Does Big Data Really Make Economic Policy More Effective?
October 30, 2016
By Ben Hyman
Beyond its influence in academia and coverage in well-known media outlets , this kind of work is so novel and powerful that even presidential nominee Hillary Clinton purportedly met with Chetty for a 2-hour discussion about his findings and their implications for policy . By using IRS records that link parental federal income filings to their children’s future tax returns, Chetty’s research team was able to track children’s economic outcomes years after being exposed to high or low quality teachers, and what happened to the future income of children of equal socioeconomic backgrounds when they moved to different local economies, cities, and neighborhoods.
What makes this kind of data so appealing is not only that millions of observations lead to great precision in estimates—generating policy-friendly statements such as “a one tax dollar increase in X results in a Y increase in Z”—but also that Big Data make results widely generalizable. This stands in contrast to another more experimental movement  in economic policy evaluation toward randomized controlled trials (RCTs)—social experiments mimicking clinical trials to test the effects of various economic interventions. RCTs tend to score highly on getting the causal relationship of an intervention right, but poorly in their generalizability to the larger economy.
Despite progress in the “answers” to important debates, there are many questions in economic policy that we think we already know the answers to. This forces us to ask ourselves whether a “lack of Big Data” has really been the great constraint to unlocking prosperity-conducive policies in this country. Two topics economists generally reach consensus on are the broad effects of trade liberalization on jobs (such as the recent debate over the Trans-Pacific Partnership trade deal), and rent control policies on housing affordability. Economists—famous for “two-handed” responses that present two sides of any story—uncharacteristically agree on these issues, as indicated by two recent polls of economists on the effects of trade deals  and rent control .
Political scientists studying voting behavior have competing theories for why voters may not support issues that are likely in their self-interest. As Gregory Mankiw notes, “…people are more altruistic than economists usually think they are. But it’s also possible that it is just hard for people to determine with much precision how national policies affect their personal circumstances…”  Mankiw goes on to cite political scientists Edward Mansfield and Diana Mutz at the University of Pennsylvania who suggest that “…preferences are driven less by economic considerations and more by an individual’s psychological worldview” (New York Times, July 2016).
Despite this healthy skepticism, researchers move forward on the premise that large and more tangible studies such as Chetty’s can help bridge information gaps and better highlight the winners and losers from different policies. While not all of us are future Nobel Laureates with the connections and entrepreneurialism required to attain confidential IRS tax data , a venue does exist for researchers that are willing to advance data collection efforts at the U.S. Census Bureau to make use of big data.
While in Washington D.C., I am working as a “Special Sworn Status” researcher at one of the U.S. Census Bureau’s 23 federal statistics Research Data Centers (RDCs). At the Census, I am working on a project to analyze the effects of local tax credit policies on the behavior of thousands of large employers in California, to help determine whether these incentives are effective at keeping good jobs in the state, or whether and in which context they may detrimental to the economic welfare of the state. Increasing access to big data such as those discussed here continue to expand our understanding of important questions. Whether they help bridge the gap between voters and issues however, remains to be seen.
 Chetty, Raj, John N. Friedman, and Jonah E. Rockoff. “Measuring the impacts of teachers I and II: Evaluating bias in teacher value-added estimates.” The American Economic Review 104.9 (2014): 2593-2632 and 2633-2679, and http://www.rajchetty.com/chettyfiles/value_added.htm.
 Chetty, R., Hendren, N., Kline, P., & Saez, E. (2014). Where is the land of Opportunity? The Geography of Intergenerational Mobility in the United States. The Quarterly Journal of Economics, 129(4), 1553-1623, and http://www.equality-of-opportunity.org/
 David Leonhardt. “In Climbing Income Ladder Location Matters.” New York Times, July 22, 2013. http://www.nytimes.com/2013/07/22/business/in-climbing-income-ladder-location-matters.html?pagewanted=all
 Bob Davis. “Economist Raj Chetty’s Proposals on Inequality Draw Interest on Both Sides of the Political Aisle.” Wall Street Journal, October 20, 2015. http://www.wsj.com/articles/economist-raj-chettys-proposals-on-inequality-draw-interest-on-both-sides-of-the-political-aisle-1445383469
 Ian Parker. “The Poverty Lab. Transforming development economics, one experiment at a time.” The New Yorker, May 17, 2010. http://www.newyorker.com/magazine/2010/05/17/the-poverty-lab
 IGM Experts Panel, Unviersity of Chicago, Booth School of Business, November 11, 2014. http://www.igmchicago.org/igm-economic-experts-panel/poll-results?SurveyID=SV_d68906VNWqVmiGN
 IGM Experts Panel, Unviersity of Chicago, Booth School of Business, Febuary 7, 2011. http://www.igmchicago.org/igm-economic-experts-panel/poll-results?SurveyID=SV_6upyzeUpI73V5k0
 N. Gregory Mankiw. “Why Voters Don’t Buy It When Economists Say Global Trade is Good.” The New York Times, The Upshot, July 29, 2016. http://www.nytimes.com/2016/07/31/upshot/why-voters-dont-buy-it-when-economists-say-global-trade-is-good.html
 Jeffrey Mervis. “How Two Economists Got Direct Access to IRS Tax Records.” Science Magazine. May 22, 2014. http://www.sciencemag.org/news/2014/05/how-two-economists-got-direct-access-irs-tax-records
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