At the end of April, several nonpartisan nonprofits sponsored two important demonstrations: the March for Science and People’s Climate March. A week of science action, that included a plethora of events in Washington, D.C. and across the country, connected the two events. These marches emphasized the importance of both a scientific and data-driven approach to developing policies that benefit the public’s interest.
These marches emphasized the importance of both a scientific and data-driven approach to developing policies that benefit the public’s interest.
The idea that the development of policy proposals should be grounded in data and scientific research is not new. Data-driven and scientific approach to policymaking inspires our society to focus on our prevalent problems and allocate resources appropriately. This approach helps to develop policies that are targeted and that produce desired results. These data that are collected should also use both quantitative and qualitative methods. Often times, in data-driven policy conversations, qualitative research methods are often not discussed. However, qualitative research methods, such as qualitative interviews or ethnography studies, for example, help us to understand the world from the subjects’ point of view. It helps to unfold the meaning of peoples’ experiences and uncover their world prior to scientific explanations.
I am going to discuss steps that can be taken to begin thinking about the data-driven approach to proposing policy. These recommendations were adopted from a 2007 report published by the Center for American Progress written by Daniel C. Esty and Reece Rushing entitled, “Governing by the Numbers: The Promise of Data-Driven Policymaking in the Information Age.” Their report details how the federal government, in particular, can benefit from adopting a data-driven to policymaking. This post, however, seeks to make the argument for a data-driven approach to policymaking for any governing body.
I am going to discuss steps that can be taken to begin thinking about the data-driven approach to proposing policy.
First, it is important to clearly identify the policy problem that you want to address. What about immigration, or education, for instance, do you want to address? Debates over policymaking routinely get slung up on how a policy issue is defined. Clearly defining the aspect of the policy issue that you would like to address will assist you to develop appropriate policy solutions.
Second, review academic literature published on this topic and research what is being done in similar states or municipalities to address the policy issue you have identified. This gives the opportunity to identify critical gaps in knowledge of the specific policy issue. Then, you can invest time in data collection and analysis to better understand the issue. As Etsy and Reece mentioned in their report, “[i]n the absence of robust, high-quality data, life and death problems may go undetected, cause and effect correlations may be missed, and comparisons from place to place may be misleading.”[2]
Next, develop a policy proposal that contains specific, quantitative goals that will measure the policy’s performance, and compare results among peers. Be sure to focus on the performance of similar state and municipal governments. These goals should be focused on results, which can later be used for a metric for program evaluation.
This is why voting, and ensuring that all citizens have the right to vote, is so important.
Lastly, as the policy is being implemented, quantitative and qualitative evaluations of its performance are imperative. In addition, performance measurement, if oversimplified or misdirected, can create warped perceptions and distorted incentives. If hospitals, for instance, were evaluated solely on death rates, these institutions may choose not to take care of the sickest patients with the greatest chance of dying. Instead, hospitals could be evaluated based their performance quality of care or by the time it takes to be seen by a doctor. Metrics need to be carefully selected to ensure that they present an accurate picture of key issues and promote desired outcomes. Finally, data might be used in ways that intrude on personal information, such as medical records, or to support inequitable policies, such as racial profiling. Hence, protections need to be in place to ensure data are not exploited.
I should also recognize that a data-driven approach to policymaking cannot address all of the challenges of governance. Data, by itself, will not reveal the optimal policy choice. Nor will data alone tell us what problems to focus on or how to direct resources. Policy decisions always depend on a combination of facts, analysis, and judgment on the part of policymakers and advocates. In sports and business—two areas that have successfully employed data-driven decision-making—goals are clear and easily measured. Goals in policymaking, however, are less straightforward and often open to debate. For example, what’s more important, a tax cut or healthcare? Data and science cannot answer these questions.
Data and science can, however, be applied in service of our values to inform policymaking. By investing in data collection and analysis, policy-makers can position themselves to spot problems faster. They can identify and test a range of policy options, learn from collective experience, target limited resources, and quickly refine and tailor policy interventions. This is why voting, and ensuring that all citizens have the right to vote, is so important. Who one votes for has a direct impact on the policies that are later enacted. At its core, a data-driven and scientific approach to developing policy is about building an effective local, state, and federal government that is receptive to the requests of its people.
[1] Kvale, S. (1996). InterViews—An introduction to qualitative research interviewing. Thousand Oaks, CA: Sage.
