The Data Revolution and Economic Analysis

Abstract

Executive SummaryMany believe that "big data" will transform business, government, and other aspects of the economy. In this article we discuss how new data may impact economic policy and economic research. Large-scale administrative data sets and proprietary private sector data can greatly improve the way we measure, track, and describe economic activity. They can also enable novel research designs that allow researchers to trace the consequences of different events or policies. We outline some of the challenges in accessing and making use of these data. We also consider whether the big data predictive modeling tools that have emerged in statistics and computer science may prove useful in economics.There was fi ve exabytes of information created between the dawn of civilization through 2003, but that much information is now created every two days, and the pace is increasing.-Eric Schmidt, former CEO of Google, 2010 1

New linked data on research investments: Scientific workforce, productivity, and public value

“…People have tried to define it by characteristics: Volume Velocity Variety (and Variability and Veracity), by source: found vs. made, by use: professionals vs. citizen science, by reach: datafication, or most fundamentally, by a change in paradigm which results from changes in many factors that affect the measurement of human behavior: the nature of the new types of data, their availability, the way in which they are collected, and data dissemination. UMETRICS fits all of these (Einav and Levin, 2013; Hey et al, 2009). …”

The STEM Requirements of 'Non-STEM' Jobs: Evidence from UK Online Vacancy Postings and Implications for Skills & Knowledge Shortages

“…Einav and Levin [18] provide an interesting discussion and some examples of recent uses of big data in economics. An example of ML application in labour economics is Frey and Osborne [28] who examine the susceptibility of jobs to computerisation in the US.…”

Mapping digital businesses with big data: Some early findings from the UK

“…'Big data' is a complex concept that needs careful specification. We follow Einav and Levin (2013), who define 'big' datasets as those that a) are available at massive scale, often millions or billions of observations; b) can be accessed in (close to) real time; c) have high 'dimensionality', that is, cover many variables including phenomena previously hard to observe quantitatively, and d) are much less structured than 'conventional' sources, such as administrative data.…”

“…Einav and Levin (2013) discuss two of these: limits on access to proprietary datasets, and the potentially limited relevance of much business data to public policy-focused research questions. Other issues include coverage (for instance, of companies not present in scraped/mined sources), reliability (when variables are probabilistic rather than directly observed, and when data is sampled), and overall quality (proprietary datasets may not be validated to the standards of administrative sources, or at all).…”

“…This is critically important for the research community, as the use of non-traditional / unstructured sources, and scraping/mining/learning tools, is growing rapidly in the social sciences (Einav and Levin, 2013;King, 2013; Varian, 2014). Enthusiasts point to huge potential in closing knowledge gaps, and taking research closer to the policy cycle.…”