Abstract
Are data scary?
In an early episode of the Simpsons (1992), Homer, lacking the money to purchase a proper Crusty the Clown bed for Bart, constructs one of his own. The bed is not elegantly fashioned.
In fact, it is scary.
Bart can’t sleep and is found in the morning muttering “Can’t sleep. Clown will eat me.”
Does this parallel how many people approach data and statistics? “Can’t analyze. Data will eat me.”
Running the Numbers: A Practical Guide to Regional Economic and Social Analysis (2014), written by research consultant John Quinterno, represents an admirable effort to make data less scary and data analysis more relevant to economic development practitioners (EDPs). Researchers would also do well by keeping this handy for reference.
The data sources discussed are comprehensive and current. It is what a manual for one national centralized U.S. statistical agency—Statistics America—might look like, if the nation did not have a fractured statistical infrastructure that resembles an inelegantly constructed Crusty the Clown bed. (Because the focus is on regional and social analysis, statistical agencies like the Bureau of Transportation Statistics are not discussed.) This reviewer—even having cut his teeth on data working at the U.S. Bureau of Economic Analysis—learned about some unfamiliar sources and types of regional data covered in this volume. Having worked for a national statistical agency and as a current heavy user of the types of data used for economic and regional analysis, discovering the appendices that contain copies of the Census 2010 and the American Community Survey questionnaires was illuminating and helpful. Often overlooked by data users, the questions that generate the tallies, for example, on the percentage of the population unable to live independently, can provide critical insights into understanding why the data are tallied the way they are and, at minimum, infer appropriate use of the results.
Some may disagree with the following statement but we need to face the facts: No book on demographic, economic, and social data that is comprehensive and adequately detailed is going to be an easy read. That said, Quinterno breaks the subject matter into digestible portions. The series of numbered “boxes” that run through the book describe a multitude of datasets and definitions, making for easy reference. The organizational structure of the chapters helps the reader find topics of interest easily.
The book also presents the standard analytical tools for analyzing regional data. In contrast to spatial econometrics, which the author wisely does not discuss, these tools and techniques—location quotients and shift-share analysis, for example—are not difficult to apply for anyone with a spreadsheet. A data user, an EDP, for example, may have more trouble negotiating government websites to access the data. And here, Quinterno lets the reader down by not referencing consolidators of free public data that can provide data from several sources and agencies all in one go for the geographies the user wants. Why should a data user have to endure the pain of visiting the U.S. Bureau of Economic Analysis (easy to use) to get compensation data, the U.S. Bureau of Labor Statistics (mildly frustrating to use) to get quarterly employment data, and the U.S. Bureau of the Census (apoplectically frustrating to use) to get educational attainment data? A section on data portals for free public data would have been well advised. Most EDPs in the field cannot afford data vendors that not only consolidate public data but also provide proprietary data.
But wait! There’s more! The book is more than just a manual on socioeconomic data and a presentation of analytical techniques. From both a practitioner’s point of view, as well as a researcher’s, Quinterno’s discussion on theories of regional development (Chapter 3) help place the data and policy options in a context. Analytical frameworks (or models) structure the data analysis and, often, dictate “the answers.” While any discussion of theoretical models may be off-putting for the average EDP, understanding how the model used for analysis drives the policies proposed can help an EDP understand why certain policy prescriptions are in vogue at a certain point in time, as well as among circles of policy makers and consultants.
While Quinterno is to be praised for providing an overview of theoretical models and how those models shape analysis and policy, one could not help but detect a slight hostility to the approach of neoclassical economics. True, the neoclassical model and explanations for why some regions fail and others prosper falls short, but so do all the other models. The theoretical models are not settled science; unfortunately, they are not much more than shots in the regional dark. A tenet of neoclassical economics is that markets clear. Prices and costs adjust so that there is no unmet demand or excess supply. The upshot for regional development is that low-cost production areas should attract capital and other necessary factors of production, thus raising incomes and growing jobs. But an economist worth her salt will also ask “Where is the market failure?” 1 And once that market failure is identified, one must be fair and realistic about effectiveness and duration of policy interventions. Changing the business climate by, say, lowering corporate taxes is relatively easy and quick to enact, even if the effectiveness may be suspect. Educating a generation of children to prepare them for the more intellectually demanding work of the future takes decades, and, when it has run its course, one may find that a state like Indiana may graduate many top-shelf engineers only to enrich the labor force of the west or east coasts.
The bias notwithstanding, one wishes that Quinterno had developed the theory and analytical framework more, and provided more examples of the theory in action. In this way, EDPs would be better equipped to understand where the policy flavor-of-the-month comes from and form a basis to challenge the fly-in-and-out consultants and academics selling that flavor and drafting strategic development plans based on that flavor.
A chapter highlighting success stories and flops, and how each one used data (or not) and the policies pursued would have been instructive. Linking data, analysis, and strategy formation is the true challenge. Showing how data drive strategies and policies would have made this book a true gem for the average EDP. While developing the “Seven Habits” of highly successful EDPs in the final chapter is helpful, comparing and contrasting vibrant cities, even cities of many contrasts like Greenville, South Carolina, and Burlington, Vermont, 2 would make the purpose of the data and analysis more concrete.
But until then, we will continue to use this book as a solid data and analysis reference.