But in the decades to come, as we try to understand whether the American dream is deceased or merely slumbering, our statistics could get so much better. Because they rely on fixed definitions — created decades ago — of the phenomena they’re charged with measuring, they do a poor job of capturing the ways in which people’s economic lives are changing. The statistics are all but useless at measuring the change in general welfare created by new technologies, like Google Search, that make once tiresome tasks far easier to complete (at the cost of adding a whole universe of time-wasting distractions). In measuring employment, the stats are built around a model of full-time, fixed jobs in fixed locations; they struggle to keep track of Uber-like companies that employ people for brief gigs with no central workplace. Entrepreneurship, too, is measured quite crudely: It’s impossible, just looking at the new-firm stats, to distinguish the creation of Facebook from the opening of a small deli in Dubuque. Definitions of occupations are rigid and often archaic. Manufacturing work, for example, is broken down into dozens of discrete subcategories — tool grinders, sewing-machine operators, tire builders, woodworking-machine setters — while all ‘‘software developers’’ and ‘‘web developers’’ are organized into single catchall groups. You could learn a lot about the wages and employment patterns of adhesive-bonding-machine operators (18,210 workers in 2014, median wage $16.28 per hour). But you’d be hard-pressed to find any guidance on whether you’d make more money learning the Ruby on Rails computer-programming framework instead of developing your graphic-design skills.

Most of our economic statistics date to World War II. When Franklin D. Roosevelt first assumed the presidency, he asked his advisers how the economy was doing, and they responded with anecdotes: rail cars running half empty in Chicago, say, or department stores lagging in customers. Other than inflation, which had been measured since 1913, there were no reliable ways to judge economic strength over time. Spurred by F.D.R., the federal government sought to quantify the economy, creating a dozen different offices of economists and statisticians in the Departments of Labor, Commerce and Agriculture; the Federal Reserve; and several other agencies. The politicians wanted simple, straightforward answers to their economic questions. But the economists knew their answers were inherently subjective. They debated what the statistics should measure — arguments that weren’t resolvable with data because the data expressed the values and assumptions used in gathering them.

The most important thinker in these debates was Simon Kuznets, an immigrant from Russia. He was an economist at the National Bureau of Economic Research, an independent nonprofit that has served, since 1920, as the semiofficial host of fundamental discussion about the best way to measure an economy. Kuznets believed that economic statistics should be an essential part of a democracy: that they could hold our leaders accountable, by demonstrating whether the government was making life better or worse. Kuznets argued — as, incidentally, most economists do — that measures like employment and inflation are substitutes for what really matters: our quality of life, as each person defines it for himself. We measure money and other practical things because we don’t know how to measure happiness or fulfillment precisely.