwich) estimator  We estimated the regression model above for each of the 10 products separately and for each of the two measures of price  We used Nielsen’s in-store, retail price scanner data, which include information on total volume sold and dollar sales for items purchased at 228 retailers including grocery stores, drug stores, mass merchandisers (such as Target), dollar stores, club stores (such as Sam’s Club), and convenience stores  The data capture 82 percent of all U S  sales  Nielsen also projects sales for the remaining noncooperating retailers, and that information is included in this dataset  We excluded some very small brands that did not have enough units sold from our regression analysis in order to avoid outliers  These brands usually had less than 50,000 units sold over the entire year, and for some products they represented less than 1 percent of all units sold  We found that average retail prices paid were significantly higher for women’s products than for men’s in 5 out of 10 personal care products  In 2 categories, men’s versions sold at a significantly higher price  One category had mixed results based on two price measures analyzed, and two others showed no significant gender price differences  A summary of our regression results is presented in table 5  We manually collected prices for 16 pairs of selected personal care products from the websites of four online retailers that also operated physical store locations  We selected comparable pairs of similar men’s and women’s products that were differentiated by product attributes, such as scent or color, and were sold at most or all of the four retailers  The products were selected based on several comparability factors such as brand, product claims, and number of blades in a razor  For two 1-week time periods in January and March 2018, we collected prices manually between 1:00 p m  and 7:00 p m  (ET) over two 7-day time periods  We collected listed prices and did not adjust the prices for any promotions that were available, such as online coupons or buy-one-get-one-free offers  Table 6 presents the results of our online price collection  These results have important limitations: The average prices shown are not generalizable to the broader universe of prices for these products sold at other times or by other online retailers  The data reflect prices advertised to consumers rather than the prices consumers actually paid  The data do not capture the volume of sales for each item for each retailer; in our analysis, we weighted all advertised prices equally across the retailers  As a result, differences we found within these advertised prices may not have translated into comparable differences in prices female and male consumers paid for these products online  The prices do not reflect any promotional discounts, volume discounts, or other discounts that may have been available to some or all consumers  This report examines (1) how prices compared for selected categories of consumer goods that are differentiated for men and women, and potential reasons for any significant price differences; (2) what is known about the extent to which men and women may pay different prices in, or experience different levels of access to, markets for credit and goods and services that are not differentiated based on gender; (3) the extent to which federal agencies have identified and taken steps to address any concerns about gender-related price differences; and (4) state and local government efforts to address concerns about gender-related price differences  To compare prices for selected goods that are differentiated for men and women, we purchased and analyzed Nielsen Company (Nielsen) data on retail prices paid for 10 personal care product categories for calendar year 2016  The product categories included underarm deodorants, body deodorants (typically sold as a spray), disposable razors, nondisposable razors, razor blades, shaving creams, shaving gels, and three categories of fragrances  We selected these categories of personal care products because they are commonly purchased consumer goods that were categorized by gender in the Nielsen data  The women’s and men’s versions of personal care products we selected are generally more similar in terms of the form, size, and packaging in comparison to certain other consumer product categories that are also differentiated by gender, such as clothing  We used regression models to analyze data on retail prices paid for the 10 categories of personal care products differentiated for women and men  To assess the reliability of the Nielsen data, we reviewed relevant documentation and conducted interviews with Nielsen representatives to review steps they took to collect and ensure the reliability of the data  In addition, we electronically tested data fields for missing values, outliers, and obvious errors  We determined that these data were sufficiently reliable for our purposes  For more details on the methodology for, and limitations of, our analysis of these retail price data, see appendix I  We also manually collected listed prices for 16 pairs of selected personal care products from four different retailer websites over two 7-day periods in January and March 2018  For each pair, we selected comparable men’s and women’s products that were differentiated by product attributes, such as scent or color, and were commonly sold across retailers  For more details on our online price data collection and the limitations associated with interpreting the results, see appendix II  To examine what is known about the extent to which men and women may be offered different prices or access for the same goods or services, we reviewed academic literature identified through a literature search covering the last 25 years  To identify existing studies from peer-reviewed journals, we conducted searches using subject and keyword searches of various databases, such as EconLit, Scopus, ProQuest, and Social SciSearch  We also used a snowball search technique—meaning we reviewed relevant academic literature cited in our selected studies—to identify additional studies  We performed these searches and identified articles from December 2016 to April 2018  From these searches, we identified 21 studies that appeared in peer-reviewed journals or research institutions’ publications from 1995 through 2016 and were relevant to gender-related price differences for the same products  We reviewed and assessed each study’s evaluation methodology based on generally accepted social science standards  See the bibliography at the end of this report for a list of the 21 studies  We then summarized the research findings  A GAO economist read and assessed each study, using the same data collection instrument  The assessment focused on information such as the types of disparities examined, the research design and data sources used, and methods of data analysis  The assessment also focused on the quality of the data used in the studies as reported by the researchers and any limitations of data sources for the purposes for which they were used  A second GAO economist reviewed each completed data collection instrument to verify the accuracy of the information included  As a result, the 21 studies that we selected for our review met our criteria for methodological quality  We found the studies we reviewed to be reliable for purposes of determining what is known about price differences for the same products  However, these studies have important limitations, such as using nonrepresentative data samples, and the results are not generalizable  To examine the federal role in overseeing gender-related price differences, we reviewed relevant federal statutes and agency guidance, and interviewed officials from the Federal Trade Commission (FTC), Bureau of Consumer Financial Protection (BCFP), the Department of Housing and Urban Development (HUD), and the Department of Justice (DOJ)  To help identify the extent of concerns about gender-related price differences, we interviewed representatives from eight consumer groups, three industry associations, and four academic experts  Additionally, we reviewed a sample of consumer complaints from databases managed by BCFP, FTC, and HUD (Consumer Complaint Database, Consumer Sentinel Network, and Enforcement Management System, respectively)  Complaints were submitted by consumers across the United States about various financial products, housing grievances, and other consumer protection concerns  To identify our universe of gender-related consumer complaints in BCFP and FTC databases, we used the following search terms that targeted sex or gender discrimination: discriminat, unfair, treat, decept, abus, female, woman, women, man, men, male, gender, sex, female, woman, women, man, men, male, gender, and sex  HUD’s consumer complaint database is categorized by protected class (e g , race, sex, national origin), so we did not need to use search terms to identify gender-related complaints  For the years 2012 through 2017, we identified 6,117 BCFP consumer complaint narratives; 10,472 FTC consumer complaints narratives; and 5,421 HUD consumer complaint narratives that were relevant to our scope  We then drew a stratified random probability sample of 100 gender-related consumer complaints from each database  To determine which complaints in our samples were about price differences related to gender or sex, two team members read through each complaint narrative and coded whether or not the complainant’s narrative indicated that they felt that they paid or were charged more because of their gender or sex  A third team member conducted a final review of the results, and made a final determination in cases where there were differences in the first two team member’s assessments  With this probability sample, each member of the study population had a nonzero probability of being included, and that probability could be computed for any member  We followed a probability procedure based on random selections and our sample is only one of a large number of samples that we might have drawn  Since each sample could have provided different estimates, we express our confidence in the precision of our particular sample’s results as a 95 percent confidence interval (with a margin of error of 5 9 percent)  This is the interval that would contain the actual population value for 95 percent of the samples we could have drawn  We assessed the reliability of these data by reviewing documentation and interviewing agency officials about the databases used to collect these complaints  We determined that these data were sufficiently reliable for our purposes of identifying complaints of gender- related price differences  To explore state and local efforts to address concerns about gender- related price differences, we conducted a literature search and identified three state or local laws or ordinances that specifically address gender- related price differences: California, Miami-Dade County, Florida, and New York City, New York  We reviewed these laws and ordinances and interviewed officials from these jurisdictions to discuss motivations for, oversight of, and the impact of these laws  We conducted this performance audit from October 2016 to August 2018 in accordance with generally accepted government auditing standards  Those standards require that we plan and perform the audit to obtain sufficient, appropriate evidence to provide a reasonable basis for our findings and conclusions based on our audit objectives  We believe that the evidence obtained provides a reasonable basis for our findings and conclusions based on our audit objectives  For each of 10 personal care product categories we analyzed, we compared the overall average prices for women’s products and men’s products using two measures of average price: average item price and average price per ounce or count  While the second price measure adjusts the average price for quantity of product, these comparisons did not take into account the effect on price of differences in product brand, packaging, and other characteristics  As shown in table 7, adjusting the average item price to account for differences in product quantity (ounces or count) significantly affected the size and magnitude of gender price differences for several product categories  This is because men’s products in the dataset were frequently larger in size or count compared with women’s products in the same category  For example, women’s disposable razors sold for 11 percent less than those targeted to men when we compared average item prices  However, when we compared average price per count of razors, women’s disposable razors sold for 19 percent more on average than men’s  This is because women’s disposable razors had on average about one fewer razor per package  In 5 out of 10 product categories, women’s versions of the product on average sold for a higher price per ounce or count than men’s and these differences were statistically significant at the 95 percent confidence level for 4 products and at the 90 percent level for one product  Information about sales and relative sizes of different products targeted to men and women are presented in table 8 below  This appendix provides additional details about the consumer complaint processes at the Bureau of Consumer Financial Protection (BCFP), Federal Trade Commission (FTC), and Department of Housing and Urban Development (HUD)  Consumers with a complaint about unfair treatment related to gender could submit a complaint to one of these agencies  BCFP and FTC monitor consumer complaints related to violations under the Equal Credit Opportunity Act, while HUD and the Department of Justice (DOJ) investigate housing discrimination complaints under the Fair Housing Act  These complaints could be about price differences because of gender  Alicia Puente Cackley, (202) 512-8678 or cackleya@gao gov  In addition to the contact named above, John Fisher (Assistant Director), Jeff Harner (Analyst in Charge), Vida Awumey, Bethany Benitez, Namita Bhatia-Sabharwal, Kelsey Kreider, and Kelsey Sagawa made key contributions to this report  Also contributing to this report were Abigail Brown, Michael Hoffman, Jill Lacey, Oliver Richard, Tovah Rom, and Paul Schmidt  We reviewed literature to identify what is known about the extent to which female and male consumers may face different prices or access in markets for credit and goods and services that are not differentiated based on gender  This bibliography contains citations for the 20 studies and articles that we reviewed that compared prices or access for female and male consumers in markets where the product is not differentiated by gender (mortgages, small business credit, auto purchases, and auto repairs)  Asiedu, Elizabeth, James A  Freeman, and Akwasi Nti-Addae  “Access to Credit by Small Businesses: How Relevant Are Race, Ethnicity, and Gender?” The American Economic Review, vol  102, no  3 (2012): 532- 537  Ayers, Ian and Peter Siegelman  “Race and Gender Discrimination in Bargaining for a New Car ” The American Economic Review, vol  85, no  3  (1995): 304-321  Blanchard, Lloyd, Bo Zhaob, and John Yinger  “Do lenders discriminate against minority and woman entrepreneurs?” Journal of Urban Economics 63 (2008): 467–497  Blanchflower, David G , Phillip B  Levine, and David J  Zimmerman  “Discrimination in the Small-Business Credit Market ” The Review of Economics and Statistics, vol  85, no  4 (2003): 930-943  Busse, Meghan R , Ayelet Israeli, and Florian Zettelmeyer  “Repairing the Damage: The Effect of Price Expectations on Auto Repair Price Quotes ” National Bureau of Economic Research, Working Paper 19154 (2013)  Cavalluzzo, Ken S , Linda C  Cavalluzzo, and John D  Wolken  “Competition, Small Business Financing, and Discrimination: Evidence from a New Survey ” The Journal of Business, vol  75, no  4 (2002): 641- 679  Cheng, Ping, Zhenguo Lin, and Yingchun Liu  “Do Women Pay More for Mortgages?” The Journal of Real Estate Finance and Economics, vol  43 (2011): 423-440  Cheng, Ping, Zhenguo Lin, and Yingchun Liu  “Racial Discrepancy in Mortgage Interest Rates ” The Journal of Real Estate Finance and Economics, vol  51 (2015): 101-120  Cole, Rebel, and Tatyana Sokolyk  “Who Needs Credit and Who Gets Credit? Evidence from the Surveys of Small Business Finances”  Journal of Financial Stability, vol  24 (2016), 40-60  Coleman, Susan  “Access to Debt Capital for Women- and Minority- Owned Small Firms: Does Educational Attainment Have an Impact?” Journal of Developmental Entrepreneurship, vol  9, no  2 (2004): 127-143  Duesterhas, Megan, Liz Grauerholz, Rebecca Weichsel, and Nicholas A  Guittar  “The Cost of Doing Femininity: Gendered Disparities in Pricing of Personal Care Products and Services,” Gender Issues, vol  28, (2011): 175-191  Goodman, Laurie, Jun Zhu, and Bing Bai  “Women Are Better than Men at Paying Their Mortgages ” Urban Institute, Research Report (2016)  Haughwout, Andrew, et al  “Subprime Mortgage Pricing: The Impact of Race, Ethnicity, and Gender on the Cost of Borrowing ” Brookings- Wharton Papers on Urban Affairs (2009): 33-63  Mijid, Naranchimeg  “Gender differences in Type 1 credit rationing of small businesses in the US ” Cogent Economics & Finance, vol  3 (2015)  Mijid, Naranchimeg  “Why are female small business owners in the United States less likely to apply for bank loans than their male counterparts?” Journal of Small Business & Entrepreneurship, vol  27, no  2 (2015): 229- 249  Mijid, Naranchimeg and Alexandra Bernasek  “Gender and the credit rationing of small businesses ” The Social Science Journal, vol  50 (2013): 55-65  Morton, Fiona Scott, Florian Zettelmeyer, and Jorge Silva-Risso  “Consumer Information and Price Discrimination: Does the Internet Affect the Pricing of New Cars to Women and Minorities?” National Bureau of Economic Research, Working Paper 8668 (2001)  O’Connor, Sally  “The Impact of Gender in the Mortgage Credit Market ” University of Wisconsin-Milwaukee Doctoral Dissertation (1996)  Van Rensselaer, Kristy N , et al  “Mortgage Pricing and Gender: A Study of New Century Financial Corporation ” Academy of Accounting and Financial Studies Journal, vol  18, no  4 (2014): 95-110  Wyly, Elvin and C S  Ponder  “Gender, age, and race in subprime America ” Housing Policy Debate, vol  21, no  4 (2011): 529-564  Zimmerman Treichel, Monica and Jonathan A  Scott  “Women-Owned Businesses and Access to Bank Credit: Evidence from Three Surveys Since 1987 ” Venture Capital, vol  8, no  1 (2006): 51-67 

ground_truth: Technology companies are a major source of high-paying U.S. jobs, but some have questioned the sector's commitment to equal employment opportunity. EEOC provides federal oversight of nondiscrimination requirements by investigating charges of discrimination, and OFCCP enforces federal contractors' compliance with affirmative action requirements. GAO was asked to review workforce trends in the technology sector and federal oversight. This report examines (1) trends in the gender, racial, and ethnic composition of the technology sector workforce; and (2) EEOC and OFCCP oversight of technology companies' compliance with equal employment and affirmative action requirements. GAO analyzed workforce data from the American Community Survey for 2005-2015 and EEOC Employer Information Reports for 2007-2015, the latest data available during our analysis. GAO analyzed OFCCP data on compliance evaluations for fiscal years 2011-2016. GAO interviewed agency officials, researchers, and workforce, industry, and company representatives. The estimated percentage of minority technology workers increased from 2005 to 2015, but GAO found that no growth occurred for female and Black workers, whereas Asian and Hispanic workers made statistically significant increases (see figure). Further, female, Black, and Hispanic workers remain a smaller proportion of the technology workforce—mathematics, computing, and engineering occupations—compared to their representation in the general workforce. These groups have also been less represented among technology workers inside the technology sector than outside it. In contrast, Asian workers were more represented in these occupations than in the general workforce. Stakeholders and researchers GAO interviewed identified several factors that may have contributed to the lower representation of certain groups, such as fewer women and minorities graduating with technical degrees and company hiring and retention practices. Both the U.S. Equal Employment Opportunity Commission (EEOC) and the Department of Labor's Office of Federal Contract Compliance Programs (OFCCP) have taken steps to enforce equal employment and affirmative action requirements in the technology sector, but face limitations. While EEOC has identified barriers to recruitment and hiring in the technology sector as a strategic priority, when EEOC conducts investigations, it does not systematically record the type of industry, therefore limiting sector-related analyses to help focus its efforts. EEOC has plans to determine how to add missing industry codes but has not set a timeframe to do this. In addition, OFCCP's regulations may hinder its ability to enforce contractors' compliance because OFCCP directs contractors to set placement goals for all minorities as a group rather than for specific racial/ethnic groups. OFCCP also has not made changes to its establishment-based approach to selecting entities for review in decades, even though changes have occurred in how workplaces are structured. Without taking steps to address these issues, OFCCP may miss opportunities to hold contractors responsible for complying with affirmative action and nondiscrimination requirements. GAO makes 6 recommendations, including that EEOC develop a timeline to improve industry data collection and OFCCP take steps toward requiring more specific minority placement goals by contractors and assess key aspects of its selection approach. EEOC neither agreed nor disagreed with its recommendation, and OFCCP stated the need for regulatory change to alter placement goal requirements. GAO continues to believe actions are needed, as discussed in the report.

prompt_front: You are given two reports by the government agency.



Question: Write a one-page summary of the report 2.

Report 1: The technology sector has major employment hubs across the country, including the San Francisco Bay area, the greater New York City region, and the Washington-Arlington-Alexandria region (see fig  1)  In addition, technology workers are employed at companies outside the technology sector, such as in the retail or financial services industries  For example, a large retail company may require technology workers to create and manage their online sales activities, but the company itself would be considered part of the retail industry  Private companies are generally prohibited by federal law from discriminating in employment on the basis of race, color, religion, sex, national origin, age, and disability status  Additionally, federal contractors and subcontractors are generally required to take affirmative action to ensure that all applicants and employees are treated without regard to race, sex, color, religion, national origin, sexual orientation, and gender identity, and to employ or advance in employment qualified individuals with disabilities and qualified covered veterans  EEOC is responsible for enforcement of federal antidiscrimination laws, and OFCCP enforces affirmative action and nondiscrimination requirements for federal contractors  EEOC and OFCCP have some shared activities and have established a memorandum of understanding (MOU) to minimize any duplication of effort  For example, under the MOU, individual complaints filed with OFCCP alleging discrimination under Title VII are generally referred to EEOC  In addition, on occasions when EEOC receives a complaint not within its purview, such as cases that involve veteran status, but over which it believes OFCCP has jurisdiction, it will refer the complaint to OFCCP  The EEOC, created by Title VII of the Civil Rights Act of 1964, enforces federal laws that prohibit employment discrimination on the basis of race, sex, color, religion, national origin, age, and disability  As the nation’s primary enforcer of antidiscrimination laws, EEOC investigates charges of employment discrimination from the public, litigates major cases, and conducts outreach to prevent discrimination by educating employers and workers  In fiscal year 2016, EEOC received about 91,500 charges, secured more than $482 million for victims of discrimination, and filed 114 lawsuits  According to EEOC, many states, counties, cities, and towns have their own laws prohibiting discrimination, usually similar to those EEOC enforces, as well as agencies responsible for enforcing those laws, called Fair Employment Practices Agencies  However, in some cases, these agencies enforce laws that offer greater protection to workers  An individual can file a charge with either the EEOC or with a Fair Employment Practices Agency  When an individual initially files with a Fair Employment Practices Agency that has a worksharing agreement with the EEOC, and the allegation is covered by a law enforced by the EEOC, the Fair Employment Practices Agency will dual file the charge with EEOC (meaning EEOC will receive a copy of the charge), but will usually retain the charge for processing  If the charge is initially filed with EEOC and the charge is also covered by state or local law, EEOC dual files the charge with the state or local Fair Employment Practices Agency (meaning the Fair Employment Practices Agency will receive a copy of the charge), but EEOC ordinarily retains the charge for processing  EEOC also pursues a limited number of cases each year designed to combat systemic discrimination, defined by the agency as patterns or practices where the alleged discrimination presented by a complainant has a broad impact on an industry, profession, company, or geographic location  EEOC can also initiate a systemic investigation under Title VII with the approval of an EEOC commissioner, called a “commissioner charge”, provided the commissioner finds there is a reasonable basis for the investigation  In addition, EEOC district directors can approve systemic investigations, called “directed investigations” which are initiated by EEOC field office directors under the Age Discrimination in Employment Act and the Equal Pay Act  Under Title VII, EEOC generally requires that large employers and non- exempt federal contractors file Employer Information Reports (EEO-1 reports) annually, which collect employees’ demographic data by business location on sex, race, and ethnic group for 10 occupational job categories  According to EEOC documentation, EEO-1 data are used in investigations of Title VII violations, litigation, research, comparative analyses, class action suits, and affirmative action plans  The OFCCP is responsible for ensuring that the nearly 200,000 federal contractor establishments comply with federal nondiscrimination and affirmative action requirements  Under Executive Order 11246 and other federal laws and regulations, covered federal contractors and subcontractors are prohibited from discriminating in employment on the basis of race, color, religion, sex, sexual orientation, gender identity, or national origin and are required to take affirmative action to help ensure that all applicants and employees are treated without regard to these factors  In general, OFCCP’s regulations require covered contractors to comply with certain recordkeeping and reporting requirements, and provide for enforcement procedures such as compliance evaluations and complaint investigations to assist OFCCP in ensuring federal contractor compliance with these regulations  Among other provisions, OFCCP’s regulations generally require that covered contractors prepare and maintain an affirmative action program (AAP)  Under OFCCP’s regulations, an AAP is a management tool that is designed to ensure equal employment opportunity, with an underlying premise that the gender, racial, and ethnic makeup of a contractor’s workforce should be representative of the labor pools from which the contractor recruits and selects  Companies must create an AAP for each business establishment—generally, a physical facility or unit that produces the goods or services, such as a factory, office, or store for the federal contractor  An AAP will also include any practical steps to address underrepresentation of women and minorities, such as expanding employment opportunities to underrepresented groups  Covered contractors must also comply with certain recordkeeping requirements, including records pertaining to hiring, promotion, lay off or termination, rates of pay, and applications, among other records  OFCCP’s enforcement program represents the majority of the agency’s activity and is carried out primarily by using compliance officers, who evaluate contractors’ compliance with various requirements, according to agency officials  In addition to conducting compliance evaluations, OFCCP also conducts investigations in response to complaints  In 2016, we reported that according to OFCCP officials, responding to complaints accounted for close to 16 percent of OFCCP’s enforcement activities  OFCCP selects contractor establishments for evaluations based on a number of neutrally applied factors, such as employee count at the establishment, contract value, or contract expiration date  We previously found that OFCCP reviews, on average, 2 percent of federal contractor establishments annually  As we previously reported, as part of its compliance evaluations, OFCCP is to review the selected contractor’s hiring, promotion, compensation, termination, and other employment practices to determine whether contractors are maintaining nondiscriminatory hiring and employment practices  OFCCP conducts evaluations at the establishment level  When a contractor establishment is selected for evaluation, OFCCP sends the contractor a “scheduling letter” requesting the AAP and supporting data, such as the percentage of women and minority staff at the workplace by job group  Then, a compliance officer is to conduct a desk audit, which is an off-site review of the submitted materials  If necessary, the compliance officer may also conduct an on-site review or further off-site analysis to make a final determination as to whether the contractor is in compliance  In addition to looking at whether federal contractors maintain nondiscriminatory hiring and employment practices, which can result in finding discrimination violations, OFCCP also frequently finds other types of violations, such as failure to keep necessary records or conduct annual reviews of equal employment and affirmative action efforts  These findings by the agency often require administrative changes on the part of the contractor, such as improved record-keeping  There are many different forms of remedies for discrimination violations, including financial, employment, and organizational change remedies  Although rare, under some circumstances, OFCCP may bar a contractor from doing business with the government  From 2005 to 2015, the estimated number of workers in the technology workforce—people who worked in mathematics, computing, or engineering occupations—increased at a higher rate (24 percent) than the estimated number of workers in the general workforce (9 percent), according to ACS data  In 2015, the technology workforce comprised an estimated 7 5 million workers, an increase of slightly over 1 4 million workers since 2005  (For a complete list of the occupations we include as technology occupations, see appendix II)  Most technology workers have a college degree and have a higher median income than workers in the general workforce  Specifically, in 2015, an estimated 69 percent of technology workers held at least a bachelor’s degree, compared to 31 percent of workers in the general workforce  In 2015, the estimated median income for technology workers was $81,000 compared to $42,000 for the general workforce  From 2005 to 2015, the percentage of women in the technology workforce remained flat and women remained a smaller proportion of the technology workforce compared to their representation in the general workforce  In 2015, women represented 22 percent (about 1 6 million workers) of workers in technology occupations, compared to 48 7 percent of workers in the general workforce (see fig  2)  Although the estimated percentage of minority technology workers as a whole had grown since 2005, we found that this trend did not apply to Black technology workers  Specifically, from 2005 through 2015, although the number of Black workers increased as the technology workforce grew, there was no statistically significant change in their representation as a percentage of the entire technology workforce  In contrast, from 2005 to 2015, Hispanic and Asian technology workers had statistically significant increases in their representation in the technology workforce  Even with the increase in their numbers in the technology workforce, Black and Hispanic technology workers remained a smaller proportion of these workers compared to their representation in the general workforce  In contrast, Asian workers were an increasing share of the technology workforce, where they remained more represented than they were in the general workforce (see fig  3)  We found that when we examined gender representation for each minority group, both Black and Hispanic men and women were less represented in the technology workforce compared to their representation in the general workforce  The same was true for White women, whereas White men, Asian men, and Asian women were more represented in the technology workforce compared to their representation in the general workforce (see fig  4)  We defined the technology sector as those companies that have the highest concentration of technology workers and are in such industries as computer systems design and software publishing  Companies categorized as outside the technology sector, for example, retail or finance companies, may still employ some technology workers  However, we found differences in median incomes for technology workers within and outside the technology sector  In 2015, technology workers employed in the technology sector earned an estimated median income of $89,000 compared to median incomes of $78,000 for those working outside the technology sector  We also compared the characteristics of technology workers within the technology sector and outside the technology sector, and found male and Asian technology workers were relatively more represented in the technology sector than outside the technology sector  Similar to the lower representation of female, Black, and Hispanic technology workers in technology occupations, we found technology workers from these groups were also more likely to work outside the technology sector than in the technology sector  For example, according to our analysis of 2015 ACS data, women represented an estimated 18 percent of all technology workers employed in the technology sector, compared to 25 percent of all technology workers employed outside the technology sector (see fig  5)  White technology workers were also more represented outside the technology sector than within the technology sector  Companies in the technology sector also employ non-technical workers such as sales people, and the lower representation of women and certain minorities in the technology sector was also present in such non-technical job categories  According to our analysis of EEO-1 data, women were less represented across the full range of management and non- management positions at companies within the technology sector, including at leading technology companies, compared to their representation in companies outside the technology sector  We determined this by comparing specific occupations at companies both within and outside the technology sectors using 2015 EEO-1 data  For example, women held about 19 percent of senior-level management positions at companies in the technology sector compared to nearly 31 percent of such positions at companies outside the technology sector in 2015  Women were also less represented in all of the remaining job categories (mid-level managers, professionals, technicians, and all other jobs) in the technology sector  (See fig  6 ) Comparing EEO-1 data at three points in time for 2007, 2011, and 2015, we found women’s representation in management positions as well as among professionals and technicians at companies within the technology sector remained at about the same level, and decreased for “all other jobs” (see table 1)  Similar to women, Black and Hispanic workers were less represented across multiple job categories in companies within the technology sector compared to those outside the technology sector (see fig 7)  For example, 1 8 percent of senior level managers in the technology sector were Black compared to 3 4 percent of senior level managers in all other sectors  Appendix IV provides percentages for each minority group in different job categories within and outside the technology sector  The lower representation of Black workers in the technology sector relative to their representation in other sectors was consistent across all job categories (mid-level managers, professionals, technicians, and “all other jobs”)  Hispanic workers were less represented in the technology sector compared to outside the technology sector across all job categories (senior and mid-level managers, professionals, technicians, and “all other jobs”)  Compared to their representation across job categories within the technology sector in general, Black and Hispanic workers had slightly greater representation at the leading technology companies in senior management and technician categories, and lower representation among mid-level managers, professionals, and holders of “all other jobs ” Asian workers comprised a greater proportion of managerial and professional roles in the technology sector than in other sectors, according to our analysis of 2015 EEO-1 data  Asian workers represented 11 0 percent of senior level managers in the technology sector compared to 4 3 percent in industries outside the technology sector  This higher representation of Asian workers in the technology sector was consistent among mid-level managers, professionals and technicians  Asian workers were more represented in the same categories at the leading technology companies  However, a lower proportion of Asian workers held senior management positions compared to their representation in professional positions in both the technology sector and leading technology companies  Further, the proportion of Asian workers in mid-level management positions was also lower than their representation in professional positions, from which mid-level managers might be selected, in both the technology sector and leading technology companies  In contrast, a higher proportion of White workers were in senior and mid- level management positions compared to their representation in professional positions in both the technology sector and leading technology companies  Comparing EEO-1 data at three points in time—2007, 2011, and 2015— we saw varied representation across job categories in the technology sector by race/ethnicity  For example, Black workers decreased in their representation in all job categories in the technology sector from 2007 to 2015  In contrast, Hispanic and Asian workers increased in their representation in all job categories we examined from 2007 to 2015 (see table 2)  Several factors may contribute to the lower representation of female, Hispanic, and Black workers in the technology workforce and at companies in the technology sector, based on research and interviews with researchers and representatives from workforce and industry organizations and technology companies  These include the lower diversity of degree earners in technology-related fields, and company- based factors such as hiring practices and retention of women and underrepresented minorities  The smaller proportion of women in the technology workforce may reflect the number of women earning technology-related degrees  Slightly over two-thirds of technology workers report having earned their bachelor’s degree in a computer, engineering, mathematics, or technology field  However, according to our analysis of 2014 IPEDS data, the percentage of technology-related bachelor’s and master’s degrees earned by women is far less than for men, although women were comparable to men in their receipt of science, technology, engineering, and math (STEM) degrees, and surpassed men in obtaining degrees in all other fields  In 2014, about 60,000 women were awarded technology-related bachelor’s or master’s degrees (compared to about 50,000 in 2004) and about 190,000 men were awarded such degrees (compared to about 147,000 in 2004)  (See fig  8 ) An estimated 218,000 technology workers were added to the technology workforce in 2015, according to our analysis of 2015 American Community Survey data from the U S  Census Bureau  In addition, technology degrees are also issued at the associate’s level  Two researchers told us that women often have the academic preparation to enter into technology-related degree programs, but they may choose not to pursue such degrees because of instances of gender bias within technology classes  Our prior work reported on studies that found women leave STEM fields at a higher rate than their male peers, citing one study that found women leave STEM academic positions at a higher rate than men in part due to dissatisfaction with departmental culture, faculty leadership, and research support  Further, a 2012 consulting firm report found that businesses viewed as male-dominated tended to attract fewer women at the entry level  In addition, according to our analysis of 2014 IPEDS data, three minority racial or ethnic groups each constituted 10 percent or fewer of bachelor’s and master’s degree earners in a technology-related field  Specifically, among the 202,200 earners of degrees in a technology-related field in 2014, there were about 20,000 Hispanic recipients, 13,000 Black recipients, and 18,000 recipients who were Multiracial or other race, which includes American Indian or Alaska Native, Other or Unknown Race, and Two or more Races, i e  respondents who selected one or more racial designations  Among all minority groups, Asian students, including Pacific Islander, earned the highest proportion of technology- related degrees (about 24,000 individuals)  (See fig  9)  One barrier to entry into technology degree paths for Black and Hispanic students may be lower likelihood of access to preparatory academic programs in secondary school  In 2016, we reported that the K-12 public schools in the United States with students who are mostly Black or Hispanic offered disproportionately fewer math and science classes for their students  One researcher told us some colleges and universities, to help these students be academically successful, provide additional academic support such as tutoring to help bridge knowledge gaps  To address the uneven access to preparatory math and science classes, representatives from five technology companies told us they have started to invest in exposing Black and Hispanic children to technology occupations by, for example, developing online resources targeted to them and their parents and creating partnerships with secondary schools to improve their academic preparation in computer science  However, we have previously also reported that the number of students graduating with STEM degrees may not be a good measure of the supply of STEM workers because students often pursue careers in fields different from the ones they studied  For example, a lower percentage of women who obtained technology-related degrees became technology workers compared to men who earned the same degrees, according to our analysis of 2015 ACS data  Specifically, among women who earned technology degrees, an estimated 33 percent worked as a technology worker compared to 45 percent of men who earned technology degrees  Several representatives we interviewed from workforce and industry organizations and technology companies told us that recruitment practices may also have affected diversity in the technology workforce  For example, representatives from three workforce and industry organizations said technology companies tend to recruit from a select number of universities and colleges, thereby limiting their pool of potential applicants  To address this, representatives from several of the technology companies we interviewed told us they had changed recruitment practices and offered internships targeted to underrepresented groups  For example, representatives of four technology companies told us that their companies had expanded recruitment to include more schools  Representatives from two companies told us they offer programs such as summer and semester internships for which the company actively recruits from Historically Black Colleges and Universities and other specific schools to increase its pool of diverse candidates  In addition, representatives from workforce organizations and technology companies discussed concerns and strategies to address companies’ hiring practices and internal cultures that may limit workforce diversity  For example, one of these representatives said that technology companies often offer financial incentives to current employees to make referrals for new hires, which can result in reliance on social networks  These networks may be largely comprised of the same race and this practice therefore makes it harder for potential candidates from demographically different groups to have their resumes reviewed  Another workforce organization representative reported that some hiring managers filter out eligible candidates if their background and qualifications are not the same as those of previously successful employees  To address these concerns, representatives from one technology company told us that they had moved away from depending on referrals since this practice may result in leaders hiring people within their own networks, which generally does not increase diversity of gender or race/ethnicity  In addition, representatives from another company said they plan to begin reviewing resumes with names removed to limit bias by the reviewer  Further, representatives we interviewed from three technology companies told us they offer training to employees to help employees identify their own, unconscious biases  Other factors may affect retention of women and underrepresented minorities  For example, a representative from a workforce organization said that women leave technology occupations at a higher rate than men because they feel as if they have not been given the same opportunities for promotion and advancement within the company  A 2016 study that examined women in engineering and science found that women’s concerns about pay and promotion are often an issue in male-dominated fields regardless of the industry  Further, this study found that retention difficulties become more severe as the share of men in the workforce increased and that affected women’s pay and promotion  Representatives from one company told us another challenge is the lack of Black workers at the top levels, which might make it more difficult for Black employees in particular to see a leadership path  Representatives we interviewed from five technology companies told us they had implemented efforts to increase retention and promotion rates among minority and female workers, for example, by developing a diversity and inclusion newsletter, employee resource groups with executive sponsors, and internal training and classes for employees to improve their readiness to be promoted  Representatives from five technology companies told us that commitment of top leadership is an important factor that can help women and underrepresented minorities in the technology sector  For example, representatives from one company told us that top management support for diversity efforts, such as setting hiring goals, can help move a company in the direction of achieving representation goals and that leadership is very important to this effort  Representatives from several companies told us that there is often a business case for such changes: These companies work in a diverse, global environment and strive to make better products for diverse users  However, our prior work on workforce diversity in the financial services sector found that some diversity initiatives faced challenges gaining the "buy-in" of key employees, such as the middle managers who are often responsible for implementing such programs  According to EEOC officials, EEOC primarily oversees compliance with equal employment opportunity requirements by investigating workers’ individual charges of employment discrimination filed against companies  EEOC has publicly acknowledged the low levels of diversity in the technology sector  However, we were unable to identify a specific number of charges received by EEOC against companies in industries that are part of this sector because EEOC does not require investigators to record the industry of the charged company  EEOC’s database of charges and enforcement actions—the Integrated Mission System (IMS)—has a data field for the North American Industry Classification System (NAICS) industry code, the standard used by federal statistical agencies in classifying business establishments  However, we found that it is completed for only about half the entries in the system  EEOC officials in both the San Francisco and New York district offices told us that, while they cannot readily identify individual charges against technology companies, they believe they have received far fewer charges against technology companies than they would have expected given the public attention to the issue of diversity in the technology sector  In terms of systemic cases, according to EEOC, as of June 2017, the commission had 255 systemic cases pending since fiscal year 2011 involving technology companies (13 of these were initiated as commissioner charges and 8 were directed investigations involving age discrimination or pay parity issues)  Officials from the New York region reported that they had seen an increase in systemic cases against technology companies in the past 3 years, largely involving practices of information technology staffing firms  Several EEOC officials we interviewed noted that technology workers may be initiating few complaints at the federal level due to factors such as fear of retaliation from employers or the availability of other employment or legal options  According to EEOC officials, fear of retaliation can affect charges across sectors and, given the growth in the technology workforce, an individual who feels discriminated against may simply leave the company because there are many other opportunities for individuals with technical skills  They also said that technology workers may generally have greater wealth and can afford to hire private attorneys to sue in state court rather than go through the EEOC  Moreover, they said that some states, including California, have stronger employment discrimination laws that allow for better remedies than federal laws, which could lead employees to file charges at the state level rather than with the EEOC  In addition, EEOC has acknowledged in a 2016 report that binding arbitration policies, which require individuals to submit their claims to private arbiters rather than courts, can also deter workers from bringing discrimination claims to the agency, leaving significant violations in entire segments of the workforce unreported  The report stated that an increasing number of arbitration policies have added bans on class actions that prevent individuals from joining together to challenge practices in any forum  The report concluded that the use of arbitration policies hinders EEOC’s ability to detect and remedy potential systemic violations  Researchers report that the use of such clauses has grown and data on federal civil filings for civil rights employment cases reflect a marked reduction in the number of such filings  Beyond pursuing charges, EEOC has taken some steps to address diversity in the technology sector including research and outreach efforts  In May 2016, citing the technology sector as a source for an increasing number of U S  jobs, EEOC released a report analyzing EEO-1 data on diversity in the technology sector in tandem with a commission meeting raising awareness on the topic  In addition, EEOC’s fiscal year 2017- 2021 Strategic Enforcement Plan identified barriers to hiring and recruiting in the technology sector as a strategic priority  EEOC has also been involved in outreach efforts with the technology sector  For example, the EEOC Pacific Region described more than 15 in-person or webinar events since 2014 in collaboration with OFCCP and local organizations focused on diversity in the technology sector  The topics of these events included equity in pay and the activities of these two agencies in enforcing nondiscrimination laws  Finally, in fall 2016, EEOC initiated an internal working group to identify practices to help improve gender and racial diversity in technology, but as of June 2017 had no progress to report  OFCCP’s regulations require covered federal contractors to take proactive steps to ensure equal employment opportunity  OFCCP annually conducts routine evaluations of selected federal contractors, which includes those in the technology sector, for compliance with federal nondiscrimination and affirmative action requirements  To the extent that technology contractors are selected for evaluation through OFCCP’s normal selection process, these contractors are assessed for compliance with nondiscrimination and affirmative action laws as are other selected contractors  While evaluation of technology contractors occurs in the course of OFCCP’s routine activities, OFCCP does not currently use type of industry as a selection factor, according to officials  We also found that few (less than 1 percent) of OFCCP’s 2,911 closed technology contractor evaluations from fiscal years 2011 through 2016 resulted in discrimination violations, though 13 percent resulted in other violations, such as record- keeping violations and failure to establish an affirmative action program (AAP)  An AAP is a key tool OFCCP requires contractors to complete to ensure equal employment opportunity  The remaining 86 percent of evaluations either found no violations or ended in administrative closure  Technology contractor evaluations that had discrimination violations resulted in back pay, salary adjustments, or other benefits totaling more than $4 5 million for 15,316 individuals (averaging about $300 per award) for fiscal years 2011 through 2016  The vast majority of discrimination violations were on the basis of gender or race/ethnicity rather than disability or veteran status  Corrective actions OFCCP identified for federal technology contractors over this timeframe also included requiring contractors to fill a total of 410 job vacancies as they arise with applicants who had been denied employment on the basis of discrimination  In addition, OFCCP recently filed three complaints against technology companies  According to our analysis, OFCCP conducted evaluations on 36 of the 65 leading technology companies from fiscal year 2011 through fiscal year 2016  During this timeframe there were 272 reviews of establishments— physical business locations—affiliated with these 36 companies  Based on these evaluations, 15 of the 36 companies had administrative violations, and 2 of the 36 also had discrimination violations  As a result of the discrimination findings against these leading technology companies, 541 individuals received monetary benefits totaling $783,387 (an average of $1,448 per award)  In terms of other steps to conduct oversight of the technology sector, OFCCP officials in the Pacific Region said they are hiring compliance officers with legal training to be better able to address needs for reviews in the technology sector, such as responding to lawyers representing technology contractors  Officials in both the Pacific and Northeast regions work closely with statisticians and labor economists on their cases, an effort officials said has increased over the past few years  OFCCP has also requested funding in its fiscal year 2018 congressional budget justification to establish centers in San Francisco and New York that would develop expertise to handle large, complex compliance evaluations in specific industries, including information technology  We found that by not requiring an industry code in its investigations data, EEOC cannot analyze charge data by industry to help identify investigation and outreach priorities, in contradiction to EEOC strategic planning documents and EEOC Inspector General reports, which have emphasized the importance of doing so  By not requiring the use of the NAICS code for each entry in IMS, EEOC is limited in its ability to use these data for the purposes of identifying charges by industry sector and conducting sector-related analyses  Officials were aware of substantial gaps in coding of charges by industry and acknowledged limitations in the commission’s ability to analyze its investigations data by industry  However, officials expressed concern that routinely creating more complete records of the companies against which charges had been filed would require investigators to divert attention from their efforts to investigate charges  EEOC officials explained that the charging party provides initial information on the respondent company and requiring EEOC personnel to generate this information would slow down the process  They said their priority is to investigate individual charges, not to address larger trends or target specific industries  “The Strategic Enforcement Plan recommends using EEOC data to allow our enforcement and outreach efforts to focus on areas of significant concern  This might include tailoring outreach efforts for industries that experience greater likelihood of certain charges or informing enforcement decisions based on knowledge that certain industries have persistent problems, such as harassment  The data maintained in IMS provide a rich resource of information that can be used to explore the characteristics of industries that appear to have higher levels of certain allegations than comparative industries ” In addition, reports completed by the Urban Institute for the EEOC Office of Inspector General in 2013 and 2015 similarly recommended analysis of charge data, including by industry, to help identify priorities and measure performance  While EEOC has plans to review a year of IMS data to clean it and determine how best to add missing industry codes, among other objectives, officials could not provide a specific timeframe for when this review would begin and end  Standards for internal control in the federal government state that management should use quality information to achieve the agency’s objectives and objectives should be defined in specific terms so they are understood at all levels of the entity  This involves clearly defining what is to be achieved, who is to achieve it, how it will be achieved, and the time frames for achievement  Efforts to scrub these data and identify missing codes could help EEOC determine how to collect industry information on an ongoing basis for all entries  Doing so would also help EEOC determine the level of NAICS code that would be feasible and useful for investigators to identify and input into IMS  Without analyzing its data on charges across industries, EEOC’s ability to proactively identify priorities for its outreach and enforcement resource use is limited  We found that OFCCP also faces challenges that may hinder the agency’s oversight of technology companies  Specifically, OFCCP reported facing delays in receiving information from federal contractors, including technology companies, but has not yet evaluated whether its own policies and practices also impede its efforts to hold federal technology contractors responsible for the legal requirements to take affirmative action and not discriminate against protected groups  In addition, OFCCP regulations do not require federal contractors to disaggregate data for the purpose of determining placement goals for hiring, which may hinder contractors’ efforts to implement effective affirmative action programs  OFCCP has not analyzed delays in obtaining information from contractors OFCCP officials told us that they face delays in obtaining complete, accurate, and timely documentation from federal contractors, including technology companies, as part of the compliance review process  They said this limited their access to critical information and hindered OFCCP’s ability to determine whether discrimination had occurred  Officials in the Pacific Region reported that when issues are identified during OFCCP’s initial review that will require additional data, the data requests can be extensive  Consequently, technology contractors are taking longer to submit complete and accurate data that are needed to conduct analyses of the contractor’s workforce  In addition, officials in both the Pacific and Northeast regions reported that companies may not provide raw data as requested, or provide access to employees for OFCCP to interview, which is part of the compliance review process  Using 2015 OFCCP compliance evaluation data, we previously reported that close to 85 percent of contractor establishments across all sectors did not submit an AAP within 30 days of being scheduled for an OFCCP compliance evaluation, as required by OFCCP policy  Officials told us of the potential need for a more flexible set of investigatory tools or sanctions, such as subpoena power to speed up data-gathering or penalties for delays in providing information, in order to obtain accurate and timely information  In the case of incomplete data, OFCCP officials said one option is to enter into an agreement with the contractor whereby the contractor will gather the missing data, and OFCCP will monitor the contractor’s efforts and review detailed records at a later date  However, they said that such an agreement could give the contractor an opportunity to modify the data in the contractor’s favor  Currently, OFCCP’s primary sanction is the threat of debarment, which makes a company ineligible to receive future federal contracts  At the same time, OFCCP officials acknowledged there may additionally be delays in their own review processes  In prior work, we’ve reported concerns by contractors and industry groups about lengthy and expansive OFCCP evaluations  However, OFCCP has not analyzed its data on closed evaluations to assess the cause of delays, which would help determine whether changes should be made to its internal processes or if stronger sanctions to obtain information from contractors are needed  Internal control standards state that management should identify, analyze, and respond to risks related to achieving its objectives  Further, it states that management should design appropriate mechanisms to enforce its directives to achieve those objectives and address related risks  Without more information on the root cause of the delays, these delays may continue, straining resources and inhibiting OFCCP’s efforts to identify potential discrimination  “An affirmative action program is a management tool designed to ensure equal employment opportunity  A central premise underlying affirmative action is that, absent discrimination, over time a contractor’s workforce, generally, will reflect the gender, racial and ethnic profile of the labor pools from which the contractor recruits and selects  Affirmative action programs contain a diagnostic component which includes a number of quantitative analyses designed to evaluate the composition of the workforce of the contractor and compare it to the composition of the relevant labor pools  Affirmative action programs also include action- oriented programs  If women and minorities are not being employed at a rate to be expected given their availability in the relevant labor pool, the contractor’s affirmative action program includes specific practical steps designed to address this underutilization ” “The placement goal-setting process       contemplates that contractors will, where required, establish a single goal for all minorities  In the event of a substantial disparity in the utilization of a particular minority group or in the utilization of women or women of a particular minority group, a contractor may be required to establish separate goals for those groups ” According to OFCCP officials, a contractor may be required to establish separate goals for particular minority groups as part of a compliance review  We found, however, that OFCCP’s regulations do not require federal contractors to disaggregate demographic data for the purpose of establishing placement goals in their AAP  This may hinder their efforts to implement effective AAPs, which are designed to assist the company in achieving a workforce that reflects the gender, racial, and ethnic profile of the labor pools from which the contractor recruits and selects  OFCCP officials in headquarters and in the field said, based on their experience evaluating companies’ compliance, it was not common for companies to have placement goals disaggregated by race and ethnicity in their AAPs  A diversity and inclusion officer we interviewed from one large technology contractor noted that the requirement in the AAP to identify the need for placement goals for minorities as a whole does not address underrepresentation in certain minority groups  According to the officer, the company does not count Asian workers in setting the company’s diversity goals because Asians are well represented and the company believes it should set a placement goal for groups for which the company knows it needs to make progress  Citing comments received during development of other regulations, OFCCP officials cautioned that an analysis of utilization disaggregated by race/ethnicity may be more challenging for smaller companies with fewer employees  Further, looking at trends in diversity for minorities as a whole may not assist a company’s affirmative action efforts to identify groups that need particular outreach or support  Specifically, our analysis of workforce data found differences in representation for Black and Hispanic workers in the technology workforce compared to Asian workers  Under the current AAP regulations, companies may opt not to detect and address underrepresentation of particular minority groups since OFCCP does not require placement goals disaggregated by race/ethnicity  While OFCCP may be able to detect underrepresentation of particular minority groups during its reviews, the office reviews only 2 percent of federal contractor establishments each year  OFCCP officials said that they would need to amend their regulations in order to require disaggregated race/ethnicity information for placement goals on AAPs  The officials said disaggregating race in placement goals could help an establishment determine how to tailor outreach accordingly or better identify impediments to its equal employment opportunity efforts  However, they have not pursued this regulatory change because of competing priorities on their regulatory agenda  OFCCP’s mission includes holding federal contractors responsible for the legal requirements to take affirmative action and not discriminate against protected groups  However, not requiring contractors to set placement goals for each minority group may hinder OFCCP’s ability to effectively achieve this mission  OFCCP has not reviewed key aspects of its current approach to evaluations OFCCP officials report the agency intends to incorporate additional information on gender, racial, and ethnic disparities by industry into its compliance evaluation selection process, but we found the methodology to determine the disparities may have weaknesses  We have previously reported on the challenges OFCCP faces with its enforcement efforts, and identified additional areas that may limit OFCCP’s enforcement of federal contractors’ equal employment and affirmative action efforts  For example, our 2016 report found that OFCCP’s weak compliance evaluation selection process, reliance on voluntary compliance, and lack of staff training create several challenges to its enforcement efforts  This report found that because OFCCP was not able to identify which factors are associated with risk of noncompliance, the agency does not have reasonable assurance that it is focusing its efforts on those contractors at greatest risk of not following nondiscrimination or affirmative action requirements  OFCCP agreed with recommendations we made to address these areas and detailed steps the agency would take  In particular, to strengthen its compliance evaluation process to select contractors at greatest risk of potential discrimination, the agency stated that it planned to incorporate information on pay disparities and employment disparities  OFCCP officials indicated this information would be based on analysis of gender and race/ethnicity by industry using ACS data and EEO-1 compensation data that was to be collected beginning March 2018  However, in August 2017, the Office of Management and Budget issued a memo suspending the pay-related data collection aspects of the EEO-1 form  Despite this change, OFCCP officials said they are exploring other options for focusing on compensation disparities by industry, including through the use of ACS data, administrative data, a previous study conducted by the Department of Labor, as well as options proposed by contractors  We also found OFCCP’s current methodology for identifying disparities by industry with the ACS data may have some weaknesses that could affect the accuracy of the outcomes  For example, its reliance on the broadest industry level available may not sufficiently identify specific industries at elevated risk  Further, the methodology includes future plans to conduct the analysis for metropolitan areas  Given the importance of regional and local labor markets for assessing affirmative action efforts, regional and local analysis should also be completed before OFCCP incorporates this analysis into its selection process  It is important that OFCCP use reliable information in modifying its basic processes and setting priorities  For the reasons cited earlier regarding the importance of using quality information to make management decisions, it is important that OFCCP assess the quality of the methods for its analysis of employment disparities among industries  Without doing so, OFCCP may not accurately identify industries at greatest risk of potential noncompliance with nondiscrimination and affirmative action requirements so it can focus its limited investigation resources most effectively  Further, according to OFCCP officials, although the agency has made slight changes to various thresholds and factors for its selection process, the agency has not made any significant changes to the selection process for about 10 years, and has made no changes to its establishment-based approach since OFCCP was founded in 1965  While OFCCP currently grounds its review of a contractor in a particular physical establishment, OFCCP officials acknowledged the changing nature of a company’s work can involve multiple locations and corresponding changes in the scope of hiring and recruitment  Officials we interviewed from five of our eight selected technology companies discussed their work spread across locations, including the United States or overseas, and the related challenges they face with OFCCP’s establishment-based approach to reviews  One company representative said the AAP is not useful because site specific plans do not connect to business decisions  However, OFCCP has not reviewed the implications for the effectiveness of its mission of continuing with its establishment-based approach to conducting compliance evaluations  In addition, OFCCP officials acknowledged their inability, in identifying establishments for review, to consistently identify and include all subcontractors to which OFCCP rules should apply  They said the agency has not assessed the potential significance of any omissions of subcontractors from the oversight process  Internal control standards state that management should identify risks throughout the entity related to achieving its defined objectives to form a basis for designing risk responses, as well as the importance of periodically reviewing policies, procedures and related control activities for continued relevance and effectiveness in achieving the agency’s objectives  OFCCP officials said they have informally discussed how to adjust their work based on how work is performed in today’s economy—with virtual sites, workplace flexibilities, and nontraditional forms of employment  However, due to competing priorities, they have not conducted a formal review of these key aspects of its current approach to selecting entities for review  They acknowledged such a review would be useful  Without assessing its current approach to its establishment-based reviews and identification of all relevant subcontractors, OFCCP does not have reasonable assurance that its approach can identify discrimination occurring within the companies it oversees and may be missing opportunities to identify more effective practices or adjust its methods to external changes  While OFCCP has offered an option—the Functional Affirmative Action Program (FAAP)—for companies to move away from establishment- based reviews and which may be more appropriate for some multi– establishment contractors, uptake has been low and the agency has not conducted an evaluation of this program  Since 2002, OFCCP has allowed companies to create FAAPs, with OFCCP approval, which are based on a business function or unit that may exist at multiple establishments  As of May 2017, 73 companies across all industries had FAAPs in place  Further, some of the companies we interviewed were unaware that the FAAP was an option or believed it was cumbersome to establish given the complexity of their workforce  Asked why the FAAP has not been more broadly adopted, OFCCP officials hypothesized it could have to do with a requirement intended to ensure that companies with FAAPs would be reviewed at least as often as others, but that may result in these companies being reviewed more often than most  Standards for internal control for government agencies state that management should periodically review policies, procedures, and related control activities for continued relevance and effectiveness in achieving the entity’s objectives  Reviewing and refining the FAAP program could help OFCCP improve its ability to achieve its objectives and may provide broader insight for OFCCP’s overall enforcement approach  Jobs in the high paying technology sector are projected to grow in coming years  Female, Black, and Hispanic workers, however, comprised a smaller proportion of technology workers compared to their representation in the general workforce from 2005 through 2015, and have also been less represented among technology workers inside the technology sector than outside it  Both EEOC’s and OFCCP’s mission is to combat discrimination and support equal employment opportunity for U S  workers; however, weaknesses in their processes impact the effectiveness of their efforts  When conducting investigations, EEOC has not been consistently capturing information on industry codes  This impedes its ability to conduct industry sector analysis that could be used to more effectively focus its limited enforcement resources and outreach activities  Similarly, OFCCP faces delays in its compliance review process but it has not analyzed its closed evaluations to understand the causes of these delays and whether its processes need to be modified to reduce them  In addition, as part of their affirmative action programs federal contractors are only required to set placement goals for all minorities in general  By not requiring contractors to disaggregate demographic data for the purpose of establishing placement goals, OFCCP has limited assurance that these contractors are setting goals that will address potential underrepresentation in certain minority groups  Further, OFCCP plans to incorporate information on disparities by industry into its process for selecting establishments for compliance evaluations, but has not fully assessed its planned methods  Without such assessment, OFCCP may use a process that does not effectively identify the industries at greatest risk of potential noncompliance with nondiscrimination and affirmative action requirements  In addition, key aspects of OFCCP’s approach to compliance reviews of contractors’ affirmative action efforts have not changed in over 50 years, whereas the structure and locations of these companies’ work have changed  Finally, although OFCCP has developed an alternative affirmative action program for multi-establishment contractors, few contractors participate in this program  Because OFCCP has not evaluated the program, it does not have information to determine why there has not been greater uptake and whether it provides a more effective alternative to an establishment-based AAP  We are making a total of six recommendations, including one to EEOC and five to OFCCP  Specifically: The Chair of the EEOC should develop a timeline to complete the planned effort to clean IMS data for a one-year period and add missing industry code data  (Recommendation 1) The Director of OFCCP should analyze internal process data from closed evaluations to better understand the cause of delays that occur during compliance evaluations and make changes accordingly  (Recommendation 2) The Director of OFCCP should take steps toward requiring contractors to disaggregate demographic data for the purpose of setting placement goals in the AAP rather than setting a single goal for all minorities, incorporating any appropriate accommodation for company size  For example, OFCCP could provide guidance to contractors to include more specific goals in their AAP or assess the feasibility of amending their regulations to require them to do so  (Recommendation 3) The Director of OFCCP should assess the quality of the methods used by OFCCP to incorporate consideration of disparities by industry into its process for selecting contractor establishments for compliance evaluation  It should use the results of this assessment in finalizing its procedures for identifying contractor establishments at greatest risk of noncompliance  (Recommendation 4) The Director of OFCCP should evaluate the current approach used for identifying entities for compliance review and determine whether modifications are needed to reflect current workplace structures and locations or to ensure that subcontractors are included  (Recommendation 5) The Director of OFCCP should evaluate the Functional Affirmative Action Program to assess its usefulness as an effective alternative to an establishment-based program, and determine what improvements, if any, could be made to better encourage contractor participation  (Recommendation 6) We provided a draft of this report to the Departments of Labor (DOL), Commerce, the Equal Employment Opportunity Commission (EEOC) and the National Science Foundation (NSF)  We received written comments from DOL that are reproduced in appendix V  In addition, DOL, Commerce, EEOC, and NSF provided technical comments which we incorporated into the report as appropriate  DOL agreed with 4 of the 5 recommendations we made to improve oversight of federal contractors, and identified some steps it plans to take to implement them  Specifically, the department agreed with our recommendations to analyze internal process data to better understand the cause of delays that occur during compliance evaluations, assess the quality of methods used to incorporate consideration of disparities by industry into the process to select contractors for review, and to evaluate its current approach to identifying entities for review in light of changes in workplace structures, as well as its Functional Affirmative Action Program  DOL stated that it appreciated, but neither agreed nor disagreed, with our recommendation to take steps toward requiring contractors to disaggregate demographic data for the purpose of setting placement goals in the AAP rather than setting a single goal for all minorities  The department said this would require a regulatory change with little immediate benefit as contractors are already required to collect demographic data on each employee and applicant, and must conduct in- depth analyses of their total employment processes to identify where impediments to equal opportunity exist  While we acknowledge these data collection requirements for federal contractors, we remain concerned that without requiring contractors to also establish placement goals to address any underrepresentation for specific minority groups, contractors may not develop objectives or targets to make affirmative action efforts work  We maintain, therefore, that DOL should take steps toward requiring contractors to develop placement goals disaggregated by race/ethnicity  EEOC provided us a memo that it characterized as technical comments on the draft report  In these comments, EEOC neither agreed nor disagreed with our recommendation to develop a timeline to complete its planned effort to clean IMS data for a one-year period, which would include adding missing industry codes, but stated that it was taking some actions to enhance these data  We continue to maintain a timeline should be developed to complete this review, which is needed for the commission to conduct industry sector analysis that could be used to more effectively focus its limited resources and outreach activities  EEOC also emphasized the importance of systemic investigations, noting that while outreach may be somewhat useful in generating charges, individual charges are unlikely to make a substantial impact on a systemic practice affecting an entire employment sector  We maintain that the ability to analyze IMS data by industry could help EEOC to focus its resource use, including for systemic investigations  EEOC also noted staffing and resource constraints as issues faced by the commission  As agreed with your office, unless you publicly announce the contents of this report earlier, we plan no further distribution until 30 days from the report date  At that time, we will send copies to the appropriate congressional committees, the Secretary of Labor, the Chair of the Equal Employment Opportunity Commission, the Secretary of Commerce, and the Director of the National Science Foundation  In addition, the report will be available at no charge on GAO’s website at http://www gao gov  If you or your staff should have any questions about this report, please contact me at (202) 512-7215 or brownbarnesc@gao gov  Contact points for our Offices of Congressional Relations and Public Affairs may be found on the last page of this report  GAO staff who made key contributions to this report are listed in appendix VI  Our two objectives were to: (1) identify the demographic trends in the technology workforce over the past 10 years, and (2) assess the efforts by the U S  Equal Employment Opportunity Commission (EEOC) and the Department of Labor’s Office of Federal Contract Compliance Programs (OFCCP) to oversee technology companies and technology contractors’ compliance with equal employment opportunity and affirmative action requirements  This appendix provides details of the data sources used to answer these questions, the analyses we conducted, and any limitations we encountered  There is no commonly accepted definition of the technology sector or technology-oriented occupations  To arrive at our definition for the technology sector, we identified industries with the highest concentration of technology-oriented occupations, a similar approach to what other federal agencies have used recently to analyze trends within this sector  To identify technology-oriented occupations, we reviewed relevant research and interviewed researchers and other individuals knowledgeable about the technology sector  Based on this research, we defined technology-oriented occupations to include all computer, engineering and mathematical occupations, including managers  We selected our occupations using Bureau of Labor Statistics (BLS) Standard Occupational Classification (SOC) System codes, and crosswalked those occupations to the corresponding U S  Census Bureau occupation codes to conduct our analysis  (For a complete list of the occupations we included as technology occupations, see appendix II)  We defined the technology sector as a group of industries with the highest concentration of technology workers  Using data from the American Community Survey, an ongoing national survey conducted by the U S  Census Bureau that collects information from a sample of households, we identified the 15 industries with the highest concentration of technology workers  For this analysis, we used Census industry codes since we used this dataset for many of our analyses  The concentration of technology workers in these industries ranged from a high of 62 2 percent in the computer systems design and related services industry to a low of 19 33 percent in the wired telecommunications carriers industry (see table 3)  Companies in the technology sector also employ non-technical workers, such as sales people  We cross-walked the industries we identified in the American Community Survey with corresponding industry codes from the North American Industry Classification System (NAICS), which is the standard used by federal statistical agencies in classifying business establishments  The other data sets used in this review use NAICS codes to identify industry  The NAICS system has six levels of industry classification, with the smallest level (2-digit code) providing the most general industry classification, and the largest (6-digit) providing the most specific classification  In total, we identified 55 6-digit NAICS industry codes that comprise the technology sector using this method  (See appendix III for a list of the 6-digit NAICS codes and industry names that correspond to the Census industries we identified ) We compared our list of industries to those included in the 2016 reports by EEOC and the BLS on the technology sector  While each report includes a somewhat different set of industries depending on the authors’ particular definition of technology occupations, most of the 15 industries we selected overlap with industries selected in these other reviews  Stemming from their particular focus, these reports included some additional industries and/or occupations excluded from our analysis, such as those in the life sciences  We also compared our findings on the demographic trends in the technology workforce to 2016 EEOC and Census Bureau reports that reviewed diversity in the technology sector  Despite the definitional and methodological variations, the demographic trends found in these other reports were generally comparable to our findings  To determine the demographic trends in the technology workforce over the past decade, we analyzed quantitative data on technology workers within and outside the technology sector from 2005 through 2015 from the Census Bureau’s Public Use Microdata Sample of the American Community Survey (ACS) for the years 2005, 2007, 2009, 2011, 2013, and 2015  ACS is an ongoing national survey that collects information from a sample of households  We analyzed trend data for gender, race, and ethnicity, and median salary by occupation and sector, and analyzed point-in-time data on educational background by occupation  We analyzed the percentage of technology workers who earned bachelor’s degrees in computer, engineering, mathematics, and technology fields  For median salary, we analyzed data for workers who were employed full-time, which included those who, over the past 12 months, reported usually working 35 hours or more per week and 50 weeks or more per year, and those with wages greater than zero  To account for the sample representation and design used in the ACS, we used the person weight present in the ACS data  We used the successive difference replication method to estimate the standard errors around any population estimate  For each comparison, we tested the statistical significance of the difference for men and women and for specific racial and ethnic groups at the p-value <0 05 level  In addition, we tested the statistical significance of the change between 2005 and 2015 for each gender and racial/ethnic group  For race categories using ACS data in this report, we included only non- Hispanic members of White, Black, Asian, and Other categories  For the Asian category, we included Asian American, Native Hawaiian or Other Pacific Islander  The Hispanic category incorporated Hispanics of all races  Our analysis included American Indian or Alaskan Native, and Two or More Races, in the category reported as “Other ” We assessed the reliability of the ACS generally and of data elements that were critical to our analyses and determined that they were sufficiently reliable for our analyses  Specifically, we reviewed documentation on the general design and methods of the ACS and on the specific elements of the ACS data that were used in our analysis  We interviewed Census Bureau officials knowledgeable about the ACS data and completed our own electronic data testing to assess the accuracy and completeness of the data used in our analyses  To determine workforce trends in companies within the technology sector and at leading information technology companies, we analyzed data from EEOC’s Employer Information Reports (EEO-1) for the years 2007, 2011, and 2015  We report EEO-1 data starting in 2007 because EEOC made significant changes to its requirements related to the reporting of EEO-1 data over time  For example, beginning in 2007, EEOC changed its requirements related to the reporting of data on managers and changed its practices for collecting certain racial/ethnicity information  EEO-1 reports contain firm-level data that is annually submitted to EEOC, generally by private-sector firms with at least 100 employees or federal contractors with at least 50 employees that have a contract, subcontract or purchase order amounting to $50,000 or more  Companies that fit the above criteria submit separate EEO-1 reports for their headquarters as well as each establishment facility  EEOC requires employers to use the North American Industry Classification System (NAICS) to classify their industry  To identify trends using EEO-1 data for workers, we analyzed data for companies with the NAICS codes we initially identified as technology industries  We selected the leading information technology companies using Standard & Poor’s (S&P) 500 Information Technology Index list, which identifies the largest public information technology companies at a given time  In October 2016, this list consisted of 67 companies in the world that have stocks trading with the United States, and we analyzed EEO-1 data from 65 of these companies  For both analyses, we analyzed EEO-1 data from all job categories by gender, race and ethnicity, and industry sectors  For job categories, the EEO-1 form collects data on 10 major job categories including 1) Executives, Senior Level Officials and Managers; 2) First/Mid-Level Officials and Managers; 3) Professionals; 4) Technicians; 5) Sales Workers; 6) Administrative Support Workers; 7) Craft Workers; 8) Operatives; 9) Laborers and Helpers; and 10) Service Workers  In our analysis, “all other jobs” combines sales workers, administrative support workers, craft workers, operatives, laborers and helpers, and service workers  We used the race/ethnicity categories used by the EEOC as follows: White, Black or African American, Asian (including Native Hawaiian or Other Pacific Islander), Hispanic or Latino, and “Two or more Races” (including American Indian or Alaska Native)  We assessed the reliability of the EEO-1 data and determined that despite limitations, they were sufficiently reliable for our analyses  To determine the reliability of the EEO-1 data that we received from EEOC, we interviewed knowledgeable EEOC officials, reviewed relevant documents provided by agency officials and obtained on its website, and performed manual data testing for missing variables  For our analysis of technology degree earners, we used degree completion data tabulated by the National Science Foundation from the National Center for Education Statistics’ Integrated Postsecondary Education Data System (IPEDS) for the year 2014  Using a variety of sources, such as academic research and interviews with representatives from academia, we defined technology-related fields as degree programs in computer science, engineering, and mathematics  We analyzed IPEDS data by race and gender and who had obtained a bachelor’s or master’s degree in technology-related fields  We determined that the potential external candidates for technology positions generally had obtained either a bachelor’s or a master’s degree in a technology-related field  We used the race/ethnicity categories used by IPEDS as follows: White, Black, Asian (including Pacific Islander), Hispanic, and Multiracial or other (which includes American Indian or Alaska Native, Other or Unknown Race, and Two or more Races, i e  respondents who selected one or more racial designations)  Race and ethnicity breakouts are for U S  citizens and permanent residents only, and thus do not include data on temporary residents  The analysis by gender includes temporary residents  To determine the reliability of IPEDs data, we reviewed relevant documents obtained on the National Center for Education Statistics website, such as annual methodology reports and the handbook of NCES survey methods  We determined that data from IPEDs were sufficiently reliable for our purposes  To identify how EEOC and OFCCP have overseen technology companies’ compliance with federal equal opportunity and affirmative action requirements, we reviewed relevant federal statutes and regulations, EEOC and OFCCP policies, strategic planning documents, and operational manuals  We interviewed EEOC and OFCCP officials in headquarters, and in two regional locations selected based on the large proportion of technology companies in those areas  At EEOC, we met with officials from the San Francisco and New York district offices  At OFCCP, we met with officials from the Pacific and Northeast regional offices  To explore charges of discrimination filed with the EEOC against technology companies, we planned to analyze data from the EEOC Integrated Mission System (IMS), which contains records on EEOC charges and enforcement activities  However, since industry code is not a mandatory field for investigators to complete, roughly half the entries did not have an industry code  Therefore, we could not reliably identify technology companies that have faced charges or enforcement  We attempted to match information we had developed on federal technology contractors with charges filed in the IMS database  Depending on the matching method we used, this yielded very different results and we determined this was not a sufficiently reliable method  Further, any matching method we used would have excluded technology companies that did not hold a federal contract  To obtain information on evaluations of technology contractors completed by OFCCP and complaints received against technology contractors, we took a two-step approach  First, using the Federal Procurement Data System–Next Generation (FPDS-NG), we developed a list of company establishments and their subsidiaries that received federal contract obligations in fiscal years 2011-2015 under any of the 55 NAICS codes we included above as technology industries  We selected only company establishments that received 50 percent or more of their total federal contract obligations under these NAICS codes  Each establishment was counted only once regardless of how many federal contracts it received during the time period  Using this method, we identified 43,448 establishments in our pool of “technology contractors ” To identify subsidiaries, which are also subject to OFCCP requirements and evaluations, we identified any other establishments that shared the global vendor code with the contractors we identified, regardless of their NAICS code  This yielded 2,116 additional contractors  Second, we matched the names (removing suffixes) of the technology contractors and their subsidiaries that we identified in FPDS-NG against OFCCP’s data on their evaluations of contractors to identify the evaluations of technology contractors that OFCCP opened and completed from fiscal year 2011 through fiscal year 2016  We conducted a similar matching exercise to identify the complaints OFCCP received against technology companies  In addition, we identified which of the leading technology companies had completed evaluations between fiscal year 2011 through 2016  We obtained information during interviews with researchers, and representatives of workforce and industry organizations and associations  In addition, we interviewed diversity and compliance representatives of eight of the leading information technology companies located in the San Francisco Bay area which were also federal contractors to discuss their efforts to increase diversity and to gain their perspectives on the federal role in overseeing compliance with nondiscrimination laws  These companies were: Cisco Systems, Inc  Facebook, Inc  Google Inc  Hewlett Packard Enterprise Company Intuit Inc  Oracle America, Inc  This is the list of technology occupations that we used in our analyses  We selected our occupations using Bureau of Labor Statistics (BLS) Standard Occupational Classification (SOC) System codes, and cross- walked those occupations to the corresponding U S  Census Bureau occupation codes  This is the list of the 55 6-digit North American Industry Classification System (NAICS) codes we identified as technology-related industries  To develop this list, we identified the 15 industries with the highest concentration of technology workers using U S  Census Bureau industry codes and then used the U S  Census Bureau’s 2012 Industry Code List for Household Surveys to crosswalk the Census codes with NAICS codes  In addition to the contact named above, Betty Ward-Zukerman (Assistant Director), Kate Blumenreich (Analyst-in-Charge), Sheranda Campbell, Julianne Hartmann Cutts, Clarita Mrena, Moon Parks, Alexandra Rouse, and John Yee made significant contributions to all phases of the work  Also contributing to this report were Rachel Beers, James Bennett, Hedieh Fusfield, Julia Kennon, Jean McSween, Jessica Orr, Dae Park, James Rebbe, Almeta Spencer, and Alexandra Squitieri 


Report 2:  These product characteristics may proxy for some underlying manufacturing costs or even consumer preferences  Since firms may create gender-specific product attributes—scents like “sweet petals” and “pure sport” or razor head types and colors to differentiate products between genders—we did not always keep every product attribute constant when comparing prices  The idiosyncratic error term is represented by ε  All of our regressions are weighted, with the proportion of units sold for a particular item in that year as the weight  This is because, for personal care products, there are large differences in units sold of various product types and brands, and therefore it not useful to compare simple un- weighted average prices  For example, for one company the highest selling men’s deodorant stick sold almost 12 million units in 2016, and the highest selling women’s deodorant stick sold over 8 million units  The average units sold for underarm deodorants as a whole was just over 300,000 units, and 1,000 products out of a total of almost 3,000 products had less than 100 units sold in 2016  The linear model we used has the usual shortcomings of being subject to specification bias to the extent the relationship between price and each of the independent variables is not linear  The model also does not include complete data on costs, such as advertising and packaging, or consumers’ willingness to pay, both of which have an effect on the price differences  The model may thus also be subject to omitted variable bias  In addition, the model may have some endogeneity issues to the extent the product characteristics themselves are influenced by consumers’ willingness to pay for some of those product features  To reduce the impact of any model misspecifications or heteroscedasticity, we used the robust (or Huber-White sandwich) estimator  We estimated the regression model above for each of the 10 products separately and for each of the two measures of price  We used Nielsen’s in-store, retail price scanner data, which include information on total volume sold and dollar sales for items purchased at 228 retailers including grocery stores, drug stores, mass merchandisers (such as Target), dollar stores, club stores (such as Sam’s Club), and convenience stores  The data capture 82 percent of all U S  sales  Nielsen also projects sales for the remaining noncooperating retailers, and that information is included in this dataset  We excluded some very small brands that did not have enough units sold from our regression analysis in order to avoid outliers  These brands usually had less than 50,000 units sold over the entire year, and for some products they represented less than 1 percent of all units sold  We found that average retail prices paid were significantly higher for women’s products than for men’s in 5 out of 10 personal care products  In 2 categories, men’s versions sold at a significantly higher price  One category had mixed results based on two price measures analyzed, and two others showed no significant gender price differences  A summary of our regression results is presented in table 5  We manually collected prices for 16 pairs of selected personal care products from the websites of four online retailers that also operated physical store locations  We selected comparable pairs of similar men’s and women’s products that were differentiated by product attributes, such as scent or color, and were sold at most or all of the four retailers  The products were selected based on several comparability factors such as brand, product claims, and number of blades in a razor  For two 1-week time periods in January and March 2018, we collected prices manually between 1:00 p m  and 7:00 p m  (ET) over two 7-day time periods  We collected listed prices and did not adjust the prices for any promotions that were available, such as online coupons or buy-one-get-one-free offers  Table 6 presents the results of our online price collection  These results have important limitations: The average prices shown are not generalizable to the broader universe of prices for these products sold at other times or by other online retailers  The data reflect prices advertised to consumers rather than the prices consumers actually paid  The data do not capture the volume of sales for each item for each retailer; in our analysis, we weighted all advertised prices equally across the retailers  As a result, differences we found within these advertised prices may not have translated into comparable differences in prices female and male consumers paid for these products online  The prices do not reflect any promotional discounts, volume discounts, or other discounts that may have been available to some or all consumers  This report examines (1) how prices compared for selected categories of consumer goods that are differentiated for men and women, and potential reasons for any significant price differences; (2) what is known about the extent to which men and women may pay different prices in, or experience different levels of access to, markets for credit and goods and services that are not differentiated based on gender; (3) the extent to which federal agencies have identified and taken steps to address any concerns about gender-related price differences; and (4) state and local government efforts to address concerns about gender-related price differences  To compare prices for selected goods that are differentiated for men and women, we purchased and analyzed Nielsen Company (Nielsen) data on retail prices paid for 10 personal care product categories for calendar year 2016  The product categories included underarm deodorants, body deodorants (typically sold as a spray), disposable razors, nondisposable razors, razor blades, shaving creams, shaving gels, and three categories of fragrances  We selected these categories of personal care products because they are commonly purchased consumer goods that were categorized by gender in the Nielsen data  The women’s and men’s versions of personal care products we selected are generally more similar in terms of the form, size, and packaging in comparison to certain other consumer product categories that are also differentiated by gender, such as clothing  We used regression models to analyze data on retail prices paid for the 10 categories of personal care products differentiated for women and men  To assess the reliability of the Nielsen data, we reviewed relevant documentation and conducted interviews with Nielsen representatives to review steps they took to collect and ensure the reliability of the data  In addition, we electronically tested data fields for missing values, outliers, and obvious errors  We determined that these data were sufficiently reliable for our purposes  For more details on the methodology for, and limitations of, our analysis of these retail price data, see appendix I  We also manually collected listed prices for 16 pairs of selected personal care products from four different retailer websites over two 7-day periods in January and March 2018  For each pair, we selected comparable men’s and women’s products that were differentiated by product attributes, such as scent or color, and were commonly sold across retailers  For more details on our online price data collection and the limitations associated with interpreting the results, see appendix II  To examine what is known about the extent to which men and women may be offered different prices or access for the same goods or services, we reviewed academic literature identified through a literature search covering the last 25 years  To identify existing studies from peer-reviewed journals, we conducted searches using subject and keyword searches of various databases, such as EconLit, Scopus, ProQuest, and Social SciSearch  We also used a snowball search technique—meaning we reviewed relevant academic literature cited in our selected studies—to identify additional studies  We performed these searches and identified articles from December 2016 to April 2018  From these searches, we identified 21 studies that appeared in peer-reviewed journals or research institutions’ publications from 1995 through 2016 and were relevant to gender-related price differences for the same products  We reviewed and assessed each study’s evaluation methodology based on generally accepted social science standards  See the bibliography at the end of this report for a list of the 21 studies  We then summarized the research findings  A GAO economist read and assessed each study, using the same data collection instrument  The assessment focused on information such as the types of disparities examined, the research design and data sources used, and methods of data analysis  The assessment also focused on the quality of the data used in the studies as reported by the researchers and any limitations of data sources for the purposes for which they were used  A second GAO economist reviewed each completed data collection instrument to verify the accuracy of the information included  As a result, the 21 studies that we selected for our review met our criteria for methodological quality  We found the studies we reviewed to be reliable for purposes of determining what is known about price differences for the same products  However, these studies have important limitations, such as using nonrepresentative data samples, and the results are not generalizable  To examine the federal role in overseeing gender-related price differences, we reviewed relevant federal statutes and agency guidance, and interviewed officials from the Federal Trade Commission (FTC), Bureau of Consumer Financial Protection (BCFP), the Department of Housing and Urban Development (HUD), and the Department of Justice (DOJ)  To help identify the extent of concerns about gender-related price differences, we interviewed representatives from eight consumer groups, three industry associations, and four academic experts  Additionally, we reviewed a sample of consumer complaints from databases managed by BCFP, FTC, and HUD (Consumer Complaint Database, Consumer Sentinel Network, and Enforcement Management System, respectively)  Complaints were submitted by consumers across the United States about various financial products, housing grievances, and other consumer protection concerns  To identify our universe of gender-related consumer complaints in BCFP and FTC databases, we used the following search terms that targeted sex or gender discrimination: discriminat, unfair, treat, decept, abus, female, woman, women, man, men, male, gender, sex, female, woman, women, man, men, male, gender, and sex  HUD’s consumer complaint database is categorized by protected class (e g , race, sex, national origin), so we did not need to use search terms to identify gender-related complaints  For the years 2012 through 2017, we identified 6,117 BCFP consumer complaint narratives; 10,472 FTC consumer complaints narratives; and 5,421 HUD consumer complaint narratives that were relevant to our scope  We then drew a stratified random probability sample of 100 gender-related consumer complaints from each database  To determine which complaints in our samples were about price differences related to gender or sex, two team members read through each complaint narrative and coded whether or not the complainant’s narrative indicated that they felt that they paid or were charged more because of their gender or sex  A third team member conducted a final review of the results, and made a final determination in cases where there were differences in the first two team member’s assessments  With this probability sample, each member of the study population had a nonzero probability of being included, and that probability could be computed for any member  We followed a probability procedure based on random selections and our sample is only one of a large number of samples that we might have drawn  Since each sample could have provided different estimates, we express our confidence in the precision of our particular sample’s results as a 95 percent confidence interval (with a margin of error of 5 9 percent)  This is the interval that would contain the actual population value for 95 percent of the samples we could have drawn  We assessed the reliability of these data by reviewing documentation and interviewing agency officials about the databases used to collect these complaints  We determined that these data were sufficiently reliable for our purposes of identifying complaints of gender- related price differences  To explore state and local efforts to address concerns about gender- related price differences, we conducted a literature search and identified three state or local laws or ordinances that specifically address gender- related price differences: California, Miami-Dade County, Florida, and New York City, New York  We reviewed these laws and ordinances and interviewed officials from these jurisdictions to discuss motivations for, oversight of, and the impact of these laws  We conducted this performance audit from October 2016 to August 2018 in accordance with generally accepted government auditing standards  Those standards require that we plan and perform the audit to obtain sufficient, appropriate evidence to provide a reasonable basis for our findings and conclusions based on our audit objectives  We believe that the evidence obtained provides a reasonable basis for our findings and conclusions based on our audit objectives  For each of 10 personal care product categories we analyzed, we compared the overall average prices for women’s products and men’s products using two measures of average price: average item price and average price per ounce or count  While the second price measure adjusts the average price for quantity of product, these comparisons did not take into account the effect on price of differences in product brand, packaging, and other characteristics  As shown in table 7, adjusting the average item price to account for differences in product quantity (ounces or count) significantly affected the size and magnitude of gender price differences for several product categories  This is because men’s products in the dataset were frequently larger in size or count compared with women’s products in the same category  For example, women’s disposable razors sold for 11 percent less than those targeted to men when we compared average item prices  However, when we compared average price per count of razors, women’s disposable razors sold for 19 percent more on average than men’s  This is because women’s disposable razors had on average about one fewer razor per package  In 5 out of 10 product categories, women’s versions of the product on average sold for a higher price per ounce or count than men’s and these differences were statistically significant at the 95 percent confidence level for 4 products and at the 90 percent level for one product  Information about sales and relative sizes of different products targeted to men and women are presented in table 8 below  This appendix provides additional details about the consumer complaint processes at the Bureau of Consumer Financial Protection (BCFP), Federal Trade Commission (FTC), and Department of Housing and Urban Development (HUD)  Consumers with a complaint about unfair treatment related to gender could submit a complaint to one of these agencies  BCFP and FTC monitor consumer complaints related to violations under the Equal Credit Opportunity Act, while HUD and the Department of Justice (DOJ) investigate housing discrimination complaints under the Fair Housing Act  These complaints could be about price differences because of gender  Alicia Puente Cackley, (202) 512-8678 or cackleya@gao gov  In addition to the contact named above, John Fisher (Assistant Director), Jeff Harner (Analyst in Charge), Vida Awumey, Bethany Benitez, Namita Bhatia-Sabharwal, Kelsey Kreider, and Kelsey Sagawa made key contributions to this report  Also contributing to this report were Abigail Brown, Michael Hoffman, Jill Lacey, Oliver Richard, Tovah Rom, and Paul Schmidt  We reviewed literature to identify what is known about the extent to which female and male consumers may face different prices or access in markets for credit and goods and services that are not differentiated based on gender  This bibliography contains citations for the 20 studies and articles that we reviewed that compared prices or access for female and male consumers in markets where the product is not differentiated by gender (mortgages, small business credit, auto purchases, and auto repairs)  Asiedu, Elizabeth, James A  Freeman, and Akwasi Nti-Addae  “Access to Credit by Small Businesses: How Relevant Are Race, Ethnicity, and Gender?” The American Economic Review, vol  102, no  3 (2012): 532- 537  Ayers, Ian and Peter Siegelman  “Race and Gender Discrimination in Bargaining for a New Car ” The American Economic Review, vol  85, no  3  (1995): 304-321  Blanchard, Lloyd, Bo Zhaob, and John Yinger  “Do lenders discriminate against minority and woman entrepreneurs?” Journal of Urban Economics 63 (2008): 467–497  Blanchflower, David G , Phillip B  Levine, and David J  Zimmerman  “Discrimination in the Small-Business Credit Market ” The Review of Economics and Statistics, vol  85, no  4 (2003): 930-943  Busse, Meghan R , Ayelet Israeli, and Florian Zettelmeyer  “Repairing the Damage: The Effect of Price Expectations on Auto Repair Price Quotes ” National Bureau of Economic Research, Working Paper 19154 (2013)  Cavalluzzo, Ken S , Linda C  Cavalluzzo, and John D  Wolken  “Competition, Small Business Financing, and Discrimination: Evidence from a New Survey ” The Journal of Business, vol  75, no  4 (2002): 641- 679  Cheng, Ping, Zhenguo Lin, and Yingchun Liu  “Do Women Pay More for Mortgages?” The Journal of Real Estate Finance and Economics, vol  43 (2011): 423-440  Cheng, Ping, Zhenguo Lin, and Yingchun Liu  “Racial Discrepancy in Mortgage Interest Rates ” The Journal of Real Estate Finance and Economics, vol  51 (2015): 101-120  Cole, Rebel, and Tatyana Sokolyk  “Who Needs Credit and Who Gets Credit? Evidence from the Surveys of Small Business Finances”  Journal of Financial Stability, vol  24 (2016), 40-60  Coleman, Susan  “Access to Debt Capital for Women- and Minority- Owned Small Firms: Does Educational Attainment Have an Impact?” Journal of Developmental Entrepreneurship, vol  9, no  2 (2004): 127-143  Duesterhas, Megan, Liz Grauerholz, Rebecca Weichsel, and Nicholas A  Guittar  “The Cost of Doing Femininity: Gendered Disparities in Pricing of Personal Care Products and Services,” Gender Issues, vol  28, (2011): 175-191  Goodman, Laurie, Jun Zhu, and Bing Bai  “Women Are Better than Men at Paying Their Mortgages ” Urban Institute, Research Report (2016)  Haughwout, Andrew, et al  “Subprime Mortgage Pricing: The Impact of Race, Ethnicity, and Gender on the Cost of Borrowing ” Brookings- Wharton Papers on Urban Affairs (2009): 33-63  Mijid, Naranchimeg  “Gender differences in Type 1 credit rationing of small businesses in the US ” Cogent Economics & Finance, vol  3 (2015)  Mijid, Naranchimeg  “Why are female small business owners in the United States less likely to apply for bank loans than their male counterparts?” Journal of Small Business & Entrepreneurship, vol  27, no  2 (2015): 229- 249  Mijid, Naranchimeg and Alexandra Bernasek  “Gender and the credit rationing of small businesses ” The Social Science Journal, vol  50 (2013): 55-65  Morton, Fiona Scott, Florian Zettelmeyer, and Jorge Silva-Risso  “Consumer Information and Price Discrimination: Does the Internet Affect the Pricing of New Cars to Women and Minorities?” National Bureau of Economic Research, Working Paper 8668 (2001)  O’Connor, Sally  “The Impact of Gender in the Mortgage Credit Market ” University of Wisconsin-Milwaukee Doctoral Dissertation (1996)  Van Rensselaer, Kristy N , et al  “Mortgage Pricing and Gender: A Study of New Century Financial Corporation ” Academy of Accounting and Financial Studies Journal, vol  18, no  4 (2014): 95-110  Wyly, Elvin and C S  Ponder  “Gender, age, and race in subprime America ” Housing Policy Debate, vol  21, no  4 (2011): 529-564  Zimmerman Treichel, Monica and Jonathan A  Scott  “Women-Owned Businesses and Access to Bank Credit: Evidence from Three Surveys Since 1987 ” Venture Capital, vol  8, no  1 (2006): 51-67 

ground_truth: The LDA, as amended, requires lobbyists to file quarterly disclosure reports and semiannual reports on certain political contributions. The law also includes a provision for GAO to annually audit lobbyists' compliance with the LDA. GAO's objectives were to (1) determine the extent to which lobbyists can demonstrate compliance with disclosure requirements, (2) identify challenges to compliance that lobbyists report, and (3) describe the resources and authorities available to USAO in its role in enforcing LDA compliance, and the efforts USAO has made to improve enforcement. This is GAO's 11th report under the provision. GAO reviewed a stratified random sample of 98 quarterly disclosure LD-2 reports filed for the third and fourth quarters of calendar year 2016 and the first and second quarters of calendar year 2017. GAO also reviewed two random samples totaling 160 LD-203 reports from year-end 2016 and midyear 2017. This methodology allowed GAO to generalize to the population of 45,818 disclosure reports with $5,000 or more in lobbying activity, and 30,594 reports of federal political campaign contributions. GAO also met with officials from USAO to obtain status updates on its efforts to focus resources on lobbyists who fail to comply. GAO is not making any recommendations in this report. GAO provided a draft of this report to the Department of Justice for review and comment. The Department of Justice provided technical comments, which GAO incorporated as appropriate. For the 2017 reporting period, most lobbyists provided documentation for key elements of their disclosure reports to demonstrate compliance with the Lobbying Disclosure Act of 1995, as amended (LDA). For lobbying disclosure (LD-2) reports and political contributions (LD-203) reports filed during the third and fourth quarter of 2016 and the first and second quarter of 2017, GAO estimates that 87 percent of lobbyists filed reports as required for the quarter in which they first registered; the figure below describes the filing process and enforcement; 99 percent of all lobbyists who filed (up from 83 percent in 2016) could provide documentation for income and expenses; and 93 percent filed year-end 2016 LD-203 reports as required. These findings are generally consistent with prior reports GAO issued for the 2010 through 2016 reporting periods. However, in recent years GAO's findings showed some variation in the estimated percentage of reports with supporting documentation. For example, an estimated increase in lobbyists who could document expenses is notable in 2017 and represents a statistically significant increase from 2016. As in GAO's other reports, some lobbyists were still unclear about the need to disclose certain previously held covered positions, such as paid congressional internships or certain executive agency positions. GAO estimates that 15 percent of all LD-2 reports may not have properly disclosed previously held covered positions. On the other hand, over the past several years of reporting on lobbying disclosure, GAO found that most lobbyists in the sample rated the terms associated with LD-2 reporting as “very easy” or “somewhat easy” to understand. The U.S. Attorney's Office for the District of Columbia (USAO) stated it has sufficient resources and authority to enforce compliance with the LDA. USAO continued its efforts to bring lobbyists into compliance by reminding them to file reports or by applying civil penalties.