[Insight & Opinion] The Reality of the Government Data Voucher Support Program

Published 21 Apr.2023 14:27(KST)

Since 2019, the government has been supporting the purchase costs of data necessary for new service development and advancement through the data voucher support project. According to the Ministry of Science and ICT, which oversees this project, a total of 413.5 billion KRW has been invested over the past four years to support about 9,000 small and medium-sized enterprises (SMEs) and small business owners. This year alone, a total of 89.4 billion KRW will be invested to support 2,010 companies.

The government's intention behind the initiative is commendable. Especially from this year, the plan is to focus on supporting young companies and regional enterprises that have lacked experience in data utilization and funding, making it difficult for them to start businesses or develop new technologies using data. The support project is also being utilized in academic research, providing opportunities for master's and doctoral students?who have relatively less research support?to access high-quality data, which is a positive development.

Data differs in many ways from ordinary goods. First, the quality of the data itself is important, but it must be supported by the necessary technologies to utilize it well. Without proper utilization, data is nothing more than a mass of numbers. When conducting research or applying data in actual business, even the absence of a single essential variable can render the entire dataset meaningless. Therefore, a complementary process between demanders and suppliers is required rather than a simple transaction. Additionally, data has the characteristics of a public good. The cost of sharing data is either nonexistent or minimal, and there are almost no restrictions on repeated use. Another important point is that data prices often lack a standard for their appropriate level.

In this regard, the current government-supported voucher project is exposed to the possibility of market disruption, and signs of this are increasingly evident. First, fraudulent gains by voucher project suppliers are problematic. They induce data purchasers to apply for the voucher project through rebates and paybacks, setting data prices relatively high to extract profits. Since there is no standard for appropriate pricing, the situation is essentially "whatever the market calls it." The root of the problem is that voucher support makes consumers less sensitive to prices. The issue is that these abnormally set prices are passed on to other general data consumers, causing a vicious cycle of market disruption through overall price increases. The fact that data previously supplied free of charge to academic researchers is gradually becoming commercialized and increasingly expensive, making access more difficult, is evidence of this. Furthermore, there is no proper post-purchase management service, so even if important variables are artificially omitted from the data, individual researchers as well as relatively disadvantaged SMEs and small business owners have no way to resolve this. Since data suppliers are often large companies, it is difficult to respond to unfair trade practices.

The government plans to enhance administrative efforts by strengthening the "early warning system for fraudulent activities" to increase the likelihood of detecting fraud in advance, but this could also be seen as a waste of government resources.

So, what would be a desirable data supply and support policy? First, rather than each data demander purchasing data individually through vouchers, universities and research organizations could jointly purchase data with government support and share it. This would enable expert support for data management and utilization, and above all, prevent duplicate purchases, thereby avoiding waste of government resources. Administrative costs wasted on managing fraudulent activities in the voucher project could also be minimized. Additionally, nonprofit researchers could use the data free of charge, benefiting academia as well. The data centers of the U.S. Medicare agency and the Kilts Center at the University of Chicago Booth School of Business are good examples.