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Translating the Promise of the Evidence Act Into a Great Concept

Published onApr 28, 2022
Translating the Promise of the Evidence Act Into a Great Concept
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The article “Data Inventories for the Modern Age? Using Data Science to Open Government Data” by J. Lane, E. Gimeno, E. Levitskaya, Z. Zhang, and A. Zigoni, in this issue is a contribution that can be processed through the lens of introducing a new product or service opportunity where pent up demand may exist, but the market has been slow to emerge. This opportunity, or idea, is the Evidence Act.  Is it a great idea? A great idea is one that uniquely improves the user’s experience in a transparently obvious way, has benefits and advantages that can be easily communicated to the user, and is potentially accessible to the user.  This is the promise of the Foundations for Evidence-Based Policymaking Act (2019).

Lane et al (2022) take on the challenge of presenting the concept or operationalization of this idea.  First and foremost, it is a representation and description of the new service.  This concept is a framework for communicating to both the data user community and the development team the following:

1.     the nature of the new service;

2.     how it will work;

3.     the features and characteristics of the service and data;

4.     the benefits to the user;

5.     its reason for being; and

6.     the problems it will solve.

Being able to operationalize the concept, the authors argue for creating incentives for the core agents in this market: data providers (federal agencies), data users, and the intermediaries (e.g., publishers).  The incentives involve lowering the costs and burdens of data providers, intermediaries, and data users to engage in exchange. At the same time, the value proposition needs clear formulation and the first step is the search and screening challenge to measurement. The authors embrace the measurement challenge on the bibliometric front, which then puts the ball in the court of the data users to convert this measurement to value. If an action, policy, or service impacts the well-being of its intended target, then there must be a way to measure this. We’ll leave this challenge to the economists.

A federal statistical agency can identify the core clientele for its statistical products and then assess if the intended users are translating the data provided into value for their stakeholders.  And let’s not overlook the positive concomitant impact to assess if there are new customers for these data products. Invariably, there will be data users we never knew gained value from these data products, which can further incentivize data providers to create new opportunities to enhance existing data products. Further, the prospects of making previously unavailable data accessible can become a reality when the cost of data release and the value of the new products begin to emerge.  

Policymakers and stakeholders’ demand for relevant, timely research and analysis is insatiable in this information age. Economists and other social scientists are increasingly data-devouring analysts, combining publicly and privately provided data in creative ways to address current and emerging questions.  The advances in data science tools and machine learning algorithms are the next generation’s market mechanism that will lead to more accurate and targeted evidence- and data-based policymaking.


Acknowledgements

The findings and conclusions in this publication are those of the author and should not be construed to represent any official USDA or U.S. Government determination or policy. 

Disclosure Statement

Spiro E. Stefanou has no financial or non-financial disclosures to share for this article.


Reference

Foundations for Evidence-Based Policymaking Act of 2018, Pub. L. No. 115-435, 132 Stat. 5529 (2019).

Lane, J., Gimeno, E., Levitskaya, E., Zhang, Z., & Zigoni, A. (2022). Data inventories for the modern age? Using data science to open government data. Harvard Data Science Review, 4(2). https://doi.org/10.1162/99608f92.8a3f2336


©2022 Spiro E. Stefanou. This article is licensed under a Creative Commons Attribution (CC BY 4.0) International license, except where otherwise indicated with respect to particular material included in the article.

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