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



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

Report 1: Many consumer products—such as deodorants, shaving products, and hair care products—are differentiated to appeal specifically to men or women through differences in packaging, scent, or other product characteristics (see fig  1)  These differences related to gender can affect manufacturing and marketing costs that may contribute to price differences in products targeted to different genders  However, firms may also charge consumers different prices for the same (or very similar) goods and services even when there are no differences in costs to produce  To maximize profits, firms use a variety of techniques to charge prices close to the highest price different consumers are willing to pay  Firms may attempt to get segments of the consumer market to pay a higher price than another segment by slightly altering or differentiating the product  Based on the differentiated products, consumers self-select into different groups according to their preferences and what they are willing to pay  For example, some consumer goods have different versions of what is essentially the same product—except for differences in packaging or features, such as scent—with one version intended for women and another version intended for men  The two products may be priced differently because the firm expects that one gender will be willing to pay more for the product than the other based on preference for certain product attributes  Firms may also use some group characteristic, such as age or gender, to charge different prices because some groups may have differences in willingness or ability to pay  For example, a firm may offer discounted movie tickets to students or seniors, as they may have less disposable income  For the seller the cost of providing the movie is the same for any customer, but the seller is able to maximize its profits by offering tickets to different groups of customers at different prices  A firm’s ability to differentiate prices depends on multiple factors, such as the firm’s market power (so that competitors cannot put downward pressure on prices to eliminate the price differences), the presence of consumer segments with different demands and willingness to pay, and control over the sale of its product so it cannot be easily resold to exploit price differences  In addition, the extent to which consumers pay different prices for the same or similar goods can depend on other factors, such as consumers’: willingness to purchase an item they believe may be priced higher for ability to compare prices and product characteristics and choose a product based on its characteristics rather than its price, choices about whether to purchase a more expensive version of the product (e g , a branded item versus a cheaper store brand), choices about where to purchase the item (i e , when different retailers sell the same item at different prices), and use of coupons or promotions  No federal law expressly prohibits businesses from charging different prices for the same or similar consumer goods and services targeted to men and women  However, consumer protection laws do prohibit sex discrimination in credit and real estate transactions  Specifically, the Equal Credit Opportunity Act (ECOA) prohibits creditors from discriminating against credit applicants based on sex or certain other characteristics and the Fair Housing Act (FHA) prohibits discrimination in the housing market on the basis of sex or certain other characteristics  ECOA and FHA (collectively known as the fair lending laws) prohibit lenders from, among other things, refusing to extend credit or using different standards in determining to extend credit based on sex  Credit, such as a credit card account or mortgage loan, is generally made available and priced based on a number of risk factors, including credit score, income, and employment history  A borrower with a lower credit score is likely to pay a higher interest rate on a loan, reflecting the greater risk to the lender that the borrower could default on the loan  In addition to the interest rate, borrowing costs for consumers can also include fees and other costs charged by lenders or brokers  However, there may be differences in average outcomes for men and women—such as for availability of credit or interest rates—if there are differences related to gender in the factors that determine creditworthiness, such as income  BCFP, FTC, the federal prudential regulators, and DOJ have the authority to investigate alleged violations of ECOA and are primarily responsible for enforcing the act’s requirements, while HUD and DOJ share responsibility for enforcing the provisions of FHA  Further, BCFP and the prudential regulators oversee regulated entities for compliance with ECOA by, among other things, collecting complaints from the public and through routine inspections of the financial institutions they oversee  HUD and DOJ have the authority to bring enforcement actions for alleged violations of FHA  In 5 out of 10 product categories we analyzed, personal care products targeted to women sold at higher average prices than those targeted to men after controlling for certain observable factors  For 2 of the 10 product categories, men’s versions sold at higher average prices  While the factors we controlled for likely proxy for various costs and consumer preferences, we could not fully observe all underlying differences in costs and demand for products targeted to different genders  As a result, we could not determine the extent to which the gender-based price differences we observed may be attributed to gender bias as opposed to other factors  Women’s versions of personal care products sold at a statistically significant higher average price than men’s versions for 5 of the 10 personal care product categories we analyzed—using two different price measures and after controlling for observable factors that could affect price, such as brands, product size or quantity, promotional expenses (see table 1) and other product-specific attributes (e g , scent, special claims, form)  Because women’s and men’s versions of the same product were frequently sold in different sizes, we compared prices using two price measures: average item price and average price per ounce or count of product  For 2 of the 10 product categories—shaving gel and nondisposable razors—men’s versions sold at a statistically significant higher price using both price measures  For one category (razor blades), women’s versions sold at a statistically significant higher average price per count, but there was no gender price difference using average item prices  Additionally, for two product categories—disposable razors and mass-market perfumes—there were no statistically significant price differences between men’s and women’s products using either price measure  In addition to this analysis of retail price scanner data, we also manually collected advertised online prices for a limited selection of personal care products targeted to women and men from several online retailers  Some price comparisons of advertised online prices for men’s and women’s versions of a product were similar to comparisons of average prices paid based on the Nielsen retail price scanner data  For example, for three pairs of comparable underarm deodorants, the women’s deodorant was listed at a higher price per ounce on average than the men’s deodorant (see app  II)  In addition, for one pair of shaving gel products we analyzed, the men’s shaving gel was listed at a higher price per ounce on average  However, for both pairs of nondisposable razors we analyzed, the women’s razors were listed at a higher average price per count than the men’s razors  This contrasted with the Nielsen data showing that men’s nondisposable razors sold at higher prices on average than women’s  An important limitation of our analysis of these advertised prices is that we were unable to determine the extent to which consumers actually paid these prices and in what volume the products were sold, and our results are not generalizable to the broader universe of prices for these products sold at other times or by other online retailers  Though we found that the target gender for a product is a significant factor contributing to price differences we identified, we do not have sufficient information to determine the extent to which these gender- related price differences were due to gender bias as opposed to other factors  Versions differentiated to appeal to men and women can result in different costs for the manufacturer  Our econometric analysis controlled for many observable factors related to costs, such as product size, promotional activity, and packaging type  We also controlled for many product attributes such as forms, scents, and special claims that products make to account for underlying manufacturing cost differences  In addition, we controlled for brands, which can reflect consumer preferences  However, we do not have firm-level data on all cost differences—for example, those related to advertising and packaging  As a result, we could not determine the extent to which the price differences we observed may be explained by remaining cost differences between men’s and women’s products  We also do not have the data to determine the extent to which men and women have different demands and willingness to pay for a product, which would be expected to affect the prices firms charge for differentiated products  For example, some academic experts we spoke with said that women may value some product attributes, such as design and scent, more than men do  If products differentiated to incorporate those attributes do not result in different costs, then differences in prices could be part of a firm’s pricing strategy based on the willingness of one gender to pay more than another  The conditions necessary for firms to be able to implement a strategy of price differentiation likely exist for the personal care products we analyzed  First, our analysis suggests that due to industry concentration, there is limited market competition for the 10 personal care products we analyzed  With more market power, firms can more easily set different prices for different consumer segments  Second, firms have the ability to segment the market for personal care products by tailoring product characteristics related to gender, such as by labeling the product as women’s deodorant or men’s deodorant, or by altering scent or colors  Third, while men and women are able to freely purchase a product targeted to the opposite gender, certain factors may limit the extent to which this occurs  For example, some product differences such as scents may discourage one gender from buying products targeted to another gender  In addition, consumers may find it difficult and time- consuming to compare prices for similar men’s and women’s products because of the ways they are differentiated (such as product size and scents) and because they may be sold in different parts of a store  We reviewed studies that compared prices for men and women in four markets where the product or service is not differentiated by gender: mortgages, small business credit, auto purchases, and auto repairs  First, we reviewed studies on mortgage and small business credit that analyzed interest rates and access to credit to identify any differences for men and women  Second, we reviewed studies that compared prices quoted to men and women in auto purchase and repair markets  However, several of these studies have important limitations, such as using nonrepresentative data samples, and the results are not generalizable  Studies we reviewed found that women as a group pay higher interest rates on average than men in part due to weaker credit characteristics  After controlling for borrower credit characteristics and other factors, three studies did not find statistically significant differences in interest rates between men and women for the same type of mortgage, while one study found that women paid higher mortgage rates for certain subprime loans  In addition, one study found that female borrowers defaulted less frequently on their loans than male borrowers with similar credit characteristics, suggesting that women as a group may pay higher mortgage rates than men relative to their default risk  While these studies attempted to control for factors other than gender or sex that could affect borrowing costs, several lacked important data on certain borrower risk characteristics  For example, several studies we reviewed rely on Home Mortgage Disclosure Act of 1975 (HMDA) data, which did not include data on risk factors such as borrower credit scores that could affect analysis of disparities between men and women  Also, several studies analyzed nonrepresentative samples of loans, such as subprime loans or loans originated more than 10 years ago, which limits the generalizability of the results (see table 2)  Three of the studies we reviewed found that while women on average were charged higher interest rates on mortgage loans than men, this difference was not statistically significant after controlling for other factors  For example, one study found that differences in mortgage interest rates between men and women became insignificant after controlling for differences in how men and women shop for mortgage rates  The authors used data from the 2004 Survey of Consumer Finances (SCF) to analyze the effect on interest rates of mortgage features, borrower characteristics such as gender, and market conditions  However, their analysis did not include data on some borrower credit characteristics such as credit score and debt-to-income ratio that could affect borrowing costs  Another study found that women were charged higher interest rates for subprime loans made in 2005, but once the authors controlled for observed risk characteristics there was no evidence of disparity in interest rates by gender of the borrower in the subprime market  However, the authors’ data did not include any fees paid at loan origination, which could affect the overall cost of borrowing  A third study that examined disparities between men and women in subprime loans found no significant evidence that gender affected the cost of borrowing within the subprime market, though it did find that women—particularly African American women—were more likely to have subprime loans  The authors found that, even after controlling for some financial characteristics and loan terms, single African American women were more likely than non-Hispanic white couples to have subprime loans  One study analyzed subprime loans made by one large lender from 2003 through 2005 and found that women paid more for subprime mortgages than men after controlling for some risk factors  This study found that women had higher average borrowing costs—as measured by annual percentage rate—than men, and controlling for credit characteristics such as credit scores and debt-to-income ratios did not fully explain the differences  However, the authors did not control for other factors that could also affect borrowing costs, such as differences in education, shopping behaviors, and geographic location  Additionally, a research paper found that female-only borrowers—that is, where the only borrower is a woman—default less than male-only borrowers with similar loans and credit characteristics  The authors found that female-only borrowers on average pay more for their mortgage loans because they generally have weaker credit characteristics, such as lower income, and also because a higher percentage of these mortgage loans are subprime  However, after controlling for credit characteristics such as credit score, loan term, and loan-to-value ratio, among others, the analysis showed that these weaker credit characteristics do not accurately predict how well women pay their mortgage loans  Since pricing is tied to credit characteristics and not performance, women may pay more relative to their actual risk than do similar men  Studies we reviewed on small business loans generally did not find differences in interest rates, though some found differences in denial rates and other accessibility issues between female- and male-owned firms  Most of the studies we reviewed used data from the 1993, 1998, or 2003 Survey of Small Business Finances (SSBF), which could limit the applicability or relevance of their findings today  A study that analyzed data from the 1993 SSBF did not find evidence that businesses owned by women paid more for credit than firms owned by white men  However, when the authors took into account the market concentration and competition, they found that white female-owned firms experienced increased denial rates in less competitive markets  In addition, the study found that women may avoid applying for credit in those markets because of the fear of being denied  For example, almost half of all small business owners who needed credit reported that they did not apply for credit, and these rates were even higher for businesses owned by women and minorities  Other studies found that women may have less access to small business credit than men, in part because of higher denial rates and because they may not apply for credit out of fear of rejection  For example, one study found that women-owned firms have higher loan denial rates compared with men; however, this is mainly due to differences in business characteristics of female- and male-owned firms  The authors also found that even when denial rates are the same for small businesses with similar characteristics, women’s loan application rates are lower, suggesting that women may be discouraged from applying for credit by the higher overall denial rates for female-owned firms  Another study by one of the same authors examined the reasons why female borrowers may be discouraged from applying for a business loan compared to male business owners and found that it was mainly because they fear that their application will be rejected  A third study by the same author found that women in general did not have less access to credit than men, though newer female-owned firms received significantly lower loan amounts than requested compared to their male-owned counterparts  Similarly, the study also found that women with few years of experience managing or owning a business received significantly lower loan amounts compared with men with similar years of experience  A fourth study looked at six different types of loans, including lines of credit, and found that white women were significantly more likely than white men to avoid applying for a loan because they assume they would be denied  However, once the authors’ model controlled for education differences, all gender disparities in applying for credit disappeared, though white women were still less likely than white men to have loans  Studies we reviewed on auto purchases and repairs found that a seller’s expectation of what customers are willing to pay and how informed they seemed can differ by gender, which can affect the price customers are quoted  However, these studies were published in 1995 and 2001, which may limit the applicability or relevance of their findings today  The 2001 study we reviewed on auto purchases found that though women paid higher prices than men for car purchases on average, these differences declined when cars were purchased online  The authors suggest that this may be because Internet consumers can effectively convey their level of price knowledge and therefore may seem better informed to the sellers  They also suggest it could be because the dealerships have less information about online consumers and their willingness to pay, which may limit the extent of price differentiation  The 1995 study on auto purchases found that the dealers quoted significantly lower prices to white males than to female or African American test buyers using identical, scripted bargaining strategies in part because dealers may have made assumptions about women’s willingness to bargain for lower prices  We also reviewed one study on auto repairs that found that women were quoted higher prices than men if they seemed uninformed about the cost of car repair when requesting a quote, but the price differences disappeared if the study participant mentioned an expected price  The study suggests that a potential explanation for this result could be that auto repair shops expect women to accept a price that is higher than the market average and men to accept a price below it  BCFP and HUD have responsibilities to monitor consumer complaints in the consumer credit and housing markets, respectively  Additionally, FTC monitors complaints about the consumer credit and consumer goods markets  All three agencies play a role in potentially monitoring or addressing issues of gender-related price differences and have online complaint forms for submission of consumer complaints: BCFP collects and reviews consumer complaints about financial products and services and provides complaints and related data in its Consumer Complaint Database  In 2017 BCFP received approximately 320,200 consumer complaints  The products that generated the most complaints in 2017 were “Credit or consumer reporting,” “Debt collection,” and “Mortgage " According to BCFP officials, BCFP also analyzes loan and demographics data collected through HMDA and other data sources to monitor and identify market trends  In addition, BCFP and the federal financial regulators examine fair lending practices of the institutions they regulate, and these examinations have uncovered sex discrimination in credit products by FDIC and NCUA  FTC receives complaints and the complaints are stored in the Consumer Sentinel Network, a database of consumer complaints received by FTC, as well as those filed with other federal and state agencies and organizations, such as mass marketing fraud complaints from the Council of Better Business Bureaus  The complaints in the Consumer Sentinel Network focus on consumer fraud, identity theft, and other consumer protection matters, such as debt collection, and can include complaints related to consumer credit markets  HUD receives consumer complaints about potential FHA violations through its website, via its toll-free phone hotline, and in writing  HUD monitors those complaints through its online HUD Enforcement Management System  HUD investigates all complaints for which it has jurisdictional authority  HUD may monitor complaints to identify trends, but HUD officials stated that the agency does not generally monitor consumer credit and housing market data, absent a specific complaint  In cases where HUD has jurisdictional authority under FHA, HUD offers conciliation between the parties  If resolution is not reached, and HUD determines there is reasonable cause to believe a violation has occurred, the parties may elect to have the matter heard in U S  District Court or at HUD  In their oversight of federal antidiscrimination statutes, BCFP officials said they have not identified significant consumer concerns about price differences based on a consumer’s sex or gender  FTC and HUD officials identified some examples of concerns of this nature  For example, FTC has taken enforcement actions alleging unlawful race- and gender-related price differences  HUD has also identified several cases where pregnant women and their partners applied for a mortgage while the woman was on maternity leave, and the couple’s mortgage loan application was denied  BCFP, FTC, and HUD have received few consumer complaints about price differences related to sex or gender, according to our analysis of a sample of each agency’s 2012–2017 complaint data (see table 3)  In separate samples of 100 gender-related complaints at BCFP, HUD, and FTC, we found that 0, 4, and 1 complaint, respectively, were related to price differences based on sex or gender  Three of the complaints from HUD also cited differences in price based on other protected classes (such as race or ethnicity)  Half of the academic experts and consumer groups we interviewed told us that in some markets it is difficult for consumers to observe and compare prices paid by other consumers, such as when prices are not posted or can be negotiated (e g , car sales)  In such cases, consumers may not know if other consumers are paying a higher or lower price than the price quoted to them  Most academic experts also told us that when consumers are aware that price differences could exist, they may make different decisions when making purchases  Additionally, officials from BCFP noted that price differences related to gender may be difficult for consumers to identify, or that consumers may not know where to complain  The consumer education resources of BCFP, FTC, and HUD provide general consumer education resources on discrimination (i e , consumer user guide or a website) and consumer awareness  Officials from BCFP and HUD said they have not identified a need to develop other consumer education resources specific to gender-related price differences  For example, BCFP’s print and online consumer education materials are intended to inform consumers of their rights and protections related to credit discrimination, which includes discrimination based on sex or gender  The three agencies’ consumer education materials also provide advice that could help consumers avoid paying higher prices regardless of their gender—such as home-buying resources and resources on comparison shopping  However, the agencies have not developed additional educational resources focused specifically on potential gender- related price differences in part because few complaints on this topic have been collected in their databases, agency officials told us  FTC officials noted that it tries to focus its education efforts on topics that will have the greatest benefit to consumers, often determined by information it gathers through complaints and investigations  Representatives of five consumer groups and industry associations told us that they have received few complaints about gender-related price differences  However, four consumer groups noted that low concern could be the result of consumers being unaware of price differences related to gender  For example, as indicated above, price differences related to gender may be difficult for consumers to identify when they cannot determine whether they are paying a higher price than others  Representatives of two retailing industry associations similarly stated that they have not heard concerns about price differences related to gender  In response to consumer complaints or concerns about gender disparities in pricing, at least one state (California) and two municipalities (Miami- Dade County and New York City) have passed laws or ordinances to prohibit businesses from charging different prices for the same or similar goods or services solely based on gender (see table 4)  In addition, two of these laws included requirements related to promoting price transparency  California enacted the Gender Tax Repeal Act of 1995, which prohibits businesses from charging different prices for the same or similar services based on a consumer’s gender  The law also requires certain businesses to display price information and disclose prices upon request, according to state officials with whom we spoke  Similarly, in 1997, Miami-Dade County passed the Gender Pricing Ordinance, which prohibits businesses from charging different prices based solely on a consumer’s gender (though businesses are permitted to charge different prices if the goods or services involve more time, difficulty, or cost)  In the same year, it also passed an ordinance that prohibits dry cleaning businesses from charging different prices for similar services based on gender  This ordinance also requires those businesses to post all prices on a clear and conspicuous sign, according to county officials with whom we spoke  State and local officials we interviewed identified benefits and challenges associated with these laws  For example, California, New York City, and Miami-Dade County officials noted that these laws give them the ability to intervene to address pricing practices that may lead to discrimination based on gender  In addition, California state officials said that the state’s efforts to implement the Gender Tax Repeal Act helped to improve consumer awareness about gender price differences  However, officials from California and Miami-Dade County cited challenges associated with tracking relevant complaints  For example, Miami-Dade County’s online complaint form includes a narrative section but does not ask for the complainant’s gender  Consumers do not always identify their gender in the narrative or state that that was the reason for their treatment  Additionally, officials from California and Miami-Dade County stated that seeking out violations would be very resource-intensive, and they rely on residents to submit complaints about violations  We provided a draft of this report to BCFP, DOJ, FTC, and HUD  BCFP, FTC, and HUD provided technical comments on the report draft, which we incorporated where appropriate  As agreed with your offices, 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, BCFP, DOJ, FTC, HUD, and other interested parties  In addition, the report will be 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) 512-8678 or cackleya@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  We used a multivariate regression model to estimate the effect of gender (to which a product is targeted to) on the price of that product while controlling for other factors that may also affect the product’s price  The factors that we controlled for were the product size, promotional and packaging costs, and other product characteristics discussed in detail later  We used scanner data from the Nielsen Company (Nielsen) for calendar year 2016 and analyzed the following 10 product categories: (1) underarm deodorants, (2) body deodorants, (3) shaving cream, (4) shaving gel, (5) disposable razors, (6) nondisposable razors, (7) razor blades, (8) designer perfumes, (9) mass-market perfumes, and (10) mass-market body sprays  We estimated the following regression model for each of our 10 product categories: P=α+β*Male + λ* Size + θ*Owner +η*Promotion+ μ*X + δ*Y + ε The dependent variable P in the above equation represents price  For our analysis, we constructed two measures of price  The first is the item price, estimated as the total dollar sales of an item (each item is depicted by a unique Universal Product Code (UPC) in the Nielsen data), divided by the total units sold of that item  The second measure of price that we use is price per ounce or price per count  This is estimated as the item price divided by the total quantity of product, where quantity or size depicts the number of ounces (as in the case of fragrances) or the count of blades in razor blade packs  The total quantity of the product is the ounces or counts of one item multiplied by the number of items included in a specific product configuration  For example, a 2-pack of deodorant sticks where each deodorant stick is 2 7 ounces would be a total quantity of 5 4 ounces  The variable Male in the above equation is an indicator variable depicting whether the product is designated as a “men’s” product in the Nielsen data  It is represented as a value of “1” for men’s products and a value of “0” for women’s products  The co-efficient for this variable, parameter β, would therefore show the price difference between a men’s and women’s product  A negative value would imply a lower price for products designated as men’s products  The variable Size represents the most appropriate specification of the size of the product  Owner is a set of indicator variables representing all the brand owners selling a particular product  The brand of a product can be expected to have a substantial effect on prices for the kind of products we analyze because brands can be a proxy for quality for some consumers  However, we also found that firms often create gender-specific brands, so holding brands constant rendered most gender-based price comparisons infeasible  To overcome this, we hold owners instead of brands constant for our price comparison analysis  The variable Promotion represents the percentage of dollar sales that were sold on any type of promotion  This variable proxies for promotional costs to some extent based on the assumption that the greater the proportion of sales due to promotional activity, the greater the promotional costs  The variables X represent a set of indicator variables for packaging characteristics such as package delivery method (for example, roll-on or aerosol spray deodorants) or package shape (for example, bottle, tube, or can)  We expect these characteristics to proxy for different costs associated with different packaging methods  The variables Y represent a set of indicator variables representing different product characteristics (for example, forms such as gel stick or smooth solid and claims such as “active cooling” or “anti-wetness” for underarm deodorants, and blade types such as “triple edge” and “flexible six” for razors)


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: VA operates one of the largest health care systems in the United States, utilizing more than 6,000 federally owned and 1,500 leased buildings. DOD has repeatedly applied the BRAC process to reduce the amount of unneeded property that it owns and leases and to save billions of dollars that could be applied to higher priority defense needs. This statement is based on GAO's April 2017 report related to VA facility alignment ( GAO-17-349 ) and numerous GAO reports related to the BRAC process as summarized in a June 2011 testimony ( GAO-11-704T ) and a March 2012 testimony ( GAO-12-513T ). This statement addresses (1) the factors that affect VA's facility alignment and the extent to which VA's capital-planning process facilitates the alignment of facilities with the veterans' population, and (2) the key elements and challenges affecting DOD and the Commission in BRAC 2005. Detailed information on our scope and methodologies for this work can be found in these published products, cited throughout this testimony. Geographic shifts in the veterans' population, changes in health care delivery, aging infrastructure, and limited stakeholder involvement affect the Department of Veterans Affairs' (VA) efforts to align its services and real property portfolio to meet the needs of veterans. For example, a shift over time from inpatient to outpatient care will likely result in underutilized space once used for inpatient care. Further, the historic status of some VA facilities adds to the complexity of converting or disposing of them. In such instances, it is often difficult and costly for VA to modernize, renovate, and retrofit these older facilities. GAO reported that two of the planning processes VA uses to align its facilities—VA's Strategic Capital Investment Planning (SCIP) and the VA Integrated Planning (VAIP)—have limitations that undermine VA's efforts to achieve its goals. Specifically: VA relies on the SCIP process to plan and prioritize capital projects, but VA routinely asks its facility planners to submit their next year's planned project narratives before knowing if their previous submissions have been funded. The overlapping budget cycle, which is outside of VA's control, combined with other SCIP limitations—including subjective narratives, long time frames, and restricted access to information—make it difficult for VA to rely on SCIP to accurately identify the capital necessary to address its service and infrastructure gaps. VA concurred that it needs to address SCIP limitations that are within its control, as GAO recommended; VA has made some progress in implementing the recommendation has made some progress in implementing the recommendation.\ The VAIP process is estimated to cost $108 million and to produce market-level service delivery plans and facility master plans. However, the VAIP master plans incorrectly assume that all future growth in services will be provided directly through VA facilities without considering alternatives, such as purchasing care from the community. GAO recommended that VA consider discontinuing the VAIP facility master plans pending an assessment of their value as a facility-planning tool. VA agreed with the recommendation and is implementing it while pursuing a national realignment strategy.. Key elements of the Department of Defense's (DOD) 2005 Base Realignment and Closure (BRAC) process could benefit VA's asset and infrastructure review. The key elements included: (1) establishing goals for the process, (2) developing criteria for evaluating closures and realignments, and (3) establishing an organizational structure to develop closure and realignment options. GAO identified key challenges that affected DOD's implementation of BRAC 2005 and the results achieved; these challenges would need to be addressed if VA is to successfully apply the process. These challenges included: (1) large, complex recommendations required sustained senior leadership's attention and a high level of coordination among many stakeholders, and (2) the large number of actions that depend on each other for successful implementation. In the April 2017 report, GAO made recommendations related to capital planning and stakeholder involvement. VA concurred with the recommendations to the extent that they were within its control and has started making improvements.

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



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

Report 1: Geographic shifts in the veterans’ population, changes in health care delivery, an aging infrastructure, and limited stakeholder involvement affect VA’s efforts to align its services and real property portfolio to meet the needs of veterans  For example, there has been a shift over time from inpatient to outpatient care  This shift will likely result in underutilized space once used for inpatient care  In such instances, it is often difficult and costly for VA to modernize, renovate, and retrofit these older facilities  In June 2017, VA reported that its facility inventory includes 430 vacant or mostly vacant buildings that are, on average, more than 60 years old, and an additional 784 buildings that are underutilized  The historic status of some VA facilities adds to the complexity of converting or disposing of them  In 2014, VA reported holding 2,957 historic buildings, structures, or land parcels—the third most in the federal government after DOD and the Department of the Interior  In some instances, it may be more expensive to renovate than to demolish and rebuild outdated facilities  In other cases, however, there may not be an option to demolish if these buildings are designated as historic  For example, planning officials at four medical facilities in our review told us that state historic preservation efforts prevented the VA from demolishing vacant buildings, even though these buildings require upkeep costs and pose potential safety hazards  (See fig  1 ) VA has also encountered challenges to its facility alignment efforts, in part, because it has not consistently followed best practices for effectively engaging stakeholders  VA may align its facilities to meet veterans’ needs by expanding or consolidating facilities or services  Stakeholders— including veterans; local, state, and federal officials; Veterans Service Organizations; historic preservation groups; VA staff; and Congress— often view changes as working against their interests or those of their constituents, especially when services are eliminated or shifted from one location to another  We found that VA has not consistently engaged with stakeholders, and, in some cases, this inconsistency resulted in adversarial relationships that reduced VA’s ability to better align facilities with the needs of the veteran population  In our April 2017 report, we recommended that VA improve stakeholder communication guidance and evaluate its efforts  VA agreed with our recommendations and outlined a plan to implement them  Two of the planning processes VA uses to align its facilities—VA’s Strategic Capital Investment Planning (SCIP) and the VA Integrated Planning (VAIP)—have limitations  VA relies on the SCIP process to plan and prioritize capital projects system-wide, but SCIP’s limitations—including subjective narratives, long timeframes, and restricted access to information—undermine VA’s ability to achieve its goals  For example, the time between when planning officials at VA medical facilities begin developing the SCIP narratives and when they are notified that a project is funded has taken between 17 and 23 months over the past 6 fiscal-year’s SCIP submissions  (See fig  2 ) As such, VA routinely asks its facility planners to submit their next year’s planned project narratives before knowing if their project submissions from the previous year have been funded  An official from the office that oversees SCIP told us that the timing of the budgeting process, which is outside VA’s control, contributes to these delays  While these aspects are outside of VA’s control, VA has chosen to wait about 6 to 10 months to report the results of the SCIP scoring process to the medical facilities  This situation makes it difficult for local officials to understand the likelihood that their projects will receive funding  A VA official said that for future SCIP cycles, VA plans to release the scoring results for minor construction and non-recurring maintenance projects to local officials earlier in the process  At the time of our review, however, the official did not have a time frame for when VA would do this  Although VA acknowledges many of these limitations, it has taken little action in response  Federal standards for internal control state that agencies should evaluate and determine appropriate corrective action for identified limitations on a timely basis  If VA does not address known limitations with the SCIP process, it will not have reasonable assurance that SCIP can be used to accurately identify the capital necessary to address VA’s service and infrastructure gaps  In our April 2017 report, we recommended that VA address identified limitations to the SCIP process, including limitations to scoring and approval, and access to information  VA concurred with the recommendation to the extent the limitations were within its control  While VA has taken some actions, the recommendation remains open  The VAIP process produces a market-level health services delivery plan for each Veterans Integrated Service Network (VISN) and a facility master plan for each medical facility  VA has estimated the entire process to create plans for VISNs and facilities to cost $108 million when fully complete  However, the VAIP process’s facility master plans assume all future growth in services will be provided directly through VA facilities  This assumption is not accurate given that (1) VA obligated about $10 1 billion to purchase care from non-VA providers in fiscal year 2015 and (2) VA can provide care directly through its medical facilities or purchase health care services from non-VA providers through both the Non-VA Medical Care Program (referred to as “care in the community” by VA) and clinical contracts  The Office of Management and Budget’s acquisition guidance notes that investments in major capital assets should be made only if no alternative private sector source can support the function at a lower cost  In our April 2017 report, we recommended that VA assess the value of the VAIP’s facility master plans as a facility-planning tool, and based on conclusions from the review, to either (1) discontinue the development of VAIP’s facility master plans or (2) address the limitations of VAIP’s facility master plans  VA concurred with the recommendation, and in August 2017, VA noted that it has discontinued its VAIP facility master plans while VA pursues a national realignment strategy, after which it plans to adjust its future facility master plans to incorporate pertinent information, including care in the community realignment opportunities  As Congress evaluates proposed legislation for disposing of or realigning VA property, it may wish to consider seven elements DOD relied on as it developed its recommendations for the BRAC Commission  Establish goals for the process  The Secretary of Defense emphasized the importance of transforming the military to make it more efficient as part of the 2005 BRAC round  Other goals for the 2005 BRAC process included fostering jointness among the four military services, reducing excess infrastructure, and producing savings  Prior rounds focused more on reducing excess infrastructure and producing savings  Develop criteria for evaluating closures and realignments  DOD proposed selection criteria, which were made available for public comment via the Federal Register  Ultimately, Congress enacted the final BRAC selection criteria in law with minor modification and specified that four selection criteria, known as the “military value criteria,” were to be given priority in developing closure and realignment recommendations  Further, Congress required that the Secretary of Defense develop and submit to Congress a force structure plan that described the estimated size of major military units needed to address probable threats to national security for the 20- year period beginning in 2005, along with a comprehensive inventory of global military installations  In authorizing the 2005 BRAC round, Congress specified that the Secretary of Defense publish a list of recommendations for the closure and realignment of military installations inside the United States based on the statutorily-required 20-year force structure plan and infrastructure inventory, and on the final selection criteria  Estimate costs and savings to implement closure and realignment recommendations  To address the cost and savings criteria, DOD developed and used the Cost of Base Realignment Actions (COBRA) model, a quantitative tool that DOD has used since the 1988 BRAC round to provide consistency in potential cost, savings, and return-on-investment estimates for closure and realignment options  We found the COBRA model to be a generally reasonable estimator for comparing potential costs and savings among alternatives  (See fig  3 ) As with any model, the quality of the output from COBRA was a direct function of the data DOD included in the model  Also, DOD’s COBRA model relied to a large extent on standard factors and averages and did not represent budget quality estimates that were developed once BRAC decisions were made and detailed implementation plans were developed  Nonetheless, the financial information provided important input into the selection process as decision makers weighed the financial implications—along with military value criteria and other considerations—in arriving at final decisions about the suitability of various closure and realignment options  Establish an organizational structure  The Office of the Secretary of Defense emphasized the need for joint cross-service groups to analyze common business-oriented functions  For the 2005 BRAC round, as for the 1993 and 1995 rounds, these joint cross-service groups performed analyses and developed closure and realignment options in addition to those developed by the military departments  Our evaluation of DOD’s 1995 BRAC round found that few cross- service recommendations were made, in part because of the lack of high-level leadership to encourage consolidations across the departments’ functions  In the 1995 BRAC round, the joint cross- service groups submitted options through the military services for approval, but few were approved  The number of approved recommendations that the joint cross-service groups developed significantly increased in the 2005 BRAC round  This increase was, in part, because high-level leadership ensured that the options were approved not by the military departments but rather by a DOD senior- level group, known as the Infrastructure Steering Group  As shown in figure 4, the Infrastructure Steering Group was placed organizationally on par with the military departments  Establish a common analytical framework  To ensure that the selection criteria were consistently applied, the Office of the Secretary of Defense, the military departments, and the seven joint cross- service groups first performed a capacity analysis of facilities and functions  Before developing the candidate recommendations, DOD’s capacity analysis relied on data calls to hundreds of locations to obtain certified data to assess such factors as maximum potential capacity, current capacity, current usage, and excess capacity  Then, the military departments and joint cross-service groups performed a military value analysis for the facilities and functions based on primary military value criteria, which included a facility’s or function’s current and future mission capabilities, physical condition, ability to accommodate future needs, and cost of operations  Develop BRAC oversight mechanisms to improve accountability for implementation  In the 2005 BRAC round, the Office of the Secretary of Defense for the first time required the military departments to develop business plans to better inform the Office of the Secretary of Defense of the status of implementation and financial details for each of the BRAC 2005 recommendations  These business plans included: (1) information such as a listing of all actions needed to implement each recommendation; (2) schedules for personnel relocations between installations; and (3) updated cost and savings estimates by DOD based on current information  This approach permitted senior-level intervention if warranted to ensure completion of the BRAC recommendations by the statutory completion date  Involve the audit community to better ensure data accuracy  The DOD Inspector General and military department audit agencies played key roles in identifying data limitations, pointing out needed corrections, and improving the accuracy of the data used in the process  In their oversight roles, the audit organizations, which had access to relevant information and officials as the process evolved, helped to improve the accuracy of the data used in the BRAC process and thus strengthened the quality and integrity of the data used to develop closure and realignment recommendations  For example, the auditors worked to ensure certified information was used for BRAC analysis and reviewed other facets of the process, including the various internal control plans, the COBRA model, and other modeling and analytical tools that were used in the development of recommendations  We identified two key challenges that affected DOD’s implementation of BRAC 2005 and would need to be addressed for VA to adopt a BRAC- like process for its asset and infrastructure review  Some transformational-type BRAC recommendations required sustained senior leadership attention and a high level of coordination among many stakeholders to complete by the required date  Implementation of some transformational BRAC recommendations—especially those where a multitude of organizations had roles to play to ensure the achievement of the goals of the recommendation—illustrated the need to involve key stakeholders and effective planning  For example, the Defense Logistics Agency committed sustained high-level leadership and included relevant stakeholders to address implementation challenges faced with the potential for disruptions to depot operations during implementation of the BRAC consolidation recommendation  To implement the BRAC recommendations, the agency had to develop strategic agreements with the services that ensured that all stakeholders agreed on its plans for implementation, and had to address certain human capital and information technology challenges  Large number of actions and interdependent recommendations complicated the implementation process  The large number and variety of BRAC actions presented challenges during implementation  The BRAC 2005 round had more individual actions (813) than the four prior rounds combined (387)  The executive staff of the Commission told us that it was more difficult to assess the costs and the amount of time for the savings to offset the implementation costs since many of the recommendations contained multiple interdependent actions, all of which needed to be reviewed  Specifically, many of the BRAC 2005 recommendations were interdependent and had to be completed in a sequential fashion within the statutory implementation period  In cases where interdependent recommendations required multiple relocations of large numbers of personnel, delays in completing one BRAC recommendation had a cascading effect on the implementation of other recommendations  Specifically, DOD had to synchronize the relocations of over 123,000 people with about $24 7 billion in new construction or renovation  Commission officials told us that in prior BRAC rounds each base was handled by a single integrated recommendation  However, in BRAC 2005, many installations were simultaneously affected by multiple interconnected BRAC recommendations  Given the complexity of interdependent recommendations, the Office of the Secretary of Defense required the military departments and defense agencies to provide periodic updates on implementation challenges and progress  Chairman Roe, Ranking Member Walz, and Members of the Committee, this concludes our prepared statement  We are happy to answer any questions related to our work on VA’s efforts to align its medical facilities and services or on DOD’s BRAC process  If you or your staff members have any questions concerning this testimony, please contact David Wise at (202) 512-2834 or wised@gao gov regarding federal real property, or Brian Lepore at (202) 512-4523 or leporeb@gao gov regarding the BRAC process  Contact points for our Offices of Congressional Relations and Public Affairs may be found on the last page of this statement  Other individuals who made key contributions to this testimony include Keith Cunningham, Assistant Director; Gina Hoffman, Assistant Director; Tracy Barnes; Jeff Mayhew; Kevin Newak; Richard Powelson; Malika Rice; Jodie Sandel; Eric Schwab; Amelia M  Weathers; and Crystal Wesco  This is a work of the U S  government and is not subject to copyright protection in the United States  The published product may be reproduced and distributed in its entirety without further permission from GAO  However, because this work may contain copyrighted images or other material, permission from the copyright holder may be necessary if you wish to reproduce this material separately 


Report 2: To participate in federal student aid programs, postsecondary schools must be 1) certified by Education as eligible to participate in federal student aid programs, 2) accredited by a recognized accrediting agency— generally nongovernmental, nonprofit entities—and 3) authorized by the state in which the school is physically located  (See table 1 ) FSA is responsible for ensuring that schools with access to federal student aid are eligible and capable of properly administering federal student aid funds, according to standards established by Education and authorized by the Higher Education Act  These standards include requirements for schools related to communication, personnel, policies, procedures and reporting, and adequate checks and balances in a system of internal controls, among others  FSA is also responsible for conducting ongoing financial oversight of schools that receive federal student aid  This includes reviewing annual financial statement audits to assess a school’s financial responsibility and providing additional oversight to schools that do not meet financial responsibility standards outlined in the Higher Education Act  Schools that participate in federal student aid programs generally are required to submit annual compliance audits  The compliance audit provides information that FSA can use to assess the school’s administration of federal student aid programs and to identify schools that require additional oversight because they do not fully comply with federal student aid administrative requirements  The OIG is required to assess the quality of school compliance audits and selects a sample to review each year  The OIG reviews the audit documentation to ensure that it supports the auditor’s opinions and that the audit results are reliable  According to agency guidance, FSA staff should refer compliance audits to the OIG for a quality review if they have any concerns about the quality of the audits  Both FSA and OIG officials stated that the OIG has primary responsibility for issues related to audit quality  When a school first applies to be certified to administer federal student aid, FSA will either approve the school for provisional certification— generally for 1 year—or deny certification (see fig  1)  Once a school is approved for initial certification and applies for recertification, FSA will provisionally or fully recertify the school, or deny certification  According to FSA procedures, FSA uses provisional certification for initial, or first time, applicants, as well as schools that are applying for recertification  Provisional certification is the only approval status available to new schools  In addition, FSA may decide to recertify a school provisionally if it determines that a school has not fully complied with federal student aid requirements  FSA prohibits provisionally certified schools from opening new campus locations or offering new programs without approval from FSA, and provisionally certified schools that are denied recertification have a less substantive appeals process than fully certified schools  Further, recertified schools in provisional status are subject to more FSA oversight than schools that are fully certified  FSA procedures allow for some discretion in determining for how long to certify a school  Provisional recertification generally lasts 1 to 3 years, while full recertification generally lasts 4 to 6 years  Education’s FSA regional staff draw information from a variety of sources during the certification process to assess a school’s capability to administer federal student aid  According to FSA documents, regional staff are to review information collected from schools and third parties, such as annual compliance audits conducted by independent auditors, among other information sources  FSA staff responsible for different functional areas, such as financial and compliance audits, accreditation status, and student loan default rates, compile and review information on schools, according to FSA procedures  FSA officials told us that these staff meet to discuss any potential program eligibility issues and to ensure that all information relevant to a school is considered before making a certification decision  FSA’s certification procedures outline some of the key information that regional staff should assess, some of which is relevant to both initial and recertification decisions, and some of which is specific to each type of certification process (see fig  2)  Documents and policies provided by schools: FSA regional staff are directed to review documents submitted by schools, including school catalogs, and certain school policies—such as admissions and student refund policies—that are relevant to assessing administrative capability  Proof of accreditation: School accreditors are responsible for applying and enforcing standards to help ensure that the education offered by schools is of sufficient quality to achieve program objectives  Accreditation of schools, which generally includes a site visit, takes place on a cycle that may range from every few years to as many as 10 years  Proof of state authorization: States are responsible for authorizing schools to offer postsecondary education and respond to student complaints  The process for approving schools varies from state to state and may include on-site visits  Audited financial statements: FSA regional staff are directed to review information in audited financial statements to assess schools’ financial health  Schools are required to have annual audited financial statements issued by an independent certified public accountant or a government auditor  Self-reported school data: FSA regional staff are instructed to review data on continual student enrollment in eligible academic programs and student withdrawal rates  Pre-certification review and school outreach: FSA staff are responsible for contacting school personnel to verify the school’s application information and discuss relevant policies, procedures, and other materials relevant to administering federal student aid  FSA visits to newly certified schools: After schools first apply and are provisionally certified, Education requires FSA regional staff to contact them within 3 months and schedule an on-site school visit  Schools cannot administer federal student aid until they are certified, so FSA has limited information on how newly certified schools are administering federal student aid programs  School visits provide FSA with an opportunity to collect additional information about a provisionally certified school’s ability to administer federal student aid  Some FSA regional staff we interviewed told us that on-site visits to newly certified schools provide valuable first-hand information about whether these schools are administering federal student aid in accordance with program requirements  If FSA regional staff find that a school is having difficulties administering federal student aid, FSA procedures direct regional staff to assist schools by providing clarification and guidance on federal student aid policies, recommending additional training for school officials, and helping schools develop a plan to track and report on their corrective actions, among other things  Compliance audits: FSA staff are directed to review information in compliance audits to determine if schools are complying with specific federal student aid requirements  Generally, compliance audits are required to be conducted annually by an independent auditor, and submitted with the school’s audited financial statements  Program reviews: FSA regional staff are also responsible for conducting program reviews, usually on site, which evaluate school compliance with federal requirements and can provide more in-depth information on schools than compliance audits, according to some FSA staff we interviewed  Generally, FSA selects schools for program reviews that it considers to be at risk for noncompliance, according to Education documents  FSA conducts approximately 250 to 300 program reviews per year, according to FSA documentation  FSA staff from all four of our selected regional offices told us they consider results from any recent program review in decisions about recertification and noted that such information, when available, is valuable for assessing schools’ administrative capability  Education data: FSA regional office staff are also directed to review data on student loan default rates  From calendar years 2006 through 2017, FSA approved most schools applying for certification to receive federal student aid, according to Education data  From 2006 through 2017, FSA approved 89 percent of schools new to administering federal student aid for provisional certification and denied 11 percent of schools overall (see fig  3)  Denial rates for initial certification were 11 percent for public and for-profit schools and 14 percent for nonprofit schools  For more information on 2006-2017 school certification outcomes by year, see appendix I  FSA regional staff responsible for reviewing school applications told us that schools are denied initial certification for issues such as a lack of accreditation, not offering eligible programs for federal student aid, or not meeting other statutory eligibility requirements  For example, FSA staff said that for-profit and vocational schools that apply for initial certification are required to provide an eligible program continuously for 2 years prior to their initial application  FSA staff may also advise schools that do not meet basic eligibility requirements not to apply, which could result in fewer initial certification denials overall  In addition, FSA staff said they often work with schools to address compliance problems, for example, by providing guidance on revising school policies that do not meet requirements, so that the schools are able to meet FSA’s certification requirements  From 2006 through 2017, 76 percent of schools applying for recertification were fully recertified, 21 percent were provisionally recertified, and 3 percent were denied recertification  Sixty-six percent of for-profit schools were fully recertified, 28 percent were provisionally recertified, and 6 percent were denied  In comparison, 86 percent of public schools were fully recertified, 14 percent were provisionally recertified, and fewer than 1 percent were denied  Nonprofit schools had rates similar to public schools, with 80 percent fully recertified, 18 percent provisionally recertified, and 2 percent denied (see fig 4)  FSA staff from all four of our selected regional offices told us that they typically deny recertification when a school no longer meets eligibility requirements, such as losing accreditation, or when there is significant evidence of serious issues or massive wrongdoing, such as fraud  For example, managers in one regional office told us they denied recertification for a school because they had evidence that the school was accepting students without valid high school diplomas and referring them to diploma mills to boost enrollment  Staff in two FSA regional offices told us that they can also choose to fully recertify a school for shorter periods of time if they uncover issues related to administrative capability  For example, one regional staff member told us that when they found a school’s default rate for one federal student loan program had been high for the prior 3 years, the regional office decided to shorten the school’s full recertification period from 6 to 4 years, to allow FSA staff to review the school again sooner  FSA staff from all four of our selected regional offices told us that they provisionally certify schools for a variety of reasons, including when a school submits a late compliance audit or when a recent compliance audit indicates that a school could potentially have significant problems  Generally, schools in provisional certification status are subject to additional monitoring by FSA compared to schools that have been fully certified  For example, Education officials said that if they have concerns about a provisionally certified school’s student withdrawal rate, they can add provisional conditions requiring the school to submit monthly enrollment rosters for review  Staff in two FSA regional offices told us that in other cases, if they have concerns about how a school is administering federal student aid or suspected fraud, they can put a school on provisional status and conduct a program review to collect more detailed information on compliance with federal requirements  Education data also show that most schools remain in provisional status the first time they are recertified—62 percent from 2006 to 2017  In contrast, FSA staff fully recertified over three-quarters of schools that applied for recertification a second time during the same time period (see table 2)  For more information on first and second recertification outcomes by school sector, see appendix II  We found that FSA generally relies on compliance audits as the only annual on-site review to determine how schools applying for recertification administer federal student aid  The audits provide direct information collected by independent auditors from school visits and file reviews examining how schools administer federal student aid and comply with program requirements  For example, OIG audit guidance directs auditors to check whether schools are distributing federal student aid to eligible students and accurately calculating student loan amounts  FSA officials and staff from all four of our selected regional offices said that compliance audits are a key source of information they use to assess a school’s administrative capability  Officials from Education’s OIG said that the quality of information in compliance audits varies substantially and depends on the auditor  The OIG has found quality problems in some of the compliance audits it selects—based on auditor and school risk factors—for its annual quality control reviews  Because the OIG selects higher risk audits to review, its reviews are more likely to detect problems, and OIG officials said they cannot make any conclusions about the overall prevalence of quality problems in compliance audits  However, our analysis of OIG quality review data found that of the 739 compliance audits reviewed by the OIG from fiscal years 2006 through 2017, the OIG passed 23 percent (173) and failed 59 percent (436)  An additional 18 percent (130) passed with deficiencies  For example, across the 41 compliance audits it reviewed in fiscal year 2016, the OIG identified 264 quality deficiencies with the auditor’s work, according to our analysis of quality reviews provided by the OIG  The most frequently cited issues in these 41 audits were: reporting (24 audits), such as lack of evidence that the auditor tested whether the school correctly reported student enrollment status; student eligibility (20 audits), such as lack of evidence that the auditor verified student school attendance; and administrative capability (19 audits), such as lack of evidence that the auditor determined whether the accreditor had been notified about a change in school ownership within 10 days  FSA officials also identified quality issues with the compliance audits of some schools  FSA headquarters officials and staff we interviewed in several regional offices said they have seen schools with significant program review findings that had not been identified in annual compliance audits  FSA staff said they have referred some compliance audits to the OIG for quality reviews when they have had questions about the thoroughness of an audit  We also found a couple of examples in our review of school certification documents in which the findings identified in a school’s compliance audit were different from the findings identified by FSA in a program review of the same school covering the same time period  In one case, FSA staff said they probably would have fully recertified the school if they had relied solely on the compliance audit  Instead, they used the program review to determine that the school should be provisionally recertified  Compliance audits and program review findings are based on a sample of student records, and FSA staff said some differences in findings might be explained by differences in the records reviewed  FSA and OIG officials cited several issues that can affect the quality of compliance audits  FSA and OIG officials we interviewed said that some auditors conducting compliance audits have insufficient training in federal student aid, which contributes to audit quality problems  OIG staff also said that even if an auditor meets the general training hour requirements for auditors, the training content may not be relevant for federal student aid audits  In addition, FSA and OIG officials said some schools— particularly smaller schools—tend to hire less experienced auditors in order to save money, often resulting in poor quality audits  FSA officials in most selected regional offices said that additional training on federal student aid for auditors who are new to or unfamiliar with federal student aid could help improve audit quality  FSA and the OIG recently have taken steps to address audit quality and the information available to FSA staff when making certification decisions  These efforts include: Training for auditors: The OIG has taken steps to enhance training offered to auditors of schools’ administration of federal student aid and is exploring opportunities to provide additional training  In December 2017, the OIG and the American Institute of Certified Public Accountants cosponsored training for auditors on the OIG’s 2016 revised guide for audits of for-profit schools, and other topics related to auditing federal student aid  The training included discussion of common audit quality issues and areas of highest risk  According to an OIG official, about 200 auditors attended, and after the event, the American Institute of Certified Public Accountants and the OIG posted a recording of the training to their websites to make it available to additional auditors  In addition, OIG officials said they maintain an email account—listed on the OIG website—through which auditors can ask questions and receive responses  In March 2018, the OIG posted frequently asked questions and answers to the website  Timeliness of OIG quality reviews: Both FSA and OIG officials said that the OIG has recently renewed efforts to issue compliance audit quality reviews more quickly, after several years in which staffing shortages and other issues led to some delayed quality reviews  Guidance to schools on selecting an auditor: OIG officials said that at the 2017 FSA training conference for school financial aid staff, they presented to more than 400 participants about factors schools should consider when hiring an auditor  For example, they suggested that schools verify the licenses of certified public accountants, ask about the types of engagements an auditing firm has conducted, request and check references, check for any actions that may have been taken against a firm, and ask whether the auditor has been subject to a previous review by the OIG or another agency  FSA officials said they expected to invite the OIG to present at future FSA conferences, and OIG officials said they were seeking additional opportunities to share information on auditor selection with schools, including a planned presentation to an association of postsecondary schools  FSA working group: FSA recently established a working group to update its guidance to FSA staff on how to coordinate with the OIG to address compliance audits with quality problems  Among other topics, the working group has consulted with the OIG about how schools are made aware of the OIG’s findings regarding the quality of their audits  FSA officials said that OIG officials have provided input and feedback on FSA’s proposed changes to the guidance  Audit guide revisions: In addition, OIG and FSA staff told us they expected the OIG’s 2016 revisions to the for-profit school audit guide to improve the quality of compliance audits for those schools  They said that because the revised guide clarified some issues that were confusing to auditors in the previous guide issued in 2000, auditors might be better able to implement the guidance  The audit guide revisions include more testing and reporting requirements, clarified procedures, and guidance on issues such as fraud reporting and coordinating financial and compliance audits  The 2016 revisions first applied to audits for fiscal years beginning after June 30, 2016, and FSA began receiving those audits at the end of 2017  In addition, although the OIG’s 2016 revisions only apply to audits of for-profit schools, FSA officials said they planned to establish a working group to consider improvements to audit guidance for public and nonprofit schools  FSA and OIG efforts to address audit quality could help ensure that compliance audits provide accurate and reliable information on school administrative capability for Education’s recertification decisions  We provided a draft of this report to Education for review and comment  Education’s Office of Inspector General provided technical comments, which we considered and incorporated as appropriate  Education did not provide other comments on the report  We are sending copies of this report to the appropriate congressional committees; the Secretary of Education; and other interested parties  In addition, the 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 (617) 788-0534 or emreyarrasm@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 III  In addition to the contact named above, Michelle St  Pierre (Assistant Director), Kristy Kennedy (Analyst-in-Charge), Edward Bodine, Marissa Jones, and Mark Ward made significant contributions  Also contributing to this report were Susan Aschoff, Deborah Bland, Nagla’a El-Hodiri, Monika Gomez, Sheila R  McCoy, Jessica Orr, Mimi Nguyen, John Mingus, Rhiannon Patterson, Monica Savoy, Benjamin Sinoff, and Rosemary Torres Lerma 

ground_truth: Education provided over $122 billion in grants, loans and work study funds to help students pay for college at about 6,000 schools in fiscal year 2017. Education is responsible for certifying that these schools are eligible for and capable of properly administering federal student aid funds. Schools are required to submit an annual compliance audit that provides information on schools' administrative capability, which Education considers in its school certification decisions. GAO was asked to review Education's process for certifying schools to receive federal student aid. This report examines (1) how Education certifies schools to administer federal student aid and how frequently schools are approved and denied certification; and (2) the role of compliance audits in the certification process and what, if any, steps Education has taken to address the quality of the audit information. GAO analyzed data on school certification outcomes for calendar years 2006-2017 (when GAO determined data were most reliable); reviewed data and reports summarizing Education's reviews of compliance audit quality for fiscal years 2006-2017; reviewed a non-generalizable sample of 21 school certification decisions from fiscal years 2015 and 2016, selected for a mix of decisions, school characteristics, and geographic regions; examined relevant federal laws, regulations, policy manuals and guidance; and interviewed Education officials. The Department of Education (Education) is responsible for evaluating a variety of information to determine whether a postsecondary school should be certified to administer federal student aid programs, and agency data show that it approves most schools that apply. Education procedures instruct regional office staff to review school policies, financial statements, and compliance audits prepared by independent auditors, among other things. Education can certify schools to participate in federal student aid programs for up to 6 years, or it can provisionally certify them for less time if it determines that increased oversight is needed—for example, when a school applies for certification for the first time or when it has met some but not all requirements to be fully certified. In calendar years 2006 through 2017, Education fully or provisionally approved most schools applying for initial or recertification to receive federal student aid (see figure). Note: Schools applying for certification for the first time and approved are placed in provisional certification. In deciding whether to certify schools, Education particularly relies on compliance audits for direct information about how well schools are administering federal student aid, and Education's offices of Federal Student Aid and Inspector General have taken steps to address audit quality. The Inspector General annually selects a sample of compliance audits for quality reviews based on risk factors, such as auditors previously cited for errors. In fiscal years 2006 through 2017, 59 percent of the 739 selected audits received failing scores. Audits that fail must be corrected; if not, the school generally must repay federal student aid covered by the audit. Because higher risk audits are selected for review, Inspector General officials said they cannot assess the overall prevalence of quality problems in compliance audits. These two Education offices have taken steps to improve audit quality. For example, the Inspector General offered additional training to auditors on its revised 2016 audit guide and provided guidance to schools on hiring an auditor, while Federal Student Aid created a working group to strengthen its procedures for addressing poor quality compliance audits. Education's efforts to address audit quality could help ensure that these audits provide reliable information for school certification decisions. GAO is not making recommendations in this report.