CONSUMER 

PROTECTION 

Gender-Related 

Price Differences for 

Goods and Services 

Report to Congressional Requesters

August 2018

GAO-18-500

United States Government Accountability Office
United States Government Accountability Office

Highlights of GAO-18-500, a report to 

congressional requesters

August 2018

CONSUMER PROTECTION

Gender-Related Price Differences for Goods and 

Services

What GAO Found

Firms differentiate many consumer products to appeal separately to men and 

women by slightly altering product attributes like color or scent. Products 

differentiated by gender may sell for different prices if men and women have 

different demands or willingness to pay for these product attributes. Of 10 

personal care product categories (e.g., deodorants and shaving products) that 

GAO analyzed, average retail prices paid were significantly higher for women’s 

products than for men’s in 5 categories. In 2 categories—shaving gel and 

nondisposable razors—men’s versions sold at a significantly higher price. One 

category—razor blades--had mixed results based on two price measures 

analyzed, and two others—disposable razors and mass-market perfumes—

showed no significant gender price differences. GAO found that the target 

gender for a product is a significant factor contributing to price differences 

identified, but GAO did 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, such as different advertising costs. Though the 

analysis controlled for several observable product attributes, such as product 

size and packaging type, all underlying differences in costs and demand for 

products targeted to different genders could not be fully observed.

Studies GAO reviewed found limited evidence of gender price differences for 

four products or services not differentiated by gender—mortgages, small 

business credit, auto purchases, and auto repairs. For example, with regard to 

mortgages, women as a group paid higher average mortgage rates than men, in 

part due to weaker credit characteristics, such as lower average income. 

However, after controlling for borrower credit characteristics and other factors, 

three studies did not find statistically significant differences in borrowing costs

between men and women, while one found women paid higher rates for certain 

subprime loans. In addition, one study found that female borrowers defaulted 

less frequently than male borrowers with similar credit characteristics, and the 

study suggested that women may pay higher mortgage rates than men relative

to their default risk. While these studies controlled for factors other than gender 

that could affect borrowing costs, several lacked important data on certain 

borrower risk characteristics, such as credit scores, which could affect analysis 

of gender disparities. Also, several studies analyzed small samples of subprime 

loans that were originated in 2005 or earlier, which limits the generalizability of 

the results.

In their oversight of federal antidiscrimination statutes, the Bureau of Consumer 

Financial Protection, Federal Trade Commission, and Department of Housing 

and Urban Development have identified limited consumer concerns based on 

gender-related pricing differences. GAO’s analysis of complaint data received by 

the three agencies from 2012–2017 found that they had received limited 

consumer complaints about gender-related price differences. The agencies 

provide general consumer education resources on discrimination and consumer 

awareness. However, given the limited consumer concern, they have not 

identified a need to incorporate additional materials specific to gender-related 

price differences into their existing consumer education resources.

View GAO-18-500. For more information, 

contact Alicia Puente Cackley at (202) 512-

8678 or cackleya@gao.gov.

Why GAO Did This Study

Gender-related price differences occur 

when consumers are charged different 

prices for the same or similar goods 

and services because of factors related 

to gender. While variation in costs and 

consumer demand may give rise to 

such price differences, some 

policymakers have raised concerns 

that gender bias may also be a factor. 

While the Equal Credit Opportunity Act

and Fair Housing Act prohibit

discrimination based on sex in credit

and housing transactions, no federal 

law prohibits businesses from charging 

consumers different prices for the 

same or similar goods targeted to 

different genders. 

GAO was asked to review genderrelated price differences for consumer 

goods and services sold in the United 

States. This report examines, among 

other things, (1) how prices compared 

for selected goods and services 

marketed to men and women, and 

potential reasons for any price 

differences; (2) what is known about 

price differences for men and women

for products not differentiated by 

gender, such as mortgages; and (3) 

the extent to which federal agencies

have identified and addressed any 

concerns about gender-related price 

differences.

To examine these issues, GAO 

analyzed retail price data, reviewed

relevant academic studies, analyzed 

federal consumer complaint data, and 

interviewed federal agency officials, 

industry experts, and academics.
Page i GAO-18-500 Gender-Related Pricing

Letter 1

Background 4

Prices Differed Significantly for Selected Men’s and Women’s 

Personal Care Products, but We Could Not Attribute the 

Differences to Bias as Opposed to Other Factors 9

Studies We Reviewed Found Limited Evidence of Price 

Differences for Men and Women for Mortgages, Small Business 

Credit, and Auto Purchases 14

Federal Agencies Have Identified Limited or No Consumer 

Concerns about Price Differences Based on Sex or Gender 22

Some State and Local Governments Have Passed Laws to 

Address Concerns about Gender-related Price Differences 27

Agency Comments 29

Appendix I Nielsen Retail Price Data Analysis Methodology 30

Appendix II Collection of Online Prices for Selected Personal Care Products

36

Appendix III Objectives, Scope, and Methodology 39

Appendix IV Descriptive Statistics of Nielsen Retail Price Data 43

Appendix V Selected Federal Agency Consumer Complaint Processes 46

Appendix VI GAO Contact and Staff Acknowledgments 48

Appendix VII Bibliography 49

Contents
Page ii GAO-18-500 Gender-Related Pricing

Tables

Table 1: Comparison of Average Prices Paid for Men’s and 

Women’s Personal Care Products After Controlling for 

Observable Product Characteristics 11

Table 2: Summary of Selected Studies on the Effect of Gender or 

Sex on Mortgage Borrowing Costs 15

Table 3: Consumer Complaints about Price Differences Related to 

Gender or Sex by Agency, 2012–2017 25

Table 4: Examples of State and Local Gender-Related Pricing 

Laws in the United States 28

Table 5: Regression Results for Analysis of Selected Personal 

Care Products, 2016 33

Table 6: Average Online Prices Collected for Selected Personal 

Care Products on Four Retailer Websites, 2018 37

Table 7: Comparison of Average Retail Prices Paid in 2016 for 

Men’s and Women’s Products for 10 Selected Personal 

Care Product Categories 44

Table 8: Total Sales and Units Sold for Selected Personal Care 

Products, 2016 45

Table 9: Summary of Agencies’ Efforts to Collect Consumer 

Complaints 46

Figure

Figure 1: Illustrative Example of Similar Products Differentiated to 

Appeal to Men and Women 5
Page iii GAO-18-500 Gender-Related Pricing

Abbreviations

APR annual percentage rate

BCFP Bureau of Consumer Financial Protection

DOJ Department of Justice

ECOA Equal Credit Opportunity Act 

FDIC Federal Deposit Insurance Corporation

Federal Reserve Board of Governors of the Federal Reserve System

FHA Fair Housing Act

FTC Federal Trade Commission

HMDA Home Mortgage Disclosure Act of 1975

HUD Department of Housing and Urban Development

Nielsen Nielsen Company

SCF Survey of Consumer Finances

SSBF Survey of Small Business Finances

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.
Page 1 GAO-18-500 Gender-Related Pricing

441 G St. N.W.

Washington, DC 20548

August 9, 2018

The Honorable Robert P. Casey, Jr.

United States Senate

The Honorable Carolyn B. Maloney

Ranking Member

Joint Economic Committee

House of Representatives

Gender-related price differences can occur when manufacturers 

differentiate their products to appeal separately to female and male 

consumers, such as with clothing, personal care products, toys, and other 

consumer goods. In addition, businesses may offer different prices to 

female and male consumers for services, such as dry cleaning or 

haircuts, to account for different costs, different consumer preferences, or 

other factors associated with providing services to different genders. 

While manufacturing, marketing, or other cost differences may contribute 

to gender-related price differences, some policymakers have raised 

concerns that gender bias may play a role.1 Moreover, if female and male 

consumers pay different prices for similar products that they purchase 

frequently, such as personal care products, this could result in substantial 

differences in expenditures by gender over time. A consumer’s annual 

spending on a product category can be significant, even when prices for 

products within that category are low.2

You asked us to review gender-related price differences for goods and 

services sold in the U.S. marketplace. 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 

 1

For example, one U.S. state and some local governments have enacted laws intended to 

address perceived gender bias in the offering of services such as haircuts or dry cleaning, 

as discussed later in this report. One of these local governments examined differences in 

advertised prices for men’s and women’s products. See New York City Department of 

Consumer Affairs, From Cradle to Cane: The Cost of Being a Female Consumer, A Study 

of Gender Pricing in New York City (New York City, N.Y.: December 2015).

2

For example, U.S. household spending on personal care products and services averaged 

more than $700 annually in 2016. See Bureau of Labor Statistics, Consumer Expenditure 

Survey (Washington, D.C.: 2017). Personal care products and services include products 

for the hair, oral hygiene products, shaving needs, cosmetics and bath products, electric 

personal care appliances, and other products and services. 

Letter
Page 2 GAO-18-500 Gender-Related Pricing

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 genderrelated price differences; and (4) state and local government efforts to 

address concerns about gender-related price differences.3

To address our first objective, we purchased and analyzed Nielsen 

Company (Nielsen) data on retail prices paid for 10 personal care product 

categories for calendar year 2016.4 The product categories included 

underarm deodorants, body deodorants, disposable razors, 

nondisposable razors, razor blades, shaving creams, shaving gels, and 

three categories of fragrances. We selected these categories 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 10 

categories of personal care products differentiated for men and women. 

We assessed the reliability of the Nielsen data by reviewing relevant 

documentation and conducting 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. We selected comparable pairs of similar 

 3

Our first objective examines price differences for products that are commonly 

differentiated to appeal either to men or women. It examines the products themselves and 

not the gender of the consumer actually purchasing them.

4

In addition to the retail price paid, the data we purchased from Nielsen include data fields 

for product brand, package size, package design, product scent, and product color, 

among other product characteristics. Nielsen collected these retail price data from over 

200 national retailers, including grocery stores, pharmacies, mass merchandizers, and 

club stores. According to Nielsen, together these retailers encompass 82 percent of all 

U.S. sales for 2016. The prices were collected from participating retailers when an item 

was scanned at the cash register during check out.
Page 3 GAO-18-500 Gender-Related Pricing

men’s and women’s products that were differentiated by product 

attributes, such as scent or color, and were commonly sold across 

retailers. We included product pairs from similar categories used in our 

analysis of the Nielsen data. For more details on our online price data 

collection and the limitations associated with interpreting the results, see 

appendix II.

To address our second objective, we identified and reviewed studies that 

examined differences between the prices men and women paid for, or 

their access to, consumer services and goods that are not differentiated 

by gender—specifically interest rates, pricing, or access for mortgages or 

other loan types while controlling for the effects of factors other than 

gender or sex. We also identified and reviewed three studies on 

differences in price for autos and auto repair services. We found the 

studies we reviewed to be reliable for purposes of determining what is 

known about price differences for the same products, though several 

studies analyzed nonrepresentative data samples, such as subprime 

mortgage loans, and thus the results are not generalizable.

For our third objective, we reviewed relevant federal antidiscrimination 

statutes and agency documentation related to oversight and enforcement 

of these statutes. To learn about the potential extent of consumer 

concerns about price differences that could be based solely on gender or 

sex, we analyzed federal data on consumer complaints and 

investigations. We selected a random sample of gender-related consumer 

complaints from three databases managed by the Bureau of Consumer 

Financial Protection (BCFP), the Federal Trade Commission (FTC), and 

the Department of Housing and Urban Development (HUD), respectively, 

and counted the number of complaints alleging a perceived genderrelated price difference.5 We assessed the reliability of these data by 

reviewing documentation and interviewing agency officials about these 

databases. We determined that these data were sufficiently reliable for 

our purposes of identifying complaints of gender-related price differences. 

In addition, we interviewed agency officials from BCFP, FTC, HUD, and 

the Department of Justice (DOJ), as well as industry experts and 

academics, on gender-related price differences.

 5

In the years following its creation, BCFP has referred to itself and been commonly known 

as the Consumer Financial Protection Bureau, or CFPB. According to BCFP officials, 

BCFP is in the process of discontinuing use of that name and acronym.
Page 4 GAO-18-500 Gender-Related Pricing

To address our fourth objective, we identified and reviewed three 

examples of state and local laws that specifically address gender-related 

price differences. We also interviewed officials from the state and

localities that had enacted these laws. See appendix III for more 

information on our scope and methodology.

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.

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.

Background
Page 5 GAO-18-500 Gender-Related Pricing

Figure 1: Illustrative Example of Similar Products Differentiated to Appeal to Men 

and Women

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.6 To maximize profits, firms use a variety of 

techniques to charge prices close to the highest price different consumers 

are willing to pay.7 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 

 6

The practice of charging consumers different prices, when the differences in price are not 

completely attributable to differences in cost, is often referred to as “price discrimination”

by economists. For the purposes of our report we refer to this practice as price 

differentiation, so as not to confuse this term with statutorily prohibited discrimination 

based on sex.

7

In theory, firms would like to charge each customer the maximum amount that customer 

is willing and able to pay. This is known in economics as first degree price discrimination, 

and it occurs when a different price is charged to each consumer. 
Page 6 GAO-18-500 Gender-Related Pricing

and what they are willing to pay.8 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.9 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.10

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 

one gender,

• 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

 8

Economists refer to this as second degree price discrimination.

9

Economists refer to this as third degree, or group-based, price discrimination. 

10One condition for a firm to be able to implement a strategy of price differentiation is that 

it must be able to segment consumers such that one segment of consumers is not able to 

buy a product at a lower price and resell at a higher price, or is not able to purchase the 

product that is meant for the other group. This is referred to as “arbitrage” in economic 

literature.
Page 7 GAO-18-500 Gender-Related Pricing

• 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.11 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.12

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.13 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 

 11The Robinson-Patman Act, Pub. L. No. 74-692, 49 Stat. 1526 (1936), prohibits price 

discrimination by producers in the sale of goods to distributors when the effect of such 

pricing is to reduce competition and also prohibits certain discriminatory allowances or 

services furnished or paid to customers. Because this report focuses on gender-based 

price differences, the actions prohibited by the act are outside our scope. Section 5 of the 

Federal Trade Commission Act prohibits unfair methods of competition and unfair or 

deceptive acts or practices in or affecting commerce. See 15 U.S.C § 45(a)(1). According 

to an FTC official, a company may violate this section if it mispresents its practices or if 

those practices cause or are likely to cause substantial consumer injury that is not 

reasonably avoidable by consumers or outweighed by benefits to consumers or 

competition.

12In addition to prohibiting discrimination based on sex, ECOA prohibits discrimination in 

the credit market on the basis of race, color, religion, national origin, marital status, or age. 

See 15 U.S.C. § 1691(a)(1). The statute and its implementing regulation, Regulation B, 

also prohibit discrimination against an applicant because that applicant receives income 

from a public assistance program, or because an applicant has in good faith exercised any 

right under the Consumer Credit Protection Act or any state law upon which an exemption 

has been granted by BCFP. See 15 U.S.C. § 1691(a)( 2)-(3) and 12 C.F.R. § 1002.2(z). In 

addition to prohibiting discrimination based on sex, FHA prohibits discrimination in the 

housing market based on race, color, religion, handicap, familial status, or national origin. 

See 42 U.S.C. § 3604.

1315 U.S.C. § 1691(a)(1); 42 U.S.C. § 3605(a). Lenders in the housing market are also 

prohibited from varying the terms of credit offered, including the amount, interest rate, 

duration, or type of loan based on sex or certain other characteristics. See 24 C.F.R. § 

100.130.
Page 8 GAO-18-500 Gender-Related Pricing

risk to the lender that the borrower could default on the loan.14 In addition 

to the interest rate, borrowing costs for consumers can also include fees 

and other costs charged by lenders or brokers.15 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.16 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.

 14A credit score predicts how likely a borrower is to pay back a loan on time. Credit 

reporting agencies use a mathematical scoring model with information from a person’s 

credit report to create a credit score. Factors that make up a typical credit score include 

bill-paying history, current outstanding debt, and the number and type of loan accounts a 

person has. Lenders use credit scores to make decisions such as whether to offer you a 

mortgage, credit card, auto loan, or other credit product, and on what terms. 

15The Truth in Lending Act and its implementing Regulation Z require that, for certain 

credit transactions, borrowers receive written disclosures about important terms relating to 

the costs of credit, which includes an annual percentage rate (APR) that reflects, among 

other things, the interest rate charged. See 12 C.F.R. pt. 1026.

16The other agencies responsible for enforcing ECOA include the Board of Governors of 

Federal Reserve System (Federal Reserve), the Federal Deposit Insurance Corporation 

(FDIC), the Office of the Comptroller of the Currency, the National Credit Union 

Administration, the Farm Credit Administration, the Department of Transportation, the 

Securities and Exchange Commission, the Small Business Administration, and the Grain 

Inspection, Packers, and Stockyards Administration of the Department of Agriculture. See

15 U.S.C. § 1691c. 
Page 9 GAO-18-500 Gender-Related Pricing

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.17 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).18 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 

 17Throughout this report section, we refer to products targeted to men and women, but we 

could not observe the gender of the consumers who actually bought the products in the 

dataset. Instead, we could only observe the target gender for the product based on 

Nielsen’s categorization of the product as a men’s or women’s product. In other words, our 

analysis compares the prices at which products targeted to men or women were sold. We 

could not determine the extent to which men or women bought products designed for a 

different gender (e.g., a woman choosing to buy a deodorant targeted to men) or buy on 

behalf of another gender (e.g., buying for a spouse of the opposite gender).

18We used regression models to analyze data on retail prices paid for 10 categories of 

personal care products differentiated for women and men and determined the significance 

of price differences at 5 percent or lower level. For more information on our regression 

analysis, see appendix I. 

Prices Differed 

Significantly for 

Selected Men’s and 

Women’s Personal 

Care Products, but 

We Could Not 

Attribute the 

Differences to Bias as 

Opposed to Other 

Factors

For 5 of 10 Product 

Categories Analyzed, 

Women’s Products Sold at 

Higher Average Prices 

Than Men’s after 

Controlling for Some 

Observable Factors
Page 10 GAO-18-500 Gender-Related Pricing

count of product.19 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.

 19The average item price is the total dollar sales for a product category divided by the total 

number of items sold for that category. This measure of price automatically incorporates 

all prices, including sale prices and other discounts, and is mathematically equivalent to 

the weighted average of all individual item prices, where the weights are each item’s 

proportional share of the total volume sales. The average price per ounce or count is the 

item price divided by the quantity of product and weighted by the proportional share of 

items sold, where quantity or size depicts the number of ounces as in the case of 

fragrances or the count of blades in razor blade packs. Sizes for women’s products were 

generally smaller than for similar men’s products. For information about how these two 

price measures compared for women’s and men’s products—without controlling for other 

observable factors that may also affect price—see appendix IV. 
Page 11 GAO-18-500 Gender-Related Pricing

Table 1: Comparison of Average Prices Paid for Men’s and Women’s Personal Care Products After Controlling for Observable 

Product Characteristics

Product 

Higher average item price

for products targeted toa Higher average price perounce or count for products targeted toa

Underarm deodorants Women Women

Body deodorants Women Women

Shaving cream Women Women

Shaving gel Men Men

Disposable razors No difference No difference

Nondisposable razors Men Men

Razor blades No difference Women

Designer perfume Women Women

Mass-market perfume No difference No difference

Mass-market body sprays Women Women

Source: GAO analysis of Nielsen Company data. | GAO-18-500

Notes: 

1. We selected these categories of personal care products because they are commonly-purchased 

consumer goods that were categorized by gender in the Nielsen Company’s data. Our analysis 

is limited to the set of personal care products that we analyzed. Our findings are not 

generalizable or applicable to other consumer goods. The average item price is the total dollar 

sales for a product category divided by the total number of items sold for that category. The 

average price per ounce or count is the item price divided by the quantity of product and 

weighted by the item’s proportional share of total volume sales, where quantity depicts the 

number of ounces as in the case of fragrances or the count of blades in razor blade packs.

2. All price comparisons are for similar products after controlling for size, promotional activity, 

owner brands, product packaging features, and other product attributes.

a

Statistically significant at the 95 percent level.

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.20

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, 

 20We collected prices for 16 pairs of selected personal care products sold on 4 selected 

websites for 7 consecutive days in January 2018 and again in March 2018. Generally, we 

selected pairs of products that had the same brand, packaging type, and other 

characteristics but were marketed to appeal separately to female and male consumers. 

We collected the online price data and Nielsen retail price data during different periods of 

time. Market shifts in demand or other factors that may change over time could account 

for differences between the online price data and Nielsen retail price data. See appendix II 

for further details.
Page 12 GAO-18-500 Gender-Related Pricing

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).21 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 genderrelated 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.22 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 

 21For one comparison pair of underarm deodorants, the men’s and women’s version were 

listed at the same price or close to the same price on average.

22Some academic experts and industry representatives we spoke with suggested that cost 

differences for some product attributes that firms use to differentiate products, such as 

scent and colors, may have negligible manufacturing cost differences.

We Could Not Determine 

the Extent to Which Price 

Differences May Be Due to 

Market Factors as 

Opposed to Gender Bias
Page 13 GAO-18-500 Gender-Related Pricing

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.23 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.24 For example, some product differences such as 

scents may discourage one gender from buying products targeted to 

another gender.25 In addition, consumers may find it difficult and timeconsuming 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.

 23As mentioned previously, the extent to which firms are able to implement a strategy of 

price differentiation depends on the extent of the firm’s market power, whether the firm 

can easily segment the market, and whether the firm has some control over the sale of its 

product. 

24In other words, an opportunity to exploit price differences by reselling lower-priced 

products at a higher price exists, but certain factors limit this opportunity. 

25Some research suggests social conditioning and expectations can affect women’s 

willingness to purchase products targeted to men. See Megan Duesterhas, 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.
Page 14 GAO-18-500 Gender-Related Pricing

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.26

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 

 26Transactions for these products and services generally can involve some negotiation 

between buyers and sellers. Some experts and academics, as well as agency officials 

with whom we spoke, have noted that disparate treatment based on gender can take 

place in transactions involving negotiations, in part, because buyers may not know the 

price other consumers have paid for a product or service.

Studies We Reviewed 

Found Limited 

Evidence of Price 

Differences for Men 

and Women for 

Mortgages, Small 

Business Credit, and 

Auto Purchases

Studies on Mortgages 

Found Mixed Evidence of 

Disparities in Borrowing 

Costs between Men and 

Women
Page 15 GAO-18-500 Gender-Related Pricing

disparities between men and women.27 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).

Table 2: Summary of Selected Studies on the Effect of Gender or Sex on Mortgage Borrowing Costs

Study authors Data sources

Scope

of data analyzed

Identified effects of 

gender or sex on 

borrowing costs

Example of

data limitations

Cheng, Lin, and Liu 

(2011)

Survey of Consumer 

Finances, 2004

Sample of 1,511 loans 

originated between 2000 

and 2004

No significant effect of 

gender or sex on 

mortgage loan interest 

rates

Did not control for credit 

score and debt-to-income 

ratio

Goodman, Zhu, and Bai 

(2016)

Home Mortgage 

Disclosure Act (HMDA), 

2004-2014; CoreLogic, 

2004-2014

All loans matched 

between datasets 

originated between 2004 

and 2014

Women default less than 

men with similar credit 

characteristics

HMDA and CoreLogic

data track loans by 

different geographic 

measures, requiring 

some estimation to 

match loans between 

datasets

Haughwout et al. (2009) HMDA, 2005; First 

American 

LoanPerformance, 2005

Sample of adjustable rate 

subprime loans 

originated in 2005

No significant effect of 

gender or sex on 

mortgage loan interest 

rates

Did not control for any 

fees paid at loan 

origination

Van Rensselaer et al. 

(2014)

Mortgage data from one 

large subprime lending 

company, 1997-2007 

Random sample of 30-

year fixed rate subprime 

loans originated between 

2003 and 2005

Women had significantly 

higher borrowing costs 

compared to men

Did not control for 

education level and 

shopping behavior

Wyly and Ponder (2011) HMDA, 2006; National 

Mortgage Data 

Repository, 1994-2008

619 subprime loans 

originated between 1994 

and 2008

No significant effect of 

gender or sex on 

mortgage loan interest 

rates

Did not control for credit 

score

Source: GAO analysis of selected studies that evaluate the effects of gender on mortgage borrowing costs. | GAO-18-500

 27We previously identified limitations of HMDA data, such as a lack of information on the 

credit risks of mortgage borrowers, which could affect the ability of agencies and 

regulators to identify discrimination violations of fair lending laws. See GAO, Fair Lending: 

Data Limitations and the Fragmented U.S. Financial Regulatory Structure Challenge 

Federal Oversight and Enforcement Efforts GAO-09-704 (Washington, D.C.: July 15,

2009). Effective January 1, 2018, some financial institutions are required to report 

additional data points, including borrower credit scores, for certain credit transactions 

covered by HMDA. See 12 C.F.R. pt. 1003. In addition, some research suggests that 

controlling for certain variables not related to gender may be inappropriate if those 

variables themselves may reflect outcomes related to gender discrimination. For example, 

see Ian Ayres, “Testing for Discrimination and the Problem of ‘Included Variable Bias,’”

unpublished working paper, Yale Law School (2010), accessed on June 4, 2018, at 

https://ianayres.yale.edu/sites/default/files/files/Testing%20for%20Discrimination.pdf.
Page 16 GAO-18-500 Gender-Related Pricing

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.28 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.29 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.30 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.31

 28Ping Cheng, Zhenguo Lin, and Yingchun Liu, “Do Women Pay More for Mortgages?”

The Journal of Real Estate Finance and Economics, vol. 43 (2011), 423-440. The authors 

concluded that men are likely to pay lower rates on mortgages because they tended to 

search for the lowest rates, and search is rewarded by the market, whereas women are 

more likely to receive higher rates—and subprime loans—because they are more likely to 

rely on referrals from people they know. In May 2018, BCFP published a series of briefs 

on the effect of shopping for a mortgage on interest rates, accessed on July 17, 2018, at

https://www.consumerfinance.gov/data-research/research-reports/know-before-you-owemortgage-shopping-study/. 

29SCF is a triennial survey of the various financial and demographic characteristics of U.S. 

families. The study is sponsored by the Federal Reserve in cooperation with the 

Department of the Treasury. The SCF is conducted among a representative sample of 

U.S. households. It contains information on mortgages as well as on broader household 

finances and demographics, but it does not contain data on credit scores, discount points 

or fees, or other factors that could affect interest rates.

30In a separate study, the same authors also used SCF data to look for racial disparity and 

found that African American borrowers pay more than white borrowers. They also found 

that the disparity between African American and white borrowers is more pronounced for 

women than it is for men. They did not conduct any analysis directly comparing rates 

between African American men and women. See Ping Cheng, Zhenguo Lin, and Yingchun 

Liu, “Racial Discrepancy in Mortgage Interest Rates,” The Journal of Real Estate Finance 

and Economics, vol. 51 (2015), 101-120.

31Andrew Haughwout 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. The authors examined the mortgage rates charged to a group of subprime 

mortgage borrowers in 2005 on a particular type of mortgage. The risk characteristics that 

the authors controlled for were the borrower’s credit score (FICO score), the initial 

combined loan-to-value and debt-to-income ratios, level of documentation used in the 

underwriting, whether the mortgage is for a purchase or a refinance, the loan amount, the 

presence and duration of a prepayment penalty, the type of property used as collateral, 

and the loan type.
Page 17 GAO-18-500 Gender-Related Pricing

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.32

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.33 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.34 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.35 The authors 

found that female-only borrowers on average pay more for their mortgage

loans because they generally have weaker credit characteristics, such as 

 32Elvin Wyly and C.S. Ponder, “Gender, age, and race in subprime America,” Housing 

Policy Debate, vol. 21, no. 4 (2011), 529-564. The authors also used data that combine 

demographic information with detailed financial circumstances of mortgage transactions 

for approximately 600 subprime loans that originated between 1994 and 2008. 

33Kristy N. Van Rensselaer 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. Generally, a borrower with a higher debt-to-income ratio is more likely to 

default.

34According to BCFP, the annual percentage rate (APR) is a broader measure of the cost 

of borrowing money since it reflects not only the interest rate but certain other credit costs. 

The higher the APR, the more costs over the life of the loan. See Consumer Financial 

Protection Bureau, “What is the difference between an interest rate and the Annual 

Percentage Rate (APR) in an auto loan?” Ask CFPB, accessed on July 30, 2018, at 

https://www.consumerfinance.gov/ask-cfpb/what-is-the-difference-between-an-interestrate-and-the-annual-percentage-rate-apr-in-an-auto-loan-en-733/. 

35Laurie Goodman, Jun Zhu, and Bing Bai, “Women Are Better than Men at Paying Their 

Mortgages,” Urban Institute, Research Report (2016). 
Page 18 GAO-18-500 Gender-Related Pricing

lower income, and also because a higher percentage of these mortgage

loans are subprime.36 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.37 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.38 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.39 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.40 In addition, the study 

found that women may avoid applying for credit in those markets because 

 36The authors also found that women faced higher denial rates than men.

37Most of these studies looked only at white women due to lack of data on minority-owned 

firms, thus limiting their generalizability.

38The SSBF, conducted by the Federal Reserve, collected information on small 

businesses (fewer than 500 employees) in the United States, such as owner 

characteristics, firm size, use of financial services, and the income and balance sheets of 

firms, among others. This survey has been suspended since 2003. In 2010, the Federal 

Reserve Bank of New York established the Small Business Credit Survey to annually 

survey firms about business performance, financing needs and choices, and borrowing 

experiences. The 2016 survey was the first iteration to be conducted on a nationwide level 

with the involvement of all 12 Federal Reserve Banks. For its report on women-owned 

firms, see Federal Reserve Banks of New York and Kansas City, Small Business Credit 

Survey: Report on Women-Owned Firms (New York City, N.Y.: Nov. 2017), accessed on 

July 9, 2018, at 

https://www.newyorkfed.org/medialibrary/media/smallbusiness/2016/SBCS-ReportWomenOwnedFirms-2016.pdf. 

39Ken S. Cavalluzzo, Linda C. Cavalluzzo, and John D. Wolken, “Competition, Small 

Business Financing, and Discrimination,” The Journal of Business, vol. 75, no. 4 (2002).

40A concentrated market has fewer firms and therefore generally less competition. In a 

more competitive market, discriminatory practices that could hurt a firm’s profits are more 

likely to be eliminated. 

Studies on Small Business 

Credit Did Not Identify 

Gender Differences in 

Borrowing Costs but 

Found Mixed Evidence of 

Differences for Access to 

Credit
Page 19 GAO-18-500 Gender-Related Pricing

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.41 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.

42 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.43 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 

 41Naranchimeg Mijid and Alexandra Bernasek, “Gender and the credit rationing of small 

businesses,” The Social Science Journal, vol. 50 (2013). At the same time, another study 

looking at data from three SSBFs did not find gender of the owner to have an effect on a 

firm being discouraged to apply for a loan or on being denied. See Rebel Cole 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. An older 

paper looking at a different survey found that women-owned businesses were significantly 

less likely to apply for a bank loan as well as obtain a lower loan amount compared with 

men even after controlling for important business characteristics. However, they say that 

these results may be due to women owners having different concerns about how much 

control they would like to have over their business, and their analysis did not include any 

variable to account for this difference. See Monica Zimmerman Treichel 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. The data in this study 

come from the Credit, Banks and Small Business survey conducted by the National 

Federation of Independent Business in 1987, 1995, and 2001.

42The study suggests that “non-application” arising out of fear of rejection is a form of 

credit rationing, or limiting by lenders of the amount of credit made available to borrowers, 

and therefore included these discouraged borrowers in its model to analyze the effects of 

the gender on credit rationing of small businesses.

43Naranchimeg Mijid, “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.
Page 20 GAO-18-500 Gender-Related Pricing

requested compared to their male-owned counterparts.44 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.45 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.46

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.47 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.48 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, 

 44Naranchimeg Mijid, “Gender differences in Type 1 credit rationing of small businesses in 

the US.” Cogent Economics & Finance, vol. 3 (2015).

45Susan Coleman, “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.

46The study found that white women were no less likely to apply for loans or be denied. 

47Fiona Scott Morton, 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). 

48The authors analyzed data on every new car transaction at selected dealerships from 

January 1, 1999 to February 28, 2000, to examine the relationship between car prices and 

demographics for both online and offline purchases, and they controlled for transactional 

and car-related factors that may affect the price of a car. 

Studies Found That Men 

and Women Paid or Were 

Quoted Different Prices for 

Auto Purchases and Auto 

Repairs
Page 21 GAO-18-500 Gender-Related Pricing

scripted bargaining strategies in part because dealers may have made

assumptions about women’s willingness to bargain for lower prices.49

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.50 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. 

 49Ian Ayres and Peter Siegelman, “Race and Gender Discrimination in Bargaining for a 

New Car,” The American Economic Review, vol. 85, no. 3. (1995), 304-321, accessed on 

November 7, 2016, at http://links.jstor.org/sici?sici=0002-

8282%28199506%2985%3A3%3C304%3ARAGDIB%3E2.0.CO%3B2-W. The authors 

sent 38 testers to bargain for 306 cars at 153 new-car dealerships in the Chicago area.

50Meghan R. Busse, 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).
Page 22 GAO-18-500 Gender-Related Pricing

BCFP and HUD have responsibilities to monitor consumer complaints in 

the consumer credit and housing markets, respectively.51 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:

52

• 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."53 According to BCFP 

 51Agency monitoring of market trends and consumer concerns is important because 

consumers cannot easily identify discrimination. For example, we reported that some 

marketplace lenders, which consumers can use to cover personal expenses (such as 

home or medical expenses), may use less traditional data during the underwriting 

process. See GAO, Financial Technology: Information on Subsectors and Regulatory 

Oversight, GAO-17-361 (Washington, D.C.: April 19, 2017). The use of less traditional 

data, such as utilities, rent, telephone bills, and educational history, introduces the risk that 

the data used are inaccurate and raises concerns that consumers may not have sufficient 

recourse if the information being used is incorrect.

52In addition to their online complaint forms, consumers can also submit complaints by 

phone or mail. All three agencies also receive complaints through other means, such as 

from other federal agencies (in the case of BCFP and FTC) or partner organizations, 

among others. See appendix V for more information on these agencies’ consumer 

complaints processes.

53Bureau of Consumer Financial Protection, Consumer Response Annual Report January 

1-December 31, 2017, accessed on June 26, 2018, at 

https://s3.amazonaws.com/files.consumerfinance.gov/f/documents/cfpb_consumerresponse-annual-report_2017.pdf. 

Federal Agencies 

Have Identified 

Limited or No 

Consumer Concerns 

about Price 

Differences Based on 

Sex or Gender

Federal Agencies Monitor 

Consumer Complaints and 

Identified Limited 

Examples of Concerns of 

Price Differences Based 

on the Consumer’s Sex or 

Gender
Page 23 GAO-18-500 Gender-Related Pricing

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.54

• 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 

 54For example, according to FDIC officials, from January 2012 to December 2017, FDIC 

cited three ECOA violations related to sex. FDIC officials stated that all three involved 

female loan applicants being charged higher prices than comparable males. The violations 

were identified in examinations that occurred, respectively, in 2013, 2015, and 2015. In 

two of the cases, restitution was provided. The third case was referred to DOJ. According 

to DOJ officials, after considering the matter, DOJ deferred to FDIC for administrative 

enforcement.
Page 24 GAO-18-500 Gender-Related Pricing

price differences.55 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.56

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).57 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.58 Three of the complaints from 

HUD also cited differences in price based on other protected classes 

(such as race or ethnicity).

 55For example, in United States v. Delta Funding Corp., No. 00-1872 (E.D.N.Y. 2000), the 

complaint alleged that higher mortgage broker fees were charged to African American 

females than to white males in violation of ECOA and FHA.

56According to HUD, refusing to provide a mortgage loan or mortgage insurance because 

a woman is pregnant or on family leave violates FHA’s prohibition against sex and familialstatus discrimination, which includes discrimination against individuals who have or are 

expecting a child. As of May 2017, HUD received nearly 150 complaints alleging maternity 

leave discrimination and has obtained more than $8 million in compensation for victims.

57We drew a stratified random probability sample of 100 gender-related consumer 

complaints with narratives from each database. 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. 

58Examples of gender-related complaints about price differences include being charged 

more for an auto lease and paying more for rental housing and home repairs.

Our Analysis of Federal 

Agency Data Identified 

Few Consumer 

Complaints about Price 

Differences Based on Sex 

or Gender
Page 25 GAO-18-500 Gender-Related Pricing

Table 3: Consumer Complaints about Price Differences Related to Gender or Sex by Agency, 2012–2017 

Agency

Number of gender 

or sex-related 

complaintsa

Sample of gender

or sex-related

complaints reviewed

Estimate of

gender or sex-related

complaints about price 

differences (percent)

95 percent

confidence interval

Bureau of Consumer Financial 

Protection (BCFP) 

6,117 100 0.0 0.0 – 3.0

Department of Housing and 

Urban Development (HUD)

5,421 100 4.0 1.1 – 9.9

Federal Trade Commission 

(FTC)

10,472 100 1.0 0.0 – 5.4

Source: GAO analysis of BCFP, HUD, and FTC consumer complaints data from 2012 - 2017. | GAO-18-500

Note: We drew a stratified random probability sample of 100 gender-related consumer complaints 

with narratives from BCFP’s, FTC’s, and HUD’s consumer complaint databases. 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. All estimates in this table have a margin of error at 

5.9 percentage points or fewer at a 95 percent confidence level.

a

To identify our universe of gender-related consumer complaints in BCFP’s and FTC’s databases, we 

used search terms that targeted sex or gender discrimination (e.g., gender, sex, female, male, 

treatment, discrimination). See our complete list of search terms in appendix III. HUD’s consumer 

complaint database is categorized by protective class (e.g., race, sex, national origin), and we did not 

need to use search terms to identify gender-related complaints.

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.
Page 26 GAO-18-500 Gender-Related Pricing

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 genderrelated 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.

Agencies Provide 

Resources on 

Discrimination and Have 

Not Developed Other 

Consumer Education 

Efforts on Gender in Part 

Due to Limited Public 

Complaints
Page 27 GAO-18-500 Gender-Related Pricing

In response to consumer complaints or concerns about gender disparities 

in pricing, at least one state (California) and two municipalities (MiamiDade 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).59 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.

 59In addition to these jurisdictions, the U.S. territory of Guam also prohibits charging 

consumers different prices for the same goods or services based on, among other factors, 

the sex of the consumer. 

Some State and 

Local Governments 

Have Passed Laws to 

Address Concerns 

about Gender-related 

Price Differences
Page 28 GAO-18-500 Gender-Related Pricing

Table 4: Examples of State and Local Gender-Related Pricing Laws in the United States

U.S. state or locality Law or ordinance Exceptions Enforcement

California Gender Tax Repeal Act of 1995. 

Prohibits any business 

establishment from charging 

different prices for the same or 

similar services based solely on a 

consumer’s gender. It also requires 

businesses that provide clothing 

alteration, hair care, dry cleaning, 

or laundry services to publicly 

display the prices they charge or 

disclose the prices to consumers 

upon request.

Excludes insurance, and 

prices can differ for women 

and men based on the amount 

of time, difficulty, or cost to 

provide services to those 

specific demographics. 

The State Attorney General or an 

individual can file a complaint in 

court. Individuals can also submit 

complaints to California’s Department

of Fair Employment and Housing, 

Violators receive written notice to 

correct the violation within 30 days; if 

the price is not changed, violators 

could be liable for a civil penalty of 

$4,000.

Miami-Dade County, 

Florida

Gender Pricing Ordinance, 

enacted in 1997. Prohibits 

businesses from charging different 

prices for goods or services based 

solely on a consumer’s gender.

Dry Cleaning and Laundering 

Ordinance, enacted in 1997. 

Prohibits cleaners from 

discriminating against a customer 

because of a customer’s gender 

with respect to the price charged 

for like cleaning.

Excludes insurance, and 

prices can differ based on the 

amount of time, difficulty, or 

cost involved in providing 

goods and services to 

consumers of different 

genders; and discounts can be 

based on gender as long as 

they do not exclude others 

from the program.

Office of Consumer Protection in the 

Department of Regulatory and 

Economic Resources, Business 

Affairs Division. Consumers can sue 

in Small Claims Court; Office of 

Consumer Protection can issue 

citations, but does not have capacity 

to seek and identify violations.

New York City, N.Y. City Council Bill Number 804-A, 

1998. Prohibits businesses that 

provide services, such as hair 

cutting and dry cleaning, from 

basing prices solely on gender.

Applicable to retail service 

establishments only.

New York City Department of 

Consumer Affairs can issue 

violations that can be discovered 

during routine inspections. Violators 

must pay a civil penalty of between 

$50 and $250 for first offense and for 

each successive offense, the penalty 

is between $100 and $500.

Source: GAO analysis and interviews with state and local officials. | GAO-18-500

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. 
Page 29 GAO-18-500 Gender-Related Pricing 

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.

Alicia Puente Cackley

Director, Financial Markets and Community Investment

Agency Comments
Appendix I: Nielsen Retail Price Data Analysis 

Methodology

Page 30 GAO-18-500 Gender-Related Pricing

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.1 We estimated the following regression model 

for each of our 10 product categories:

P=α+β*Male + λ* Size + θ*Owneri +η*Promotion+ μ*Xj + δ*Yk + 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.

 1

The prices were collected from participating retailers when an item was scanned at the 

cash register during check out. 

Appendix I: Nielsen Retail Price Data 

Analysis Methodology
Appendix I: Nielsen Retail Price Data Analysis 

Methodology

Page 31 GAO-18-500 Gender-Related Pricing

The variable Size represents the most appropriate specification of the 

size of the product.2

Owneri 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.3 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 Xi 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 Yk 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). These product characteristics may proxy for some 

underlying manufacturing costs or even consumer preferences. Since 

 2

We tried four different potential functional forms for size—size, square-root of size, 

log(size) and a quadratic term (size plus size-squared)—for each of our two prices. Since 

size may affect prices differently for different products (for example, the number of 

disposable razors in a plastic bag versus number of ounces of designer perfume), we 

chose different size specifications for different products. We used Akaike’s information 

criterion to choose the model with the best fit. 

3

According to Nielsen, these promotions comprise feature w/o Display, Display w/o 

Feature, Feature & Display or Temporary Price Reduction. Nielsen also reports the total 

number of weeks a product has been on promotion. We chose not to include both of these 

variables as they are highly correlated. We chose the former as we expect average prices 

to be directly influenced more by how much sold at promotional prices versus the amount 

of time the product was on promotion.
Appendix I: Nielsen Retail Price Data Analysis

Methodology

Page 32 GAO-18-500 Gender-Related Pricing

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.4 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 unweighted 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.5

 4

Firms may use different product characteristics to differentiate one product from another 

in the expectation that different segments of consumers (in this case, men and women) 

will self-select into different groups. Thus, firms may create gender-specific scents, razor 

head types, and even brands. Therefore when we control for the differentiating attribute, 

the products may not remain comparable, and any price differences become insignificant 

in our analyses. When controlling for some attribute made price differences insignificant, 

we explored further to see whether there was enough overlap between men’s and 

women’s products for that particular attribute. If there was no overlap or if it was limited to 

only a couple of products with insignificant sales, it did not make sense to conclude that 

lack of significant price differences implied no gender-related price differences for those 

products.

5

Using this estimator helps to forgo model-based variance estimates in favor of the more 

model-agnostic “robust” variances, which give more accurate assessments of the sampleto-sample variability of the parameter estimates even when the model is mis-specified. 
Appendix I: Nielsen Retail Price Data Analysis 

Methodology

Page 33 GAO-18-500 Gender-Related Pricing

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.

Table 5: Regression Results for Analysis of Selected Personal Care Products, 2016

Underarm 

deodorants

Body 

deodorants

Shaving 

cream

Shaving 

gel

Disposable

razors

Nondisposable 

razors

Razor 

blades

Designer 

perfume

Massmarket 

perfume

Massmarket 

body 

sprays

Number of 

observations

2014 64 193 435 1237 417 567 3945 2003 523

R-squarea 0.77 and 0.90 0.99 and 0.99 0.98 and 

0.99

0.79 and 

0.71

0.87 and

0.91

0.92 and

0.93

0.69 and

0.73

0.60 and 

0.61

0.64 and 

0.75

0.92 and 

0.87

Co-efficient 

for “male” in 

item price 

regressions

-0.275*

(0.120)

-0.77*

(0.178)

-0.568*

(0.096)

0.567*

(0.146)

-0.0.31

(0.229)

2.04*

(0.482)

0.303

(0.971)

-1.73**

(1.02)

0.792

(0.798)

-0.503*

(0.144)

Co-efficient 

for “male” in 

ounce or 

count price 

regressions

-0.081*

(0.038)

-0.139*

(0.029)

-0.029*

(0.005)

0.071*

(0.020)

-.004

(0.062)

1.44*

(0.507)

-0.335*

(0.146)

-2.678*

(0.636)

0.326

(0.534)

-0.166*

(.049)

Brands or 

owners

√ √ √ √ √ √ √ √ √ √

Promotional 

expenditure

√ √ √ √ √ √ √ √ √ √
Appendix I: Nielsen Retail Price Data Analysis 

Methodology

Page 34 GAO-18-500 Gender-Related Pricing

Underarm 

deodorants

Body 

deodorants

Shaving 

cream

Shaving 

gel

Disposable

razors

Nondisposable 

razors

Razor 

blades

Designer 

perfume

Massmarket 

perfume

Massmarket 

body 

sprays

Packaging 

general 

shape

√ √ √ √ √ √ √ √ √ √

Packaging 

delivery 

method

√ √ √ √ n/a n/a n/a √ √ √

Size transformation in 

regressions 

with:

Item price Square root 

(size)

(size + 

square(size))

Size (size + 

square 

(size)) 

(size + 

square 

(size)) 

(size+ 

square

(size)) 

Size Size Size Log

(size)

Ounce or 

count price 

Log(size) Log(size) (size + 

square

(size)) 

Log(size) Log(size) Log (size) (size + 

square 

(size)) 

(size + 

square 

(size))

(size + 

square 

(size))

Log

(size)

Attributes included

Form X n/a n/a n/a n/a n/a n/a n/a n/a n/a

Claim X √ X X X n/a n/a n/a n/a n/a

Scent X X X X n/a n/a n/a X X X

Color n/a n/a n/a n/a X X X n/a n/a n/a

Razor blade 

type

n/a n/a n/a n/a √ X X n/a n/a n/a

Razor head 

type

n/a n/a n/a n/a √ √ n/a n/a n/a n/a

Legend: √ = included; X = not included; n/a = data not available or relevant

* denotes significance at 5 percent or less. ** denotes significance at 10 percent.

Source: GAO analysis of Nielsen Company data. | GAO-18-500

Notes:

1. Standard errors for the coefficients are in brackets.

2. Body deodorants: We have under 100 observations for body deodorants and therefore the 

significance tests conducted may not have very high power.

3. Shaving cream and shaving gel: Shaving cream data provided by Nielsen included both 

“creams” and “gels.” Upon examining the data, we observed that gels are typically more 

expensive than creams. Therefore, we decided to treat these as separate products for our 

regression analysis.

4. Disposable razors: Our data show that consumers buy a larger quantity of more expensive 

razors targeted to men in general. For example, in the price category of $20 and above, 

consumers mostly buy razors targeted to men. These higher-priced razors are usually the ones 

that include more razors in that product configuration. For this reason, we repeated our analysis 

only for products that had an average price of less than $10 and the results remained 

qualitatively similar.
Appendix I: Nielsen Retail Price Data Analysis 

Methodology

Page 35 GAO-18-500 Gender-Related Pricing

5. Nondisposable razors: Looking at the distribution of buying behavior, it seemed that in the range 

of prices above $12.50, consumers buy razors targeted to men. We therefore split our sample 

into average prices above $12.50 and below $12.50 to analyze the relative prices. For prices 

below $12.50, it seemed there were no significant differences in prices even after we controlled 

for owners and packaging shape. Controlling for blade-type however made prices for razors 

targeted to women significantly lower. For prices above $12.50 we do not have many 

observations to make reasonable comparisons.

6. Fragrances: We chose to analyze products in the designer and mass-market segments 

separately as they are at very different price points and cannot be considered substitutes for 

each other. We also dropped the gift-pack segment as these may comprise different product and 

packaging details that we could not account for from our data. The other common segment 

between men and women is fragrance soap, which we chose not to analyze due to insignificant 

sales in the women’s segment. Fragrances are also further divided into several products such as 

body splash, fragrant soap, after-shave, eau de parfum, powder, etc. To keep our analysis 

meaningful and manageable, we decided to select only the few products that represented at 

least 5 percent of that segment in sales. Based on our analysis of types of products, price points, 

and common owners, we decided to group the fragrance products into three product categories: 

(1) designer perfumes = designer eau de toilette + designer eau de parfum + designer cologne; 

(2) mass-market perfumes = mass-market eau de toilette + mass-market eau de parfum + massmarket cologne; and (3) mass-market body spray = body spray + fragrance body spray.

a

The first R-square value is from the item price regressions and the second one is from the per unit 

price regressions.
Appendix II: Collection of Online Prices for 

Selected Personal Care Products

Page 36 GAO-18-500 Gender-Related Pricing

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.1 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.

 1

The retailers consisted of two large national drugstores and two national mass 

merchandisers. 

Appendix II: Collection of Online Prices for

Selected Personal Care Products
Appendix II: Collection of Online Prices for 

Selected Personal Care Products

Page 37 GAO-18-500 Gender-Related Pricing

Table 6: Average Online Prices Collected for Selected Personal Care Products on Four Retailer Websites, 2018

Product

Average item price (dollars)

Average price per ounce or count

(dollars)

Average product 

size (ounces or 

count)

January 2018 March 2018 January 2018 March 2018

January and

March 2018

Men’s Women’s Men’s Women’s Men’s Women’s Men’s Women’s Men’s Women’s

Body Spray Pair 

1 

5.14 5.53 4.87 5.96 1.29 1.38 1.22 1.49 4.0 4.0

Body Spray Pair 

2 

4.98 7.49 4.87 6.08 1.25 0.94 1.22 0.94 4.0 7.2

Body Spray Pair 

3 

25.41 22.48 27.08 27.62 10.16 13.22 11.40 16.25 2.5 1.7

Deodorant Pair 

1 

5.45 5.79 5.23 5.84 2.02 2.23 1.94 2.25 2.7 2.6

Deodorant Pair 

2 

4.22 4.28 4.25 4.26 1.56 1.64 1.58 1.64 2.7 2.6

Deodorant Pair 

3 

5.45 5.45 5.17 5.31 2.42 2.42 2.30 2.36 2.3 2.3

Deodorant 

Spray Pair 1 

5.48 5.32 5.28 5.32 1.37 1.33 1.32 1.33 4.0 4.0

Deodorant 

Spray Pair 2 

6.28 6.28 6.08 6.15 1.65 1.65 1.60 1.62 3.8 3.8

Disposable 

Razors Pair 1 

7.10 7.57 6.65 7.03 1.44 1.55 1.38 1.49 5.1 5.1 

Disposable 

Razors Pair 2 

4.17 3.84 5.08 3.88 0.42 0.36 0.51 0.36 10.0 10.9

Nondisposable 

Razors Pair 1

10.55 11.49 10.48 11.04 10.55 11.49 10.48 11.04 1.0 1.0 

Nondisposable 

Razors Pair 2

9.64 10.12 9.93 11.41 9.64 10.12 9.93 11.41 1.0 1.0

Razor Blades 

Pair 1 

9.43 13.85 11.48 12.90 2.36 2.96 2.87 2.69 4.0 5.0

Razor Blades 

Pair 2 

19.05 21.19 14.99 21.23 3.64 4.06 3.31 4.13 5.1 5.3

Shaving Gel 

Pair 1 

4.34 2.38 4.43 2.32 0.62 0.34 0.63 0.33 7.0 7.0

Shaving Gel 

Pair 2 

3.24 3.21 3.24 3.24 0.46 0.46 0.46 0.46 7.0 7.0

Source: GAO analysis. | GAO-18-500

Note: We manually collected prices for 16 pairs of selected personal care products from websites of 

four online retailers. 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 commonly 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. The selected online retailers were retailers that also 

operated physical store locations. For two 1-week time periods in January and March 2018, we 
Appendix II: Collection of Online Prices for 

Selected Personal Care Products

Page 38 GAO-18-500 Gender-Related Pricing

collected prices manually between 1:00 p.m. and 7:00 p.m. (ET). We collected listed prices and did 

not adjust the prices for any promotions that were available, such as online coupons or buy-one-getone-free offers. The data are presented for illustrative purposes only and are not generalizable to any

other personal care products or retailer websites.
Appendix III: Objectives, Scope, and 

Methodology

Page 39 GAO-18-500 Gender-Related Pricing

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 2016.1 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.

 1

In addition to the retail price paid, the data we purchased from Nielsen include data fields 

for product brand, package size, package design, product scent, and product color, 

among other product characteristics. Nielsen collected these retail price data from over 

200 national retailers, including grocery stores, pharmacies, mass merchandizers, and 

club stores. Together these retailers encompass 82 percent of all U.S. sales for 2016. The 

prices were collected from participating retailers when an item was scanned at the cash 

register during check out.

Appendix III: Objectives, Scope, and 

Methodology
Appendix III: Objectives, Scope, and 

Methodology

Page 40 GAO-18-500 Gender-Related Pricing

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), 
Appendix III: Objectives, Scope, and 

Methodology

Page 41 GAO-18-500 Gender-Related Pricing

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 
Appendix III: Objectives, Scope, and 

Methodology

Page 42 GAO-18-500 Gender-Related Pricing

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 genderrelated price differences.

To explore state and local efforts to address concerns about genderrelated price differences, we conducted a literature search and identified 

three state or local laws or ordinances that specifically address genderrelated 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.
Appendix IV: Descriptive Statistics of Nielsen 

Retail Price Data

Page 43 GAO-18-500 Gender-Related Pricing

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.1 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.

 1

However, comparing average price per ounce or count does not capture quantity 

discounts that may be associated with larger quantities or counts of products. We account 

for this in our regression models for analyzing comparative prices.

Appendix IV: Descriptive Statistics of Nielsen 

Retail Price Data
Appendix IV: Descriptive Statistics of Nielsen 

Retail Price Data

Page 44 GAO-18-500 Gender-Related Pricing

Table 7: Comparison of Average Retail Prices Paid in 2016 for Men’s and Women’s Products for 10 Selected Personal Care 

Product Categories

Average item pricea (dollars) Average price per ounce or countb (dollars)

Product 

Products 

targeted to 

women (w)

Products

targeted

to men (m)

Difference

(percent)

(w-m) / m 

Products 

targeted to 

women (w)

Products 

targeted

to men (m)

Difference

(percent)

(w-m) / m 

Underarm 

deodorants

4.14 3.98 4 1.70 1.22 39*

Body deodorants 2.59 5.72 -55* 1.63 1.18 38*

Shaving cream 2.80 1.95 44* 0.37 0.27 37

Shaving gel 2.90 3.35 -13 0.41 0.48 -15*

Disposable razors 5.02 5.64 -11 1.34 1.13 19

Nondisposable 

razors

9.66 10.95 -12** 2.87 4.34 -34**

Razor blades 17.63 18.49 -5 3.77 3.26 16

Designer perfume 24.91 27.32 -9* 21.62 18.63 16*

Mass-market 

perfume 

11.59 11.28 3 8.04 6.48 24**

Mass-market 

body spray

2.68 4.33 -38* 1.21 0.97 25*

Legend: * = statistically significant at the 95 percent level (p-value < 0.05); ** = statistically significant at the 90 percent level (p-value < 0.1)

Source: GAO analysis of Nielsen Company data. | GAO-18-500

Note: We selected these categories of personal care products because they are commonlypurchased consumer goods that were categorized by gender in the Nielsen Company’s data. Our 

analysis is limited to the set of personal care products that we analyzed. Our findings are not 

generalizable or applicable to other consumer goods.

a

The average item price is the total dollar sales for a product category divided by the total number of 

items sold for that category. 

b

The average price per ounce or count is the item price divided by the quantity of product and 

weighted by the proportional share of items sold, where quantity depicts the number of ounces, as in 

the case of fragrances or the count of blades in razor blade packs.

Information about sales and relative sizes of different products targeted to 

men and women are presented in table 8 below.
Appendix IV: Descriptive Statistics of Nielsen 

Retail Price Data

Page 45 GAO-18-500 Gender-Related Pricing

Table 8: Total Sales and Units Sold for Selected Personal Care Products, 2016

Total sales (dollars in 

millions)

Units sold 

(millions)

Ounces or count sold 

(millions)

Average size 

(ounces or count)

Product Men’s Women’s Men’s Women’s Men’s Women’s Men’s Women’s

Underarm deodorants 1,474 930 370 225 1,362 632 4.21 3.72

Body deodorants 15 10 1.7 5.5 9.2 10.5 7.13 4.26

Shaving cream 80 64 41 23 366 185 9.10 8.71

Shaving gel 195 115 58 40 443 322 9.63 11.38

Disposable razors 639 498 113 99 815 523 7.13 6.12

Nondisposable razors 308 214 28 22 158 113 5.44 5.82

Razor blades 803 333 44 19 276 107 7.25 5.72

Designer perfume 142 138 5 6 10 8 2.42 1.98

Mass-market perfume 73 88 6.5 7.6 19 18 2.71 2.14

Mass-market body 

spray

62 57 14 21 74 74 8.18 8.03

Source: GAO analysis of Nielsen Company data. | GAO-18-500

Note: We selected these categories of personal care products because they are commonlypurchased consumer goods that were categorized by gender in the Nielsen Company’s data. Our 

analysis is limited to the set of personal care products that we analyzed. Our findings are not 

generalizable or applicable to other consumer goods. The average item price is the total dollar sales 

for a product category divided by the total number of items sold for that category. The average price 

per ounce or count is the item price divided by the quantity of product and weighted by the item’s 

proportional share of total volume sales, where quantity depicts the number of ounces as in the case 

of fragrances or the count of blades in razor blade packs.
Appendix V: Selected Federal Agency 

Consumer Complaint Processes

Page 46 GAO-18-500 Gender-Related Pricing

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.1

Table 9: Summary of Agencies’ Efforts to Collect Consumer Complaints

Bureau of Consumer 

Financial Protection (BCFP)

Federal Trade

Commission (FTC)

Department of Housing and 

Urban Development (HUD)

How agencies collect or 

receive complaints

• Consumers can submit 

their complaint through an 

online complaint form to the 

Consumer Complaint 

Database on BCFP’s 

website.

• Phone

• Mail or fax

• Other financial regulators

• Consumers can submit their 

complaint through an online 

complaint form to the 

Consumer Sentinel Network 

on FTC’s website.

• Phone

• Mail

• FTC also imports complaints 

from other federal agencies 

(e.g., BCFP)

• State level agencies (e.g., 

State attorneys general)

• Other entities, such as the 

Council of Better Business 

Bureaus, consumer groups, 

and certain companies (e.g., 

Publishers Clearing House)

• Consumers can submit 

their complaint through an 

online complaint form to the 

HUD Enforcement 

Management System on 

HUD’s website.

• Phone

• Mail

• HUD Regional offices

• Partner organizations 

 1

Depending on the scope and magnitude, complaints may be referred to DOJ if they 

indicate a potential pattern or practice of illegal discrimination. According to DOJ officials, 

from 2012 through 2016, the Civil Rights Division investigated 69 lending matters, 

including matters referred to DOJ by the financial regulators, of which15 involved genderrelated price differences. 

Appendix V: Selected Federal Agency 

Consumer Complaint Processes
Appendix V: Selected Federal Agency 

Consumer Complaint Processes

Page 47 GAO-18-500 Gender-Related Pricing

Bureau of Consumer 

Financial Protection (BCFP)

Federal Trade

Commission (FTC)

Department of Housing and 

Urban Development (HUD)

How complaints are handled • Complaints are reviewed to 

determine if the complaint 

can be sent to a company 

for response or should be 

referred to another 

regulator. Complaints are 

categorized by consumerselected product and subproduct.

• Complaints sent to 

companies for response 

are published in the publicly 

available Consumer 

Complaint Database after 

the company has 

responded, or after the 

company has had the 

complaint for 15 calendar 

days, whichever comes 

first.

• Complaints are sorted into 

three broad categories: 

Fraud, Identity Theft, and 

Other Consumer Protection 

Problems.

• Complaints marked as “other”

are categorized by FTC staff.

• All complaints are made 

available to members of the 

Consumer Sentinel Network, 

which include 2,300 law 

enforcement members across 

the country who use the data 

to investigate targets, identify 

witnesses, and spot trends.

• Complainants are 

interviewed to collect facts 

about alleged 

discrimination by HUD staff 

to determine if HUD has 

jurisdiction over the 

complaint.

• If HUD has the authority to 

investigate, it will file the 

complaint and begin an 

investigation of the alleged 

discrimination.

•

How complaints are 

responded to and monitored

• BCFP monitors complaints 

and company responses to 

identify trends. 

• FTC makes the complaints 

available to law enforcement 

partners across the country. 

However, FTC does not 

intervene in individual 

disputes between consumers 

and merchants.

• FTC monitors its complaint 

database to identify emerging 

consumer concern trends. 

• HUD offers conciliation 

between the parties in 

cases of violations. 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.

• HUD monitors the 

complaint database to 

identify trends. 

Source: GAO analysis of BCFP, FTC, and HUD consumer complaint processes. | GAO-18-500
Appendix VI: GAO Contact and Staff 

Acknowledgments

Page 48 GAO-18-500 Gender-Related Pricing

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.

Appendix VI: GAO Contact and Staff 

Acknowledgments

GAO Contact

Staff 

Acknowledgments
Appendix VII: Bibliography

Page 49 GAO-18-500 Gender-Related Pricing

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.

Appendix VII: Bibliography
Appendix VII: Bibliography

Page 50 GAO-18-500 Gender-Related Pricing

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 MinorityOwned 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.” BrookingsWharton 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.
Appendix VII: Bibliography

Page 51 GAO-18-500 Gender-Related Pricing

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.

(101202)
The Government Accountability Office, the audit, evaluation, and investigative 

arm of Congress, exists to support Congress in meeting its constitutional 

responsibilities and to help improve the performance and accountability of the 

federal government for the American people. GAO examines the use of public 

funds; evaluates federal programs and policies; and provides analyses, 

recommendations, and other assistance to help Congress make informed 

oversight, policy, and funding decisions. GAO’s commitment to good government 

is reflected in its core values of accountability, integrity, and reliability.

The fastest and easiest way to obtain copies of GAO documents at no cost is 

through GAO’s website (https://www.gao.gov). Each weekday afternoon, GAO 

posts on its website newly released reports, testimony, and correspondence. To 

have GAO e-mail you a list of newly posted products, go to https://www.gao.gov

and select “E-mail Updates.”

The price of each GAO publication reflects GAO’s actual cost of production and 

distribution and depends on the number of pages in the publication and whether 

the publication is printed in color or black and white. Pricing and ordering 

information is posted on GAO’s website, https://www.gao.gov/ordering.htm. 

Place orders by calling (202) 512-6000, toll free (866) 801-7077, or 

TDD (202) 512-2537.

Orders may be paid for using American Express, Discover Card, MasterCard, 

Visa, check, or money order. Call for additional information.

Connect with GAO on Facebook, Flickr, Twitter, and YouTube.

Subscribe to our RSS Feeds or E-mail Updates. Listen to our Podcasts.

Visit GAO on the web at https://www.gao.gov.

Contact:

Website: https://www.gao.gov/fraudnet/fraudnet.htm

Automated answering system: (800) 424-5454 or (202) 512-7700

Orice Williams Brown, Managing Director, WilliamsO@gao.gov, (202) 512-4400, 

U.S. Government Accountability Office, 441 G Street NW, Room 7125, 

Washington, DC 20548

Chuck Young, Managing Director, youngc1@gao.gov, (202) 512-4800

U.S. Government Accountability Office, 441 G Street NW, Room 7149 

Washington, DC 20548

James-Christian Blockwood, Managing Director, spel@gao.gov, (202) 512-4707

U.S. Government Accountability Office, 441 G Street NW, Room 7814, 

Washington, DC 20548

GAO’s Mission

Obtaining Copies of 

GAO Reports and 

Testimony

Order by Phone

Connect with GAO

To Report Fraud, 

Waste, and Abuse in 

Federal Programs

Congressional 

Relations

Public Affairs

Strategic Planning and 

External Liaison

Please Print on Recycled Paper.