Economic data is vital to running a business, organization, or nation. Governments and businesses gather a lot of it, and analyze it extensively, to provide better services to stakeholders. However, these same entities use this same data to delve into personal lives and influence personal behavior. Ordinary people need to understand all of these uses, know the benefits, and yet guard themselves and others.
By Mark D. Harris
The world is awash in data. The government obtains data, typically by querying governmental institutions, requiring reports from private industry and organizations, and surveying groups of stakeholders. No other organization could gather information of such depth and scope. Even if some other organization attempted to gather such a volume of data, they would not provide it free to inquirers. After collection, the government checks, analyzes, categorizes, and interprets the data. Finally, the government acts on and distributes the data, hopefully for the benefit of all its citizens. Governments may use information derived from data to position resources, cut crime, minimize poverty, prevent disease, aid business, and otherwise do good.
There are many dangers when anyone has too much information. Governments have so much data that they can violate privacy and manipulate people. Big tech and large companies, from Amazon to Zhejiang, can do the same. The literature is flooded with studies trying to discover the proper use of data and information in the modern world.
Current Research
Hughes-Cromwick & Coronado (2019) summarize the types and importance of government economic data, providing examples of how the automotive, energy, and financial industries use it. Their concern is that with budget cuts, government data will lag technological advances and may even decline in scope, quality, or availability.
Mendez-Carbajo & Podleski (2020) provide a history of Fed data and discuss how Federal Reserve Economic Data (FRED) are used in educating disparate groups of Americans. Data uses include reviewing economic indicators, research, fact-checking, and even homework. Teaching people how economics works makes them more facile in the modern complex economic environment. It also allows the government to shape the decisions and expectations of citizens.
Cissé et al. (2020) investigate characteristics of location that are important to various sectors. Manufacturers prefer smaller cities and need local specialization, meaning that the specialized materials and skills must be present. Service industry firms, by contrast, prefer regional hubs and must be physically close to suppliers and customers. Data guiding such decisions are found in national, regional, state, and local databases. Government data at all levels is important to guide entrepreneurs as they start new businesses.
Callen et al. (2020) studied the effects of replacing a paper procedure for rural health clinic inspections with a connected, computerized one. The new system included government data and other requirements. Only 23% of facilities had received their required monthly inspection at baseline. Similarly, doctors were present at only 24% of facilities during regular operating hours. After the intervention, inspection rates increased to 51% and doctor attendance improved to 41.3%.
Brown-Liburd et al. (2019) editorialize about Government Economic Monitoring (GEM). This entails national governments using data on all aspects of life from companies, agencies, and all other organizations to know, track, and predict every want and need in the lives of its citizens. Their editorial is not about firms using data from governments but governments taking data from companies, and everywhere else.
Agbozo & Asamoah (2019) address a similar theme, that of data-driven e-Government (DDeG) but identify three threats. The first is that governments may not be able to maintain the privacy of their citizen’s data. The second is that governments may use prejudicial labels and extend bias by the categorization and use of data. The third is that governments will use DDeG to legitimize themselves without gaining the benefits of DDeG. Citizens need to ensure that data-driven e-government primarily benefits citizens, not bureaucrats.
Example of Clickstream Analysis
Lesk (2012) reports, “Clickstream data is seen as one of the top value adding data sources by businesses (Statista 2016a) with applications in online marketing, customer analysis, or website development.” Clickstreams are a common and yet sometimes unexpected way that organizations track user’s actions and identify preferences. Clicking from a news article into an advertisement and then onto a sponsored joke site, and doing so many times per day, over a long time period, reveals much about the individual who is doing it. Further, when many people click on a certain part of a website, firms can use similar content, style, or other characteristics to draw viewers to other things. Changing the characteristics of websites or parts of sites can be done almost instantly and organizations can test which work best.
Clickstream analysis can be complicated by users who create large numbers of fake identities and fake accounts (Sybils) to disseminate unwanted content. Turing tests (CAPTCHAs) can be overcome, and even complicated tasks can be accomplished with crowdsourcing services (Wang et al., 2017). More importantly, informed customers resent clickstream analysis as yet another invasion of their privacy. Companies, governments, and other organizations that seek big data on their customers, citizens, and other stakeholders are under increased scrutiny with how they collect and use these data, at least in the free world. Autocracies such as China, Russia, North Korea, Turkey, Iran, Belarus, and others gather such data and limit stakeholder scrutiny. Minimizing the reach of big government and big business is a big problem the world over.
The US Census
The decennial US Census is one of the most intensive data collection efforts in the world. Required by Article 1, Section 2 of the US Constitution, the census began in 1790 and is used for the distribution of federal funding, transportation and infrastructure planning, economic analysis (both governmental and commercial), disaster response planning, and general reference. States try to maximize their population counts and do whatever they can to get more federal money from the census.
Businesses use census data for a variety of purposes. A woman wishing to start an Asian restaurant can use census data to pick a location, as could a man trying to locate his new gun shop and shooting range. Either could tabulate state and zip code (ZTCA) information, along with race and income level, and enter it into geographical information software, to find areas of high density in their target population (Sushant K., 2016). Since such data is available free of charge, is reliable, and is readily available, careful study of such data can provide a competitive advantage to businesses who know how to use it.
COVID 19 has caused terrible disruption in businesses throughout the world. Investigators looking to understand the pandemic’s effect on small businesses can use the Census Bureau’s Small Business Pulse Survey (SBPS), and those wishing to grasp the pandemic’s effect on new business can access the US Census Bureau’s Business Formation Statistics (BFS). From 2020, the former shows that multiple indicators of small business performance fell deeply into the red and have remained there. By contrast, the rate of new business formation has jumped (Buffington et al., 2021).
Future Research
The questions to address on data in the future are legion. What data do organizations need to help investors and companies make the best possible decisions? How do governments get people to heed the data that they already put out? How can data be perfectly safeguarded? Who needs whose data, when and how? How can organizations move information so that it can be used by people on the ground? How can the people prevent the misuse of data, and stop such misuse when it occurs? How can researchers gather and code data to minimize bias and prejudicial labels? How can information garnered from data, both governmental and private, benefit average citizens?
What can individuals and families do to benefit from Big Data but still maximize their privacy and freedom?
In addition to answering the research questions above, individuals and families can maintain most of the benefits of technology and minimize their risk of overstep from the government or big business.
- Minimize your web and social media footprint. For some people, like internet businesses or influencers, being online is their job. For most, however, web surfing and social media interaction is a recreational activity.
- Don’t take your phone or smart watch everywhere. Only take smart devices when and where you need them.
- Write letters on paper and send them through the mail.
- Use a Virtual Private Network (VPN). A VPN encrypts your internet activity and hides your identity as you go from page to page.
- Take as much of life as possible off-line.
- Practice good cybersecurity. Have a good computer protection software on your computers, and follow sound practices regarding passwords and the like.
- Pay with cash. Unlike credit and debit cards and online payment, cash leaves little trace.
- Pressure your governmental leaders to pass solid legislation protecting privacy and promoting individual freedom.
- Follow the recommendations given in 70 Ways to Beat Inflation and Save the Environment. Spending less will decrease your overall footprint in big data.
Conclusion
Many have opined that data is the “oil” of the twenty-first century. This suggests that as oil drove wealth and power in the last century, so data will drive it in this one. Data, and the information derived from it, contribute to better decisions by individuals, companies, and governments. Everyone stands to gain from its proper use.
Data, and the information derived from it, can also predict the movement of individuals, allow surveillance of private lives, and send innocent people to unemployment or even jail on trumped up charges. Everyone stands to lose from its misuse.
An educated and active citizenry, doing many of the things noted herein, will make the difference.
References
Agbozo, E., & Asamoah, B. K. (2019). Data-driven E-Government: Exploring the Socio-Economic Ramifications. JeDEM – EJournal of EDemocracy and Open Government, 11(1), 81–90. https://doi.org/10.29379/jedem.v11i1.510
Brown-Liburd, H., Cheong, A., Vasarhelyi, M. A., & Wang, X. (2019). Measuring with Exogenous Data (MED), and Government Economic Monitoring (GEM). Journal of Emerging Technologies in Accounting, 16(1), 1–19. https://doi.org/10.2308/jeta-10682
Buffington, C., Chapman, D., Dinlersoz, E., Foster, L., & Haltiwanger, J. (2021). High-frequency data from the U.S. Census Bureau during the COVID-19 pandemic: small vs. new businesses. Business Economics. https://doi.org/10.1057/s11369-021-00229-0
Callen, M., Gulzar, S., Hasanain, A., Khan, M. Y., & Rezaee, A. (2020). Data and policy decisions: Experimental evidence from Pakistan. Journal of Development Economics, 146, 102523. https://doi.org/10.1016/j.jdeveco.2020.102523
Cissé, I., Dubé, J., & Brunelle, C. (2020). New business location: how local characteristics influence individual location decision? The Annals of Regional Science, 64(1), 185–214. https://doi.org/10.1007/s00168-019-00968-1
Hughes-Cromwick, E., & Coronado, J. (2019). The Value of US Government Data to US Business Decisions. Journal of Economic Perspectives, 33(1), 131–146. https://doi.org/10.1257/jep.33.1.131
Lesk, M. (2012). The Price of Privacy. IEEE Security & Privacy, 10(5), 79–81. https://doi.org/10.1109/msp.2012.133
Mendez-Carbajo, D., & Podleski, G. M. (2020). Federal Reserve Economic Data: A History. The American Economist, 056943452097398. https://doi.org/10.1177/0569434520973989
Sushant K., S. (2016). Geospatial analysis of census data for targeting new businesses using geoeconomics. Journal of Intelligence Studies in Business, 6(3). https://doi.org/10.37380/jisib.v6i3.192
Wang, G., Zhang, X., Tang, S., Wilson, C., Zheng, H., & Zhao, B. Y. (2017). Clickstream User Behavior Models. ACM Transactions on the Web, 11(4), 1–37. https://doi.org/10.1145/3068332
