Essay by Barry Bozeman and Jan Youtie: “…examines university research administration and the use of software systems that automate university research grants and contract administration, including the automatic sending of emails for reporting and compliance purposes. These systems are described as “robotic bureaucracy.” The rise of regulations and their contribution to administrative burden on university research have led university administrators to increasingly rely on robotic bureaucracy to handle compliance. This article draws on the administrative burden, behavioral public administration, and electronic communications and management literatures, which are increasingly focused on the psychological and cognitive bases of behavior. These literatures suggest that the assumptions behind robotic bureaucracy ignore the extent to which these systems shift the burden of compliance from administrators to researchers….(More)”.
The Passion Economy and the Future of Work
Li Jin at Andreessen-Horowitz: “The top-earning writer on the paid newsletter platform Substack earns more than $500,000 a year from reader subscriptions. The top content creator on Podia, a platform for video courses and digital memberships, makes more than $100,000 a month. And teachers across the US are bringing in thousands of dollars a month teaching live, virtual classes on Outschool and Juni Learning.
These stories are indicative of a larger trend: call it the “creator stack” or the “enterprization of consumer.” Whereas previously, the biggest online labor marketplaces flattened the individuality of workers, new platforms allow anyone to monetize unique skills. Gig work isn’t going anywhere—but there are now more ways to capitalize on creativity. Users can now build audiences at scale and turn their passions into livelihoods, whether that’s playing video games or producing video content. This has huge implications for entrepreneurship and what we’ll think of as a “job” in the future….(More)”.
Information Wars: How We Lost the Global Battle Against Disinformation and What We Can Do About It
Book by Richard Stengel: “Disinformation is as old as humanity. When Satan told Eve nothing would happen if she bit the apple, that was disinformation. But the rise of social media has made disinformation even more pervasive and pernicious in our current era. In a disturbing turn of events, governments are increasingly using disinformation to create their own false narratives, and democracies are proving not to be very good at fighting it.
During the final three years of the Obama administration, Richard Stengel, the former editor of Time and an Under Secretary of State, was on the front lines of this new global information war. At the time, he was the single person in government tasked with unpacking, disproving, and combating both ISIS’s messaging and Russian disinformation. Then, in 2016, as the presidential election unfolded, Stengel watched as Donald Trump used disinformation himself, weaponizing the grievances of Americans who felt left out by modernism. In fact, Stengel quickly came to see how all three players had used the same playbook: ISIS sought to make Islam great again; Putin tried to make Russia great again; and we all know about Trump.
In a narrative that is by turns dramatic and eye-opening, Information Wars walks readers through of this often frustrating battle. Stengel moves through Russia and Ukraine, Saudi Arabia and Iraq, and introduces characters from Putin to Hillary Clinton, John Kerry and Mohamed bin Salman to show how disinformation is impacting our global society. He illustrates how ISIS terrorized the world using social media, and how the Russians launched a tsunami of disinformation around the annexation of Crimea – a scheme that became the model for their interference with the 2016 presidential election. An urgent book for our times, Information Wars stresses that we must find a way to combat this ever growing threat to democracy….(More)”.
Nowcasting
/naʊˈkæstɪŋ/
A method of describing the present or the near future by analyzing datasets that are not traditionally included in the analysis (e.g. web searches, reviews, social media data, etc.)
Nowcasting is a term that originates in meteorology, which refers to “the detailed description of the current weather along with forecasts obtained by extrapolation for a period of 0 to 6 hours ahead.” Today, nowcasting is also used in other fields, such as macroeconomics and health, to provide more up-to-date statistics.
Traditionally, macroeconomic statistics are collected on a quarterly basis and released with a substantial lag. For example, GDP data for the euro area “is only available at quarterly frequency and is released six weeks after the close of the quarter.” Further, economic datasets from government agencies such as the US Census Bureau “typically appear only after multi-year lags, and the public-facing versions are aggregated to the county or ZIP code level.”
The arrival of the big data era has shown some promise to improve nowcasting. A paper by Edward L. Glaeser, Hyunjin Kim, and Michael Luca presents “evidence that Yelp data can complement government surveys by measuring economic activity in close to real-time, at a granular level, and at almost any geographic scale.” In the paper, the authors concluded:
“Our analyses of one possible data source, Yelp, suggests that these new data sources can be a useful complement to official government data. Yelp can help predict contemporaneous changes in the local economy. It can also provide a snapshot of economic change at the local level. It is a useful addition to the data tools that local policy-makers can access.
“Yet our analysis also highlights the challenges with the idea of replacing the Census altogether at any point in the near future. Government statistical agencies invest heavily in developing relatively complete coverage, for a wide set of metrics. The variation in coverage inherent in data from online platforms make it difficult to replace the role of providing official statistics that government data sources play.
“Ultimately, data from platforms like Yelp –combined with official government statistics – can provide valuable complementary datasets that will ultimately allow for more timely and granular forecasts and policy analyses, with a wider set of variables and more complete view of the local economy.”
Another example comes from the United States Federal Reserve (The Fed), which used data from payroll-processing company ADP to payroll employment. This data is traditionally provided by Current Employment Statistics (CES) survey. Despite being “one of the most carefully conducted measures of labor market activity and uses an extremely large sample, it is still subject to significant sampling error and nonsampling errors.” The Fed sought to improve the reliability of this survey by including data provided by ADP. The study found that combining CES and ADP data “reduces the error inherent in both data sources.”
However, nowcasting using big data comes with some limitations. Several researchers evaluated the accuracy of Google Flu Trends (GFT) in the 2012-2013 and 2013-2014 seasons. GFT uses flu-related google searches to make its prediction. The study found that GFT data showed significant overestimation compared to Centers for Disease Control and Prevention (CDC) flu trends prediction.
Jesse Dunietz wrote in Nautilus describing how to address the limitations of big data and make nowcasting efforts more accurate:
“But when big data isn’t seen as a panacea, it can be transformative. Several groups, like Columbia University researcher Jeffrey Shaman’s, for example, have outperformed the flu predictions of both the CDC and GFT by using the former to compensate for the skew of the latter. “Shaman’s team tested their model against actual flu activity that had already occurred during the season,” according to the CDC. By taking the immediate past into consideration, Shaman and his team fine-tuned their mathematical model to better predict the future. All it takes is for teams to critically assess their assumptions about their data.”
Secure Shouldn’t Mean Secret: A Call for Public Policy Schools to Share, Support, and Teach Data Stewardship
Paper by Maggie Reeves and Robert McMillan: “The public has long benefitted from researchers using individual-level administrative data (microdata) to answer questions on a gamut of issues related to the efficiency, effectiveness, and causality of programs and policies. However, these benefits have not been pervasive because few researchers have had access to microdata, and their tools, security practices, and technology have rarely been shared. With a clear push to expand access to microdata for purposes of rigorous analysis (Abraham et al., 2017; ADRF Network Working Group Participants, 2018), public policy schools must grapple with imperfect options and decide how to support secure data facilities for their faculty and students. They also must take the lead to educate students as data stewards who can navigate the challenges of microdata access for public policy research.
This white paper outlines the essential components of any secure facility, the pros and cons of four types of secure microdata facilities used for public policy research, the benefits of sharing tools and resources, and the importance of training. It closes with a call on public policy schools to include data stewardship as part of the standard curriculum…(More)”.
Democratic Transparency in the Platform Society
Chapter by Robert Gorwa and Timothy Garton Ash: “Following an host of major scandals, transparency has emerged in recent years as one of the leading accountability mechanisms through which the companies operating global platforms for user-generated content have attempted to regain the trust of the public, politicians, and regulatory authorities. Ranging from Facebook’s efforts to partner with academics and create a reputable mechanism for third party data access and independent research to the expanded advertising disclosure tools being built for elections around the world, transparency is playing a major role in current governance debates around free expression, social media, and democracy.
This article thus seeks to (a) contextualize the recent implementation of transparency as enacted by platform companies with an overview of the ample relevant literature on digital transparency in both theory and practice; (b) consider the potential positive governance impacts of transparency as a form of accountability in the current political moment; and (c) reflect upon the potential shortfalls of transparency that should be considered by legislators, academics, and funding bodies weighing the relative benefits of policy or research dealing with transparency in this area…(More)”.
The Urban Institute Data Catalog
Data@Urban: “We believe that data make the biggest impact when they are accessible to everyone.
Today, we are excited to announce the public launch of the Urban Institute Data Catalog, a place to discover, learn about, and download open data provided by Urban Institute researchers and data scientists. You can find data that reflect the breadth of Urban’s expertise — health, education, the workforce, nonprofits, local government finances, and so much more.
Built using open source technology, the catalog holds valuable data and metadata that Urban Institute staff have created, enhanced, cleaned, or otherwise added value to as part of our work. And it will provide, for the first time, a central, searchable resource to find many of Urban’s published open data assets.
We hope that researchers, data analysts, civic tech actors, application developers, and many others will use this tool to enhance their work, save time, and generate insights that elevate the policy debate. As Urban produces data for research, analysis, and data visualization, and as new data are released, we will continue to update the catalog.
We’re thrilled to put the power of data in your hands to better understand and respond to many critical issues facing us locally and nationally. If you have comments about the tool or the data it contains, or if you would like to share examples of how you are using these data, please feel free to contact us at datacatalog@urban.org.
Here are some current highlights of the Urban Data Catalog — both the data and research products we’ve built using the data — as of this writing:
– LODES data: The Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics (LODES) from the US Census Bureau provide detailed information on workers and jobs by census block. We have summarized these large, dispersed data into a set of census tract and census place datasets to make them easier to use. For more information, read our earlier Data@Urban blog post.
– Medicaid opioid data: Our Medicaid Spending and Prescriptions for the Treatment of Opioid Use Disorder and Opioid Overdose dataset is sourced from state drug utilization data and provides breakdowns by state, year, quarter, drug type, and brand name or generic drug status. For more information and to view our data visualization using the data, see the complete project page.
– Nonprofit and foundation data: Members of Urban’s National Center for Charitable Statistics (NCCS) compile, clean, and standardize data from the Internal Revenue Service (IRS) on organizations filing IRS forms 990 or 990-EZ, including private charities, foundations, and other tax-exempt organizations. To read more about these data, see our previous blog posts on redesigning our Nonprofit Sector in Brief Report in R and repurposing our open code and data to create your own custom summary tables….(More)”.
Nudging the Nudger: Toward a Choice Architecture for Regulators
Working Paper by Susan E. Dudley and Zhoudan Xie: “Behavioral research has shown that individuals do not always behave in ways that match textbook definitions of rationality. Recognizing that “bounded rationality” also occurs in the regulatory process and building on public choice insights that focus on how institutional incentives affect behavior, this article explores the interaction between the institutions in which regulators operate and their cognitive biases. It attempts to understand the extent to which the “choice architecture” regulators face reinforces or counteracts predictable cognitive biases. Just as behavioral insights are increasingly used to design choice architecture to frame individual decisions in ways that encourage welfare-enhancing choices, consciously designing the institutions that influence regulators’ policy decisions with behavioral insights in mind could lead to more public-welfare-enhancing policies. The article concludes with some modest ideas for improving regulators’ choice architecture and suggestions for further research….(More)”.
Index: Secondary Uses of Personal Data
By Alexandra Shaw, Andrew Zahuranec, Andrew Young, Stefaan Verhulst
The Living Library Index–inspired by the Harper’s Index–provides important statistics and highlights global trends in governance innovation. This installment focuses on public perceptions regarding secondary uses of personal data (or the re-use of data initially collected for a different purpose). It provides a summary of societal perspectives toward personal data usage, sharing, and control. It is not meant to be comprehensive–rather, it intends to illustrate conflicting, and often confusing, attitudes toward the re-use of personal data.
Please share any additional, illustrative statistics on data, or other issues at the nexus of technology and governance, with us at info@thelivinglib.org
Data ownership and control
- Percentage of Americans who say it is “very important” they control information collected about them: 74% – 2016
- Americans who think that today’s privacy laws are not good enough at protecting people’s privacy online: 68% – 2016
- Americans who say they have “a lot” of control over how companies collect and use their information: 9% – 2015
- In a survey of 507 online shoppers, the number of respondents who indicated they don’t want brands tracking their location: 62% – 2015
- In a survey of 507 online shoppers, the amount who “prefer offers that are targeted to where they are and what they are doing:” 60% – 2015
- Number of surveyed American consumers willing to provide data to corporations under the following conditions:
- “Data about my social concerns to better connect me with non-profit organizations that advance those causes:” 19% – 2018
- “Data about my DNA to help me uncover any hereditary illnesses:” 21% – 2018
- “Data about my interests and hobbies to receive relevant information and offers from online sellers:” 32% – 2018
- “Data about my location to help me find the fastest route to my destination:” 40% – 2018
- “My email address to receive exclusive offers from my favorite brands:” 56% – 2018
Consumer Attitudes
- Academic study participants willing to donate personal data to research if it could lead to public good: 60% – 2014
- Academic study participants willing to share personal data for research purposes in the interest of public good: 25% – 2014
- Percentage who expect companies to “treat [them] like an individual, not as a member of some segment like ‘millennials’ or ‘suburban mothers:’” 74% – 2018
- Percentage who believe that brands should understand a “consumer’s individual situation (e.g. marital status, age, location, etc.)” when they’re being marketed to: 70% – 2018 Number who are “more annoyed” by companies now compared to 5 years ago: 40% – 2018Percentage worried their data is shared across companies without their permission: 88% – 2018Amount worried about a brand’s ability to track their behavior while on the brand’s website, app, or neither: 75% – 2018
- Consumers globally who expect brands to anticipate needs before they arise: 33% – 2018
- Surveyed residents of the United Kingdom who identify as:
- “Data pragmatists” willing to share personal data “under the right circumstances:” 58% – 2017
- “Fundamentalists,” who would not share personal data for better services: 24% – 2017
- Respondents who think data sharing is part of participating in the modern economy: 62% – 2018
- Respondents who believe that data sharing benefits enterprises more than consumers: 75% – 2018
- People who want more control over their data that enterprises collect: 84% – 2018
- Percentage “unconcerned” about personal data protection: 18% – 2018
- Percentage of Americans who think that government should do more to regulate large technology companies: 55% – 2018
- Registered American voters who trust broadband companies with personal data “a great deal” or “a fair amount”: 43% – 2017
- Americans who report experiencing a major data breach: 64% – 2017
- Number of Americans who believe that their personal data is less secure than it was 5 years ago: 49% – 2019
- Amount of surveyed American citizens who consider trust in a company an important factor for sharing data: 54% – 2018
Convenience
Microsoft’s 2015 Consumer Data Value Exchange Report attempts to understand consumer attitudes on the exchange of personal data across the global markets of Australia, Brazil, Canada, Colombia, Egypt, Germany, Kenya, Mexico, Nigeria, Spain, South Africa, United Kingdom and the United States. From their survey of 16,500 users, they find:
- The most popular incentives for sharing data are:
- Respondents who would prefer to see more ads to get new services: 34% – 2015
- Respondents willing to share search terms for a service that enabled fewer steps to get things done: 70% – 2015
- Respondents willing to share activity data for such an improvement: 82% – 2015
- Respondents willing to share their gender for “a service that inspires something new based on others like them:” 79% – 2015
A 2015 Pew Research Center survey presented Americans with several data-sharing scenarios related to convenience. Participants could respond: “acceptable,” “it depends,” or “not acceptable” to the following scenarios:
- Share health information to get access to personal health records and arrange appointments more easily:
- Share data for discounted auto insurance rates:
- Share data for free social media services:
- Share data on smart thermostats for cheaper energy bills:
Other Studies
- Surveyed banking and insurance customers who would exchange personal data for:
- Surveyed banking and insurance customers willing to share data specifically related to income, location and lifestyle habits to:
- In a survey of over 7,000 members of the public around the globe, respondents indicated:
- They thought “smartphone and tablet apps used for navigation, chat, and news that can access your contacts, photos, and browsing history” is “creepy;” 16% – 2016
- Emailing a friend about a trip to Paris and receiving advertisements for hotels, restaurants and excursions in Paris is “creepy:” 32% – 2016
- A free fitness-tracking device that monitors your well-being and sends a monthly report to you and your employer is “creepy:” 45% – 2016
- A telematics device that allows emergency services to track your vehicle is “creepy:” 78% – 2016
- The number of British residents who do not want to work with virtual agents of any kind: 48% – 2017
- Americans who disagree that “if companies give me a discount, it is a fair exchange for them to collect information about me without my knowing”: 91% – 2015
Data Brokers, Intermediaries, and Third Parties
- Americans who consider it acceptable for a grocery store to offer a free loyalty card in exchange for selling their shopping data to third parties: 47% – 2016
- Number of people who know that “searches, site visits and purchases” are reviewed without consent: 55% – 2015
- The number of people in 1991 who wanted companies to ask them for permission first before collecting their personal information and selling that data to intermediaries: 93% – 1991
- Number of Americans who “would be very concerned if the company at which their data were stored sold it to another party:” 90% – 2008
- Percentage of Americans who think it’s unacceptable for their grocery store to share their shopping data with third parties in exchange for a free loyalty card: 32% – 2016
- Percentage of Americans who think that government needs to do more to regulate advertisers: 64% – 2016
- Registered American voters who are “somewhat uncomfortable” or “very uncomfortable” with companies like Internet service providers or websites using personal data to recommend stories, articles, or videos: 56% – 2017
- Registered American voters who are “somewhat uncomfortable” or “very uncomfortable” with companies like Internet service providers or websites selling their personal information to third parties for advertising purposes: 64% – 2017
Personal Health Data
The Robert Wood Johnson Foundation’s 2014 Health Data Exploration Project Report analyzes attitudes about personal health data (PHD). PHD is self-tracking data related to health that is traceable through wearable devices and sensors. The three major stakeholder groups involved in using PHD for public good are users, companies that track the users’ data, and researchers.
- Overall Respondents:
- Percentage who believe anonymity is “very” or “extremely” important: 67% – 2014
- Percentage who “probably would” or “definitely would” share their personal data with researchers: 78% – 2014
- Percentage who believe that they own—or should own—all the data about them, even when it is indirectly collected: 54% – 2014
- Percentage who think they share or ought to share ownership with the company: 30% – 2014
- Percentage who think companies alone own or should own all the data about them: 4% – 2014
- Percentage for whom data ownership “is not something I care about”: 13% – 2014
- Percentage who indicated they wanted to own their data: 75% – 2014
- Percentage who would share data only if “privacy were assured:” 68% – 2014
- People who would supply data regardless of privacy or compensation: 27% – 2014
- Percentage of participants who mentioned privacy, anonymity, or confidentiality when asked under what conditions they would share their data: 63% – 2014
- Percentage who would be “more” or “much more” likely to share data for compensation: 56% – 2014
- Percentage who indicated compensation would make no difference: 38% – 2014
- Amount opposed to commercial or profit-making use of their data: 13% – 2014
- Percentage of people who would only share personal health data with a guarantee of:
- Surveyed Researchers:
- Percentage who agree or strongly agree that self-tracking data would help provide more insights in their research: 89% – 2014
- Percentage who say PHD could answer questions that other data sources could not: 95% – 2014
- Percentage who have used public datasets: 57% – 2014
- Percentage who have paid for data for research: 19% – 2014
- Percentage who have used self-tracking data before for research purposes: 46% – 2014
- Percentage who have worked with application, device, or social media companies: 23% – 2014
- Percentage who “somewhat disagree” or “strongly disagree” there are barriers that cannot be overcome to using self-tracking data in their research: 82% – 2014
SOURCES:
“2019 Accenture Global Financial Services Consumer Study: Discover the Patterns in Personality”, Accenture, 2019.
“Americans’ Views About Data Collection and Security”, Pew Research Center, 2015.
“Data Donation: Sharing Personal Data for Public Good?”, ResearchGate, 2014.
“Data privacy: What the consumer really thinks,” Acxiom, 2018.
“Exclusive: Public wants Big Tech regulated”, Axios, 2018.
“Consumer data value exchange,” Microsoft, 2015.
“Crossing the Line: Staying on the right side of consumer privacy,” KPMG International Cooperative, 2016.
“How do you feel about the government sharing our personal data? – livechat”, The Guardian, 2017.
“Personal data for public good: using health information in medical research”, The Academy of Medical Sciences, 2006.
“Personal Data for the Public Good: New Opportunities to Enrich Understanding of Individual and Population Health”, Robert Wood Johnson Foundation, Health Data Exploration Project, Calit2, UC Irvine and UC San Diego, 2014.
“Pew Internet and American Life Project: Cloud Computing Raises Privacy Concerns”, Pew Research Center, 2008.
“Poll: Little Trust That Tech Giants Will Keep Personal Data Private”, Morning Consult & Politico, 2017.
“Privacy and Information Sharing”, Pew Research Center, 2016.
“Privacy, Data and the Consumer: What US Thinks About Sharing Data”, MarTech Advisor, 2018.
“Public Opinion on Privacy”, Electronic Privacy Information Center, 2019.
“Selligent Marketing Cloud Study Finds Consumer Expectations and Marketer Challenges are Rising in Tandem”, Selligent Marketing Cloud, 2018.
“The Data-Sharing Disconnect: The Impact of Context, Consumer Trust, and Relevance in Retail Marketing,” Boxever, 2015.
“Microsoft Research reveals understanding gap in the brand-consumer data exchange,” Microsoft Research, 2015.
“Survey: 58% will share personal data under the right circumstances”, Marketing Land: Third Door Media, 2019.
“The state of privacy in post-Snowden America”, Pew Research Center, 2016.
The Tradeoff Fallacy: How Marketers Are Misrepresenting American Consumers And Opening Them Up to Exploitation”, University of Pennsylvania, 2015.
Andrew Yang proposes that your digital data be considered personal property
Michael Grothaus at Fast Company: “2020 Democratic presidential candidate Andrew Yang may not be at the top of the race when it comes to polling (Politico currently has him ranked as the 7th most-popular Democratic contender), but his policies, including support for universal basic income, have made him popular among a subset of young, liberal-leaning, tech-savvy voters. Yang’s latest proposal, too, is sure to strike a chord with them.
The presidential candidate published his latest policy proposal today: to treat data as a property right. Announcing the proposal on his website, Yang lamented how our data is collected, used, and abused by companies, often with little awareness or consent from us. “This needs to stop,” Yang says. “Data generated by each individual needs to be owned by them, with certain rights conveyed that will allow them to know how it’s used and protect it.”
The rights Yang is proposing:
- The right to be informed as to what data will be collected, and how it will be used
- The right to opt out of data collection or sharing
- The right to be told if a website has data on you, and what that data is
- The right to be forgotten; to have all data related to you deleted upon request
- The right to be informed if ownership of your data changes hands
- The right to be informed of any data breaches including your information in a timely manner
- The right to download all data in a standardized format to port to another platform…(More)”.