Balancing Consumer Data Privacy Concerns,
Firms need, & Economic Efficiency

Abstract

Data privacy is a hot-button topic. In contrast to other approaches, in 2018, we chose to address data privacy from an entirely different perspective. We examine three scenarios from the perspective of ownership. Data owned by i) firms, ii) consumers or the iii) government that places strict control over the use of consumer data. For the most part, i) and iii) are understood. Several critical questions arise when consumers have rights to their data; will they hoard their data or share their data with advertisers or balance their concerns for privacy against, say, with economic gains from selling their data? We concluded our study in 2019 and launched our product development. Two economists from the Graduate School of Business, Stanford University, asked and addressed the same questions in their research paper published in Sept 2020, as we concluded in 2019. Lucky for the economists, after their survey, empirical data, modeling, and rigorous mathematical analysis, we both arrived at the same conclusions. This white paper takes you into our journey and backs the basis of our β-product on published work of top US and European economists.

Introduction

Milton Friedman is considered the free market economy champion. In the early 1980s, Professor Friedman, with others, advised President Reagan on the free-market economy that heralded a protracted, uninterrupted growth in the US economy. In its wake, US jobs left for low-wage foreign destinations, and as a result, it continues to expand the gulf between rich and poor, pushing the ever-growing population to poverty.
Fed Chairman Alan Greenspan, partly inspired by Milton Friedman's free-market economy, took one step further. Greenspan instituted policies that assumed that the self-interest of organizations, specifically bankers and others, were such as that they were best capable of protecting their own shareholder's values and their equity in the firms. Soon after the 2008 meltdown, in 2009 testimony to congress, now ex-Fed Chairman Alan Greenspan admitted that these assumptions were “flawed.” Broadly assessing Milton Friedman's recommendations, his ideas brought the US economy to its knees.
Today, the US government claim to have measures that will unlikely allow the events of 2008 to repeat. With every successive Government amending or outlawing checks and balances erected by previous administrations, the public can never be sure. More of a reaction to the seismic events of 2008, it is believed, Satoshi Nakamoto put forward the idea of Blockchain, in the rawest sense, laid a framework of a transparent financial system that gives little latitude for 2008 recurrence.
Here, we propose using one feature of blockchain technology to address the pressing issue of consumer data privacy. Unlike the 2008 housing crash, where one party’s loss was gain for others, we use Blockchain’s transparency and bring awareness particular to a party who can experiences losses and at the same time use aspect of Blockchain that allows all to view any transaction as an advertisement
We create a balanced economic interdependence of all parties who depend on consumer data and protect consumer privacy. In 2018, we searched for an eco-system that delivers a balanced benefit to all parties.
Today, firms own consumer data and with no oversight to respect consumer privacy. Firms can amass consumer data because they have the vehicle, reach, capabilities, resources, analyze data, harness wealth, and then keep all that out of the public’s view has led to abuse accusations. Meanwhile, consumers have little knowledge of the content, value, and understanding of its usage.
Based on the players, we decided to examine three scenarios from the perspective of ownership; data owned by i) firms, ii) consumers, or the iii) government that places strict control over the use of consumer data. For the most part, i) and iii) are understood and researched by economists for the last eight years. ii) which gives ownership of consumers' data to its owner and gives them the rights to their data (consumer data as a “property right”) is not well studied. We arrived at the following questions that we need to answer before launching our development work,

  1. Are consumers willing to share personal data with organizations?
  2. If they are willing to share, what are the benefits they are seeking?
  3. What are the terms of the exchange of use of their data?
  4. Is there context of the data exchange that will urge the consumer to share beyond monetary gains?
  5. Do we need to rethink how we think of “data” and how we handle and organize it?

We answered the above questions and led us to launch the development of our application.
In section 1, we summarize leading economists' published work on the economics of privacy, its value to consumers, benefits to firms, and the corresponding economic impact in this data-centric eco-system.
In section 2, we discuss the current status of consumer’s privacy and the impact of the widespread availability of predictive mathematical tools in the market.
In Section 3, we present/summarize a seminal paper by two economists from the Graduate School of Business, Stanford University, who shows that it supports our startup's approach. We arrive at the same conclusion, independently.
Section 4 presents our startup’s app and correlates our approach with the paper published by Jones and Tonetti.
In Section 5, we give the audience a glimpse of our vision of the future. We believe that the data-economy will emerge from data only and provide more structure and evolve into the “next step.” We believe AI is not about the intelligent handling of data and gaining insight, and it needs to be structured to give ideas. We discuss how AI can generate ideas in the last section.





SECTION 1: CONSUMER AND CONSUMER PRIVACY




Consumer Privacy, Data, and Firms:

Consumer data is of paramount importance to the firm's survival. Many pressing questions need answers; if the data's ownership and control shifts from firms to the consumers, most importantly, what factors would influence consumers to share their data? The answer to the questions is scattered in scores of studies by economists and surveys. In our opinion, the research conducted by Sarah Spiekermann and her collaborators is the most prominent and answers the relevant questions in their paper titled "Personal data markets [1.1].”

Consumer Priorities

Once the consumer owns their data, what factors are of concern to the consumer that will influence their decisions and permit firms to access their data? In other words, what condition would the consumer impose to enable firms to use their data?

Personal data markets: Excerpts

Boston Consulting Group (BCG), one of the world's largest consulting houses, has been watching personal data markets' development closely for over a decade. BCG studies revealed, in line with privacy, consumers hold the following impression;

  1. Consumers are generally willing to share personal data with organizations, but this
  2. Sharing depends on the benefits to them and
  3. Terms of the exchange of use of their data and if they share
  4. The context of data exchange is even more critical than the data itself.
Discussion: from "Personal data markets."

Consumers accept active sharing where they are consciously involved in the exchange but are much less optimistic about passive information collection. Data use is acceptable to consumers as long as it is part of an ongoing relationship. For service delivery and marketing purposes, companies seem to be allowed to use data, but there is a mistrust in third-party sharing of data, not in an anonymous form, and least so in an identified manner. In light of the secrecy with which the firms treat consumers' data even from consumers whose data they captured, these latter results suggest that personal data markets in their current form will have difficulties finding acceptance among people. In particular, data used by third parties leads to a substantial utility drop for consumers.

According to economists, we need to treat data as ordinary tradable assets, including recognizing consumer privacy, self-determination to their data, and the right to their information as fundamental human rights. The protection of consumer privacy is a principal tenet in the data-economy and fundamental consumer rights. When consumers have no clue to the depth of the content captured from consumers' online activities, value enhancement using predictive analytics, and breadth of use of their personal information, consumer data's actual value is grossly under-estimated.

Competing values with corresponding balancing factors in the data-economy by the economists are summarized,

  1. Privacy, primarily a consumer concern
  2. Competition, consumer and firm concerns and
  3. Efficiency, firm, and government from higher revenue from a taxation point

We will discuss the effort of research economists over the past decade, in the above three, the effect of one on the other and in a differentiating environment. In the final section, we will summarize, aggregate over a decade of research and summarize that justifies that as the call for privacy gains strength, firms can sustain their growth by treating data as nonrival.

Consumer Knowledge and Right to their Data:

Consumers know very little to non about the depth of the content captured by firms. Most consumers become aware of their data held by firms when firms are legally binded to reveal data breaches. Consumers are urgently requested to change their passwords to stop the misuse of their information. National coverage follows, with every newspaper and social media platform covering the news on its front page. Outrage and then, as quickly forgotten. As data come to dominate the news/social media, the focus primarily falls on consumer privacy. Excerpts of a few of the well-cited papers: -


Research in Consumer Data Handling:

In the following two papers, consumers own their data in their setup. The main difference is that one consumer individually sells their data, and in second, how can such a consumer know that the intermediaries selling your data fetch appropriate return? In the third paper that follows, the consumer broker their data anonymously. In the fourth paper, economists answered how individual sellers become tempted to reveal more detail about themselves for higher return and show that it leads to price deterioration. In the fifth paper, economists address the impact of using sophisticated tools to gain deep insight into consumer behavior when limited information is available and the effects of replicating consumer information on market price.

  1. Consumer disclosure of personal information in several settings to firm and consequent pricing and welfare implication. "Optimal price guarantee in several settings” [1.2].
  2. Consumers sell their transactional and activity data to an interested party. What guarantees does the consumer have that it fetches appropriate return and is used appropriately in mutually agreed conditions? [1.3]
  3. Consumer data is brokered anonymously and voluntarily in a "personal data market" [1.4]
  4. The more consumers reveal, one expects, the higher the price firms are willing to pay. In reality, as the consumer continues to disclose more information about themselves, the lower the compensation until in optimized equilibrium lowest possible price is paid for the information. Dynamics, hustling data [1.5]
  5. Information is replicable in that it can be simultaneously consumed and sold to others. You can't prove that you have the information until you disclose the information, so you need to reveal it. Any disclosed info can be replicated and resold. How much can you earn in the revealing-replicating cycle? What are the factors that influence its utility? Their modeling yields diminishing returns for consumers. [1.6]

In our opinion, the critical factor for the growth is we need to make sure we avoid run against data monopsonists.

Research in Optimizing Efficiency:

Researchers explored other aspects to create efficiency in the economy, mainly dealing with firms.

  1. One study analyzed time and resources wasted on dead-end research by firms, i.e., ones that do not benefit or lead to innovation. These results are held proprietory or market competition and not revealed to any external entity and in particular to competitors. Significant efficiency losses result due to such dead-end, replication, and early abandonment of risky projects. Would society benefit if such information is for sale? [1.7]
  2. Another case studies endogenous growth model. Firms invest in propinquitous R&D for new ideas that result in non-utility patents sold to create efficiency in the market. The analysis gauges how efficiency in the patent market affects growth. [1.8]
Conclusion:

The research on the data economy built its momentum soon after the internet went mainstream. Data gradually came to be realized as a powerful new commodity. As data became available, many started to tinker with the databases. Analyzing data gave deep insight into everyday consumers’ habits that can yield substantial gains for one’s who own the data. The aggregation of people’s data, when analyzed, can produce results that predict their future behavior. In other words, the corporations can project, your and I, propensity towards goods and our reaction to such goods when offered at an opportune time and in a persistent manner that results in the firm’s profitability.

[1.1]   Spiekermann, S., Böhme, R., Acquisti, A. et al. Personal data markets. Electron Markets 25, 91–93 (2015). https://doi.org/10.1007/s12525-015-0190-1
[1.2]   Ali, S. Nageeb, Greg Lewis, and Shoshana Vasserman. 2019. “Voluntary Disclosure and Personalized Pricing.” Unpublished.
[1.3]   “A metadata-based architecture for user-centered data accountability.” Sean Maguire, Jeffrey Friedberg, M.-H. Carolyn Nguyen & Peter Haynes, Electronic Markets volume 25, pages155–160(2015)
[1.4]   Gkatzelis, V., Aperjis, C. & Huberman, B.A. Pricing private data. Electron Markets 25, 109–123 (2015). https://doi.org/10.1007/s12525-015-0188-8
[1.5]   Rayna, T., Darlington, J., & Striukova, L. (2015). Pricing music using personal data: mutually advantageous first-degree price discrimination. Electronic Markets, 25(2). https://doi:10.1007/s12525-014-0165-7
[1.6]   Ali, S. Nageeb, Ayal Chen-Zion, and Erik Lillethun. 2020. “Reselling Information.” Unpublished.
[1.7]   Akcigit, Ufuk, and Qingmin Liu. 2016. "The Role of Information in Innovation and Competition." Journal of the European Economic Association 14 (4): 828–70.
[1.8]   Ufuk Akcigit & Murat Alp Celik & Jeremy Greenwood, 2016. "Buy, Keep, or Sell: Economic Growth and the Market for Ideas," Econometrica, Vol. 84, No. 3 (May, 2016), 943–984.






SECTION 2: IMPACT ON CONSUMER PRIVACY FROM THE USE OF
AI, ANALYTIC TOOLS, GAME THEORY, DECISION THEORIES …




Analytic Tools, Consumer Data and Privacy:

Governments, particularly in the US and Europe, and its public have realized the importance of issues related to consumer data. Foremost on the agenda is consumer privacy making round through the state legislatures with a full-fledged hearing planned later this year. Apple soon plans to change its privacy rules, and app developers will need to request users' permission on its platform before tracking their online activities. This is a serious challenge to Facebook, where their business model depends on tracking users to sell "personalized ads." It is all but a declaration of war on Facebook, while Google appears to be getting ready to join the fight.

The fight among the tech giant is over the consumer data that they capture surreptitiously. Meanwhile, the consumer whose data they use to fuel their phenomenal growth does not even have the slightest clue of the depth of personal-consumer details that gives them unprecedented control over the consumer. One of this startup's primary purposes is to elevate consumers as the key power player and firms to accept consumers as participants and a key player in decision-making. The catalyst for "consumers have rights to their data."

In this whitepaper, we organize to create an equilibrium between the three players, i.e., firms, consumers, and intermediaries. This equilibrium brings enhanced benefits to all three, establishing "economic optimization" as eloquently derived mathematically and empirically backed in their seminal paper by Stanford Economics Professors Chris Jones and Christopher Tonetti of Graduate School of Business. In the following sections, we will highlight peer-reviewed published work from dozens of leading economists who studied the benefits of data analytics tools, privacy, and its economic impact. The economist aims to derive a robust economic model that delivers optimized benefit to all parties in the consumer information and privacy age.

AI, Consumer Data and Privacy

AI, Machine Learning is dominating the data industry with applications developed to analyze big data and gain insights that add unprecedented value to firms. With new applications developed continuously and many innovative uses and ideas in the pipeline, it is understood that AI will revolutionize almost every aspect of our lives. In the next section, we will discuss data, information, and economists' views on AI's impact on efficiency.

Consumer Data and Firms:
Impact of AI on sales:

Early work focused on the benefits of AI on direct sales, mainly how big-data helps grow any size firms with the sale of their products by using predictive analysis tools applications
[2.1]. Others conducted empirical analysis on sales-data and quantitatively measured AI's impact on a firm's growth
[2.2]. With the insight gained from using AI, others studied using databases to reduce sales forecast error, i.e., reducing projection errors [2.3].

Impact of AI on Corporations Internally:

Several research papers followed, looking at the benefits of AI on other aspects of business, notably, led to the question of the extent of AI's benefits on firms. Does it delivers benefits across the board, or is it disproportionate? Economists determined that big firms benefited more by reducing their capital costs [2.4].

Impact of AI in general on Corporations:

AI and Machine Learning is a powerful tool to search for patterns and predict behavior. Agrawal et al. provide an overview of the economics of machine learning in their book titled "Prediction Machines: The Simple Economics of Artificial Intelligence." [2.5].

Impact of leveraging AI in Predictions:

After the publication of the book, "Prediction Machines: The Simple Economics of Artificial Intelligence." A fury of research by Economists followed who studied the predictive capability of AI and Machine Learning. One of the many of the first, extensive, and in-depth works by economists centered around looking at AI's impact in predicting consumers' behavior from other consumers' data with similar traits. They all concluded that this depresses the data's price, and when the price falls, interest loosens, there are fewer reasons to protect data and corresponding privacy. These depressed prices lead to excessive data sharing resulting in the breakdown of the market [2.6].

AI predictive Capabilities in Financial Market

In one interesting study, economists studied AI's and Machine Learning technology's impact on financial markets. Contrary to conventional wisdom, this resulted in financial market inefficiencies. The analysts were more concerned with figuring out what competitors were doing and acting on this insight rather than conducting their analysis, resulting in efficiencies [2.7].

Role of Intermediaries:

Most of the studies before 2019 focused on consumers handling privacy on their own and directly selling their data in the market through a brokerage firm. Treating data as nonrival raises questions regarding the role played by brokerage firms (intermediaries) and the economic impact on firms and consumers. Consumer selling price to intermediaries, intermediaries selling price to firms, and competition among data intermediaries are studied [2.8].


Business benefits

The US government is a major producer of information covering broad topics concerned with the population and firms, including race, age, gender, firm's size, area of interest, and scores of characteristics that help directly or indirectly create value for the economy. The Government generates economic and financial data, statistics, analysis, and forecasts, gathered, compiled, and published as public goods for use by citizens, government agencies, researchers, nonprofits, and the business community at no cost, and this is nonrival data.

The Government generates information with the clear understanding that this will add value to the economy. There is no transaction in the distribution and dissemination and therefore no market-determined value, and it is widely used by businesses, academia, and the public in general. The authors of this paper outline and augment understanding of the importance of government data for business decision-making. They analyze how digital platform companies utilize government data in conjunction with their privately generated data (or "big data") to foster more informed business decisions and is a significant insight into the benefit of treating data as nonrival [2.9].

Re-categorizing Data: Data as new Oil, Data as Labor:

In the data-economy market that we envision, the data is generated and owned by the consumer, and the consumer defines privacy. Primary ownership of consumer-generated, consumer-owned data will hold significant tangible and nontangible financial gains, parallel of which does not exist today, when it resides with “consumers” themselves. As the consumer-data-capturing means (apps) and enabling technology (internet and smartphone) became widely available, the economists delivered a substantial body of peer-reviewed research work, mainly in the last decade. They assessed how the organic economy might benefit and re-shape in the face of economy fuelled by the data, i.e., emerging data economy market. It is natural to assume that economists re-categorize consumer data.

  1. Treat data as labor and hence "Data as a Labor." [2.10], [2.11]
  2. IMF has noticed that data is wielding substantial monetary gains for largest to smallest firms, and this phenomenon is taking place throughout the world. The IMF economists are defining and taking the position that “data is a new oil.” The IMF suggests substantial changes in policies to give consumers ownership, and law discourages undermining consumer ownership. [2.12]

Summarizing:

What does big data comprise of, where do we get the data, who owns it, what can we do with it, and most importantly, is it utilized to maximize efficiency? Can we enhance the economy by improving productivity function?


References
[2.1]
Bajari, Patrick, Victor Chernozhukov, Ali Hortaçsu, and Junichi Suzuki. 2019. “The Impact of Big Data on Firm Performance: An Empirical Investigation.” AEA Papers and Proceedings 109: 33–37.] “The impact of Big Data on firm performance.”
[2.2]
Agrawal, Ajay, Joshua Gans, and Avi Goldfarb. 2018. Prediction Machines: The Simple Economics of Artificial Intelligence. Boston: Harvard Business Press.
[2.3]
Farboodi, Maryam, and Laura Veldkamp. 2019. “A Growth Model of the Data Economy.” Unpublished.
[2.4]
Begenau, Juliane, Maryam Farboodi, and Laura Veldkamp. 2018. “Big Data in Finance and the Growth of Large Firms.” Journal of Monetary Economics 97: 71-87.
[2.5]
Agrawal, Ajay, Joshua Gans, and Avi Goldfarb. 2018. Prediction Machines: The Simple Economics of Artificial Intelligence. Boston: Harvard Business Press.
[2.6]
Acemoglu, Daron, Ali Makhdoumi, Azarakhsh Malekian, and Asuman Ozdaglar, 2020. "Too Much Data: Prices and Inefficiencies in Data Markets." Massachusetts Institute of Technology, Cambridge, MA, 02139, Fuqua School of Business, Duke University, Durham, NC, 2770, School of Management, University of Toronto, ON, M5S 3E6
[2.7]
Farboodi, Maryam, and Laura Veldkamp. 2020. “Long-Run Growth of Financial Technology.” American Economic Review 110 (8): 2485–523
[2.8]
Shota Ichihashi, "Competing Data Intermediaries," Canadian Economic Analysis Department, Bank of Canada, Ottawa, Ontario, Canada K1A 0G9. sichihasi@bankofcanada.ca
[2.9]
Hughes-Cromwick, Ellen, Associate Director, and Senior Economist, University of Michigan and Julia Coronado - President of MacroPolicy Perspectives LLC, New York City, New York. 2019. “The Value of US Government Data to US Business Decisions.” Journal of Economic Perspectives 33 (1): 131–46.
[2.10]
Arrieta Ibarra, Imanol, Leonard Goff, Diego Jiménez Hernández, Jaron Lanier, and E. Glen Weyl. 2018. "Should We Treat Data as Labor? Moving beyond 'Free.'” AEA Papers and Proceedings 108: 38–42.
[2.11]
Posner, Eric A., and E. Glen Weyl. 2018. Radical Markets: Uprooting Capitalism and Democracy for a Just Society. Princeton, NJ: Princeton University Press.))
[2.12]
Carriere-Swallow, Yan, and Vikram Haksar. 2019. “The Economics and Implications of Data: An Integrated Perspective.” IMF Departmental Paper 19/16.






SECTION 3: ECONOMICS OF DATA



Discussion of Paper
Charles I. Jones and Christopher Tonetti,
American Economic Review 2020, 110(9): 2819–2858

Background

Firms have captured consumer data and applied data analytics tools to gain in-depth insight into consumers' interests and habits. Such efforts have resulted in unprecedented rapid growth and profitability for the firms. As the analytic tools matured, researchers realized that they could predict consumer behavior and even influence for their firm’s benefit. One suspects that these tools are widely used and indiscriminately applied, such as a correlation between the analytics tools, consumer credit cards, and credit card debts. The analytic tools have evolved dangerously from being a passive tool for analyzing to aggressive influencing consumer behavior and habits. As analytical tools continue to evolve, economists analyzed how the consumer data market is affected when firms can predict consumers' interests with a high degree of probability, behavior, and habits with limited consumer information. In other words, consumer's habits and interests can be “guessed” fairly accurately with minimal data. Several models were developed and studied to analyze how consumers can benefit from selling their own data. Economists studied the impact of data brokers' (intermediaries) on data value, including competition among themselves. Democratizing sophisticated AI tools, accessible recently to any size businesses, allowed economists to analyze its impact on their growth and profitability. These papers' main objective was to achieve economic optimization for these three players – firms, consumers, and intermediaries that optimize privacy, efficiency, and competitiveness. Initially, they studied in binary terms with one's action, resulting in the others' loss or benefit. These factors were further studied when firms leveraged emerging utility tools, empirical study of consumer consumption data, and businesses' use of advanced predictive tools. A decade of these studies attempts to find solutions, realized that these factors are interconnected. With consumer data privacy becoming one of the vital elements, it led economists towards addressing consumer privacy as a key factor playing a crucial role in the economy. As discussed above, this led to detailed studies into the impact of consumer privacy on the economy. Jones and Tonetti’s paper, published in 2020, addresses these and other related questions. In an interesting and far-reaching implication, they conclude that treating data as nonrival and giving consumers ownership to their data helps the economy achieve optimum efficiency benefiting all players. We summarize Jones and Tonetti’s economic model and show that our startup’s approach and corresponding app, derived independently, is the same as theirs. In their model, consumer data is nonrival, where consumers own their data and right to exercise their privacy. They study its impact on consumers, firms, and intermediaries, and we put it into practice.


Introduction to Jones and Tonetti’s Research Paper:

Companies continue to rapidly gain a hold over consumers through the data they capture from consumers' online activities. These companies collect consumers' data by various means and analyze it using sophisticated modern analytics tools to gain deep insight into consumer behavior for the express purpose of gaining/keeping an edge. The final content detail of the individual's data is kept out of sight of consumers and competitors and remains shrouded in secrecy. Firms have never revealed what they do with the data, and it is a closely guarded secret, understood as an invaluable asset and an engine for growth and profitability. We believe the Government, policymakers, the general public, or for that matter, and by any legislation; will unlikely convince them to “hand-over,” share or reveal the Data on consumers other than raw, redacted data, and that also with considerable effort.

In this section, we will discuss a seminal paper published in 2020 by Charles I. Jones and Christopher Tonetti of Stanford Business School. They offer solutions to the concerns expressed by economists, as discussed in the previous sections. Their approach elevates consumer privacy as of principal importance, gives consumer rights to their data, and treats their data as nonrival and concludes by rigorously demonstrating that their model helps all three (firms, consumers, and intermediaries) achieve “optimum efficiency.” They achieve their efficiency by treating data as nonrival. Since firms with trillion dollars market capitalization will unlikely “hand-over” consumer data that they captured, our startup will embark on creating nonrival consumer databases.

Jones and Tonetti: Excerpts from their interview

How do you balance concerns over privacy, competition, and efficiency when considering a market for data? To answer this question, Jones and Tonetti started by modeling an optimal economy to maximize welfare. The starting point for Jones and Tonetti’s analysis is the observation that data is nonrival. In the following section, I will lead the discussion as given by Jones and Tonetti,

“Laws are being written right now concerning data use and property rights in California, in Europe, all over the world,” says Christopher Tonetti, an associate professor of economics at Stanford Graduate School of Business. “They’re being written to protect consumer privacy…”

In recent work, Tonetti and Charles I. Jones, an economics professor at Stanford GSB, studied how data is valued, with an eye toward determining what an ideal market ought to look like — a question that economists have, have given little consideration. Still, the research has been pointing in this direction.

“Who should own data?” they ask. “What restrictions should apply to the use of data?”

In answering these questions in their seminal paper, they found that the current arrangement is far from optimal: “People, not companies, should have the rights to their data, and people, not companies, should be able to sell it as they see fit, and people should have control over their data in regards to the privacy rights.” Their argument is efficiency, and in their opinion, it is inefficient for firms to own consumer data. ‘They can keep consumer data secret from consumers, benefit themselves only, and overlook consumer privacy.’ With this data, the firms can create a psychological consumer dragnet that entices them into buying products that they may have little need. The zero-sum game here is to maximize the firm's profits with little benefit to the consumer except for enlightening the consumer with “free” services.

As economists grasped the value of consumer data and only in 2019, they began to label data differently.

The Limitless Nature of Data: “Data as new Oil” and “Data as Labor”?

Data, like ideas, is peculiar in one very important dimension. Whereas most goods in any economy are scarce — if you buy a car or eat a sandwich, your neighbor can’t buy that same car or eat that same sandwich — data is limitless. Whether data is used by one person, ten people, or 10 million people, it doesn’t get depleted. In economic terms, data is nonrival, while most other goods are rivals. Jones and Tonetti believe that “At some level, that means there is inherent social value in sharing data.” Sharing data gives rise to two competing interests. On one side is privacy. “People have a natural tendency not to want everything shared,” Jones says. The same is true of companies that hoard data for competitive advantage; if a company made its consumer information public, competitors could easily undermine its business. On the other side, though, are the interests of efficiency. “It’s also important to consider the use of data as a factor of production across multiple firms,” Tonetti says. When it comes to self-driving cars, for instance, bigger datasets are better for training machine-learning algorithms. Because data is nonrival, Uber and Tesla, and every other car company could theoretically share consumers’ driving data, and each sell a better product. But they don’t. The same is true for medical research companies and their health records. Natural language companies and their speech and text archives — sharing would improve efficiency, but currently, sharing like this is rare.

Applying Jones and Tonetti Model

How do you balance concerns over privacy, competition, and efficiency when considering a market for data? To answer this question, Jones and Tonetti started by modeling an optimal economy managed by a benevolent dictator who respects all play variables. This scenario is a benchmark of what it looks like to maximize welfare.

Against this ideal, they introduced three factors;

  1. Companies own data,
  2. People own data, or
  3. Sharing of Data is essentially outlawed.

  1. In the first case, which closely resembles today’s market, companies neither respect consumer privacy nor share consumer data with anyone outside of their firms.
  2. When individuals owned their data, Jones and Tonetti found outcomes close to optimal. “Consumers care about privacy, but they care about consumption, too,” says Jones. With this conjoined incentive, consumers preserved the data they wanted private and sold data to many different firms, capitalizing on the value inherent in sharing nonrival data and benefiting all involved.
  3. The third case, where data sharing was banned, unearthed an important insight directly relevant to the present World: Failing to share data ultimately stifled economic growth. Therefore, legislation around how to regulate data concerns privacy issues and the long-term health of the economy.

The next question Jones and Tonetti is how to design a marketplace through which individual consumers can preserve and sell their data. Though nothing like this yet exists, “people are thinking about it and working on it,” says Jones. “There are ways to use blockchain with other novel technologies so that consumers own their data, and the scenario we laid out could be a reality.”

“I think everyone should have control over how their information is used.” Jones and Tonetti say their research shows that personal control of information is paramount not just to bolster personal privacy but, importantly, to make the best use of “nonrival” data to increase productivity and overall economic well-being.

Comments on nonrival Goods and their Benefits:

The world is rapidly entering into an age dominated by information where data is a commodity. Data can be used by many and never diminishes; data and ideas are nonrival dissimilar to all the remaining goods in the industrial world. Jones and Tonetti define “information as a set of all economic goods that are nonrival.” “In the information age, data is a commodity, and this commodity is nonrival since the data can be used by any number of firms or people without diminishing its value and using it simultaneously.” Examples of nonrival data include US census data, human genome, images, autonomous vehicles. Anyone can use the data to generate opportunities without reducing the amount of data available to anyone else. Jones and Tonetti's paper suggests that nonrival data, when used broadly, can potentially make significant gains for everyone involved. This approach changes the nature of competition, shifting it from collecting data and holding it from everyone else to innovation and creativity, recruiting top talent who develops solutions outside of the box, and focusing on developing products consumers desires. In instances when treating data as nonrival, the benefits had been exemplary. Credit rating company captures consumer data, analyzes it, and earns by sharing consumer credit details. GOOGLE captures street maps and adds data elements for the benefit of the public. The Government collects enormous data, analyzes it, and makes it available for public and businesses general use.

Summarizing Jones Tonetti Paper
Salient Features of Jones and Tonetti model

The following are the highlights of Jones and Tonetti’s paper;

  • Data as property rights
    • Their study's starting point is consumer versus firm ownership of data and is the fundamental building block of their research.
  • Distinguishing data ownership: firm or consumer
  • Consumer ownership of data generates consumption and welfare that are superior to firm ownership
    • In contrast, when firms own data,
      • Sharp limit to the use of data by other firms
      • Cost of secrecy,
      • Limiting consumers to benefit from their data,
      • Loss of innovation and productivity gain if the data was available for others to use
  • Consumer balances privacy versus income from selling their data
    • Privacy concerns are acknowledged not addressed
  • Unrestricted use of nonrival goods is allowed to achieve optimum allocation
  • Existence for the market for buy/sell nonrival goods
  • The market for data allows multiple firms to use the nonrival good simultaneously.
  • Each firm decides on a quantity of data to buy and sell from the market
    • Institutional arrangement in which consumer owns their behavioral data
  • Data ownership influences data access.
  • Treating data as property rights affect the efficiency associated sale and use of nonrival data can.
  • Limited use of nonrival input will incur large losses. Inefficiency arises from a nonrival input not being used at an appropriate scale
  • Consumers only commit to selling their data to multiple firms only with no clue about economic efficiency in the presence of externalities
  • They do not model firms that can learn about their customers’ from the widespread availability of consumer data.
  • In idea-based models, firms have the property rights for the ideas

Integrating all of the above parameters into account, they consider an institutional arrangement in which consumers own the data associated with their behavior. Consumers then balance their privacy concerns against the economic gains from selling data to all interested parties. Consumers take their own privacy considerations into account but are incentivized by markets to sell their data broadly to a range of firms, leading them to nearly optimal allocations. This equilibrium results in data being used broadly across firms, taking advantage of the nonrivalry of data. Across a wide range of parameter values are explored in their numerical example. Jones and Tonetti's model shows that Equilibrium welfare is just 93 percent of optimal when firms own data, compared to 99+ percent of optimal when consumers own data. Failing to appropriately take advantage of the nonrivalry of data leads consumption to be lower by more than 7 percent along the balanced growth path, even in this example in which there are sharply diminishing returns to additional data. Hence consumer ownership of data generates consumption and welfare substantially better than when firms have ownership. When taking US economy size at $21 Trillion, 7% equates to growth in the economy by $1.47 Trillion. In contrast, when firms own data, concerns about creative destruction sharply limit the amount of data they sell to other firms.


Our Conclusion:

New privacy laws are on legislature tables, and some enacted, others refined in the US, EU, and emerging economies, letting consumers control their information to bolster personal privacy and make the best use of “nonrival” data will bring considerable benefit to all involved. Treating data as nonrival will increase productivity and overall economic well-being, particularly for the consumer and definitely for the firms. Treating data as nonrival will level the field among competitors, giving all the same starting line. In this modified economy, the winner (firms) will be the ones who are the most innovative, superb management teams, hire and organize best human resources, develop or own top-notch analytics tool, and much more; will dominate their market. Supremacy and empowerment will be on creativity, innovation, and thinking outside of the box, benefiting both, the firms and the consumers, whose daily routine revolves around consuming firms' products. In our vision, nonrival data gives rise to firms in the data economy who no longer get the edge by hoarding consumer data for a competitive advantage.





SECTION 4: OUR STARTUP



Our Startup

We started our journey abroad, captured a large contract to build an e-911 system using open-source software, and delivered it in 2016. We stumbled into Blockchain back in 2013, imitated some of its security features, and delivered a product to our customers. We developed a Location-Based Marketing Application (LBMA) and other related applications. While building LBMA, we noticed the ever marginalized consumer and significant benefit the consumer’s data delivered to the firms who captured their information. One couldn’t help notice the relationship; the more consumer data captured, the higher the firm's market capitalization with corresponding diminishing returns to consumers. Generally speaking, little consideration is given to consumers' rights, compensation, privacy, limited info on consumer data usage, and consumer's habit content details after the powerful analytic tools are applied. When we embarked on the idea on which this startup based in 2018, we were unaware of Charles I. Jones and Christopher Tonetti, Economics Professor, Graduate School of Business; (American Economic Review 2020, 110(9): 2819–2858). We are driven by our organic studies.

Our Goals
Features of Jones and Tonetti model

The following are our product highlights. Our product is backed by several research papers and offers solutions to the problem highlighted in the same studies. Jones and Tonetti focus on property rights and how the associated sale and use of nonrival data can affect efficiency and conclude that data ownership influences access. Their main point is that broadly non-use of nonrival data can result in considerable losses. In their model, consumers are committed to selling their data to multiple firms with no clue about economic efficiency in the presence of externalities. Consumer interaction is necessary to create data, and Tonetti’s setup makes the consumers-own-data a property right regime a natural consideration.

How we treat data:
  • Distinguishing data ownership; firm or consumer
  • Assign property rights to data
    • Comparing consumer versus firm ownership of data is fundamental.
    • Treat data as property and assign rights as property to the consumer who creates the data from consumption or other activities.
  • Treat all data as nonrival
  • We allow unrestricted use of nonrival goods
  • We protect the data for consumers from replication
  • We prohibit reselling of consumer data unless prior agreement is in place
    • Selling consumer data to as many firms, entities as there are willing
  • Generate consumer data on demand by pre-negotiating with firms
    • Such data may or may not be nonrival based on a negotiated price
  • Create a market for buy/sell nonrival goods
Our Agreement with Consumer and their Data Usage:

  • We pre-negotiate the context of the use of the data
  • Firms can ask for specific data, such as driving habits of certain demographics in particular regions at a specific time.
  • Free if consumer’s consent, for example, medical purpose
  • Selling to the third party if details are known
  • The firm decides on a quantity of data to buy and sell from our startup
Our Process: Leveraging Blockchain for Transparency, Trust, and Integrity
  • Consumer owned-data-associated-with-their-behavior
    • Assemble,
    • Organize
    • Analyze, and
    • SHA256 hash-function generated
    • Pre and post-application of SHA256 hash function is securely stored
  • Generate data summary of the consumer owned-data-associated-with-their-behavior
  • The above two, SHA256 hash function, and the summary, is placed on Blockchain and advertised for sale
  • Summary of the consumers' behavioral data, captured it with their consent, and use SHA256 hash function.
  • Data hosted and on sale on the Blockchain.
  • The consumers are informed via our app on their smart-device that their data is for sale on Blockchain.
  • All sale-transaction of the data performed on Blockchain, and anyone can follow transactional activities.
  • Customers are provided with a public key so they can monitor detail transactional activities on Blockchain

Financial disputes related to income, from the sale of the data:
  • Conflicts addressed by re-generating from consumer-owned-data-associated-with-their-behavior the same SHA256 hash function stored on the Blockchain.
  • The startup handles compensation issues with consumers by comparing the database's data with the published revenue model.
  • Purchased consumer data is a joint ownership
    • Covers the following scenarios regarding ownership of consumer-generated data

Summarizing: What we Discourage and Don’t Allow
  • We discourage firms from learning about their customers’ from the widespread availability of the data.
    • We mail all benefits through our platform
    • Bring to consumer’s attention who is not respecting their data property rights
    • Consumers protests are sent collectively for maximum leverage
Conclusion

Integrating all of the above parameters into account, we offer an institutional arrangement in which consumers own the data associated with their behavior. Startup able consumers balance their privacy concerns against the economic gains that come their way from selling their data. As Jones and Tonetti conclude, we believe that optimum equilibrium will result from broad use of the data when treating data as nonrival. Long term goal of our startup is to explore the wide range of parameter values. We let consumers balance their privacy concerns against economic gains. With Jones and Tonetti's rigorous mathematical analysis, backed by empirical data analysis of other researchers, we believe that giving data property rights to consumers can lead to close to optimal allocations. Our startup, which acts as an intermediary between the consumer and the firm, helps overcome transaction costs that might otherwise overwhelm consumers and firms. Our startup business model, backed by Jones and Tonetti's analysis, shows the

value of solving these problems, perhaps via intermediaries and technological innovations, may be considerable, benefitting both firms and consumers.


Snapshot of our Application: Smartphone and Web Application

Our Home Page URL page is www.nonrivaldata.com . First-time signup pages displayed below

Standard Login and Merchant Sign-in and Sign-on

Two-step authentication and intuitive, easy-to-understand data layout, signed up customers can see the data nonrivaldata.com has captured. This data can be deleted but not edited to maintain data integrity.

Mobile Application

Standard pages, sign-in, and sign-on or creating an account

Customer authorizes nonrival.com, which search engines we can capture user activities. Once data is sold on the Blockchain, all activities displayed by tapping, as shown below


Our Web Page







After reading our agreement, users can click on any search engine icons and permit them to collect their online activities data. … as shown



Transactions are displayed, i.e., income that the consumer generates from noorivaldata.com sale of consumer data.



The amount received by nonrivaldata.com is shared with consumers based on the consumer information captured, the total amount received from the firm(s) from the sale, and distributed to all the consumers.




SECTION 5:IDEA
FUTURE OF DATA ECONOMY


We all know this ….

Consumers possess enormous smartphone-housed computing power at their fingertips, loaded with intuitive and easy-to-use applications, providing exceptional user experience. Close to 5 billion people carry the devices, and the numbers continue to grow as prices continue to drop. To connect a more extensive consumer base to the internet, poor or rich, and in any part of the planet, corporations are launching satellites and enabling access. Software applications are developed on a wide range of subjects to appeal to everyone, whether for their benefit or leisure. Behind the scene, corporations continuously collect ever-larger user data, and data analysts and researchers seek ever-more-powerful tools to analyze the database …. until when ….?

Economics of Ideas
Some of the economists have addressed and taken on the “economics of idea.”

At the core of Jones and Tonetti's analysis is a market for data and while Akcigit, Celik, and Greenwood analyze the market for ideas. The contrast between the two is that Jones and Tonetti allow sharing data with as many. In comparison, Ufuk Akcigit & Murat Alp Celik & Jeremy Greenwood allow an idea to be shared with many firms and allocate to one who extracts the most benefit.

Akcigit et al. develop and study an endogenous growth model where firms invest in R&D for new ideas. Return on investment depends on propinquity between an idea and the firm's line of business. A firm can sell an idea that is not relevant to its business or buy one if it fails to innovate. In their setup, firms can only use one idea at a time, and the market helps allocate the idea to the firm that could best use it. Ideas are sold or bought on the market for patents. Their analysis gauges how efficiency in the patent market affects growth. In contrast, Profs Jones and Tonetti allow data to be used by multiple firms at the same time, i.e., treat and use data as a nonrival good. ((9)) ((55))

so ….

As the database increases in size from petabyte, exabyte to zettabyte and even unfathomable today to yottabyte. Assuming the trend continues, the data will continue to grow exponentially for decades and longer. Making sense out of the vast database will likely end up, as has the data from spacecraft Voyager 1 launched in 1973, stored in the basement of the Lunar and Planetary Lab Department, University of Arizona, Tucson, Arizona. Managing such data to make “sense” out of would be extremely difficult.

Humans prefer something that they can grasp in ways when physicist Prof Murray Gell-Mann of CalTech introduced the idea of quarks and backed it with a mathematical model that allowed others to grasp and understand the working of strong interaction in particle physics. Our startup believes there is a new evolution, a next step, on the horizon. We think we're at a juncture where we can harness the power of human ingenuity and creativity; this will allow us to organize and better understand the database the way the database needs to be. Database that better reflects and places values on human creativity and ingenuity. Human ingenuity and creativity are hard to teach or guess, who has it and who doesn’t, and what better ways than to try it to see which yields the most economic benefit and rank it accordingly.

our idea ….. “idea”

In our vision, we believe we need to categorize data under “idea.” An idea, when applied, can generate data and such data falls under that specific idea. The idea can also have a family tree and even linked with other trees to generate more data. In this model, humans will generate ideas, and machines (AI and Machine Learning tools) will organize, categorize and manage the data. Ultimately, humans need to figure out how to develop codes that enable machines to create thoughts or ideas.


[5.1]
Ufuk Akcigit & Murat Alp Celik & Jeremy Greenwood, 2016. "Buy, Keep, or Sell: Economic Growth and the Market for Ideas," Econometrica, Econometric Society, vol. 84, pages 943-984, May.

GET THE APP
  • Get it on Google Play
  • Download on the App Store
  • Google Play and the Google Play logo are trademarks of Google Inc. Apple and the Apple logo are trademarks of Apple Inc.

  • NonRivalData.com - © 2021 All Rights Reserved