podesta_email_02855.txt

podesta-emails 884 words email

P17 D6 V11 V16 V10

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Attached and available here<http://www.whitehouse.gov/sites/default/files/whitehouse_files/docs/Big_Data_Report_Nonembargo_v2.pdf> is a new CEA report on the economics of big data and differential pricing. Please find below a blog post summarizing our findings. The Economics of Big Data and Differential Pricing Posted by Jason Furman, Tim Simcoe on February 06, 2015 at 03:58 PM EST This morning, the White House released an update to the big data working group's May 2014 report<http://www.whitehouse.gov/sites/default/files/docs/big_data_privacy_report_5.1.14_final_print.pdf> on big data, describing progress in implementing the working group's recommendations throughout the government. As part of that process, the Council of Economic Advisers released a new study<http://www.whitehouse.gov/sites/default/files/whitehouse_files/docs/Big_Data_Report_Nonembargo_v2.pdf> on big data and differential pricing. Differential pricing is the practice of charging different prices to different customers. Economic textbooks typically refer to this as "price discrimination." Everyday examples include discounts for senior citizens at the movie-theater and higher priced tickets for last minute business travelers. Economists have studied differential pricing for many years<http://www.nber.org/chapters/c0971.pdf>, and while big data seems poised to revolutionize pricing in practice<http://www.mckinsey.com/insights/marketing_sales/using_big_data_to_make_better_pricing_decisions>, it has not altered the underlying principles. Perhaps surprisingly, those principles suggest that differential pricing is often good for both firms and their customers. When prices reflect a buyer's ability to pay, sellers can often serve customers who would otherwise get priced out of the market, as with need-based financial aid for college students. Price differences can also reflect the cost or risk of serving different customers, which can discourage inappropriate risk<http://www.jstor.org/stable/3003320>-taking and expand the size<http://www.jstor.org/stable/1879431> of the market. The benefits of differential pricing indicate that it can play an important positive role in the overall economy. However, our report also explains how discriminatory pricing can pose difficult trade-offs and present serious concerns about fairness, especially when consumers are unaware of how sellers are using information about them, or when pricing is based on factors outside of individuals' control. One way to limit to unfair or inaccurate applications of big data in this context is to give consumers increased visibility into the types of information that companies collect, and more control over how it is used, as proposed<http://www.whitehouse.gov/sites/default/files/privacy-final.pdf> in the President's Consumer Privacy Bill of Rights. Big data allows companies to collect more information about customers and use it to create new kinds of measures, raising the likelihood differential pricing will become more common and more personalized over time. Our review of differential pricing online revealed that companies are presently experimenting with three broad pricing strategies: (1) experiments<http://www.nber.org/papers/w17385> that randomly manipulate prices to learn about demand; (2) efforts to steer<http://www.ccs.neu.edu/home/cbw/pdf/imc151-hannak.pdf> consumers towards particular products without altering their prices; and (3) using big data to customize<http://research.chicagobooth.edu/marketing/databases/dominicks/docs/1996_The_Value_of.pdf> prices to individual buyers. Research on the prevalence<http://www.ccs.neu.edu/home/cbw/pdf/imc151-hannak.pdf> of these pricing practices suggests that experiments and steering are common at some web sites, while cases of personalized pricing remain limited. Nevertheless, there is some evidence<http://benjaminshiller.com/images/First_Degree_PD_Using_Big_Data_Apr_8,_2014.pdf> that personalized pricing could prove very profitable, providing strong incentives for companies to continue experimenting with these tools. Our report also finds evidence that big data and related technologies can empower individual buyers. In the online environment, for example, many Internet browsers have privacy settings that allow users to control their personal information. Buyers can also use tools like price tracking and comparison web sites, or even a simple search engine, to seek out the best available price. For example, the chart below shows the relative frequency of Google searches on the term "best price" and how it is strongly correlated with the holiday shopping season. Partly because of these tools, and the competition they foster, Americans are using the Internet to shop in rapidly growing numbers<http://www.bls.gov/opub/btn/volume-3/trends-in-producer-prices-between-e-commerce-and-brick-and-mortar-retail-trade-establishments.htm>. [http://www.whitehouse.gov/sites/default/files/image/differentialpricing.png] Differential pricing in high-stakes transactions such as employment, insurance or credit provision can raise substantial concerns regarding privacy, data quality and fairness. In these settings, big data may facilitate discriminatory pricing strategies that target consumers based on factors outside their own control, or run afoul of antidiscrimination provisions in existing laws such as the Fair Credit Reporting Act or Civil Rights Act. Here too, however, big data can be a tool that works for consumers. For example, where the law protects specific groups against discriminatory pricing, big data can be used to conduct audits for disparate impact that help detect problems, both before and perhaps after a discriminatory algorithm is used on real consumers. Given the speed at which both the technology and business practices are evolving, the CEA report on Big Data and Differential Pricing provides only a first look at a set of issues that will keep economists<http://www.nber.org/papers/w19035>, engineers and policy makers busy for many years. The long-run challenge in this area is to promote the use of big data and differential pricing where they help to expand markets, while preventing unfair discrimination based on sensitive information that consumers may not understand they have revealed.