Earlier in October, the Bureau of Labor Statistics reported that consumer prices rose 3.7 percent over the past year, a little higher than the month before, with gasoline accounting for more than half of September's increase. Rising prices are the kind of news no incumbent politician wants weeks before an election. The report was published anyway, on schedule. That is the quiet discipline of a trustworthy statistical system. It tells us what is true even when the number is unwelcome to whoever holds power.
So, do Americans trust government data? We do. But that trust is incomplete, and it has room to grow.
In a national survey this summer, nearly three in four adults agreed that federal statistics are valuable and needed for policymakers to make good decisions. At the same time, only about half said they tend to trust those statistics. That gap between perceived value and trust is significant, which the nonpartisan Data Foundation documented in a report this month and is worth digging into. Americans are not rejecting the value of federal statistics at all. Instead, many are simply unsure whether to believe the statistics. Our national statistics are a portrait of our economy, our communities, and our people. They should also tell us about ourselves. The question is why so many of us do not yet see ourselves in those statistics.
Part of the answer may be that averages are a poor guide to any one life. A national inflation rate is a single number standing in for millions of households with very different spending, spread across dozens of metro areas, each with its own mix of rent, groceries, and gas. Inflation is accurate, and it may also bear little resemblance to your receipt. Over the year ending in September, prices rose about 4.1 percent in the Washington, D.C., metro area but just 1.9 percent around Tampa. In September alone, ground beef and milk got cheaper while gasoline got more expensive. Whether the national number feels true to any person depends on where and how they live. When the headline average does not match the life a person is living, no wonder the number can feel meaningless.
That doubt matters most this fall because the economy is the top issue for voters, according to the Pew Research Center. Economic statistics are therefore under the most pressure to tell a convenient story. So look at the places where that pressure is highest. We examined Senate battleground states, where any hypothetical thumb-on-the-scale would pay off most. In September, unemployment was 3.3 percent in Maine and 3.4 percent in Ohio, below the then national rate of 4.2 percent, while Texas sat at 4.4 percent and Michigan at 5.0 percent, above it. That is not the uniformly favorable story a campaign would want. What keeps these figures honest is process: established methods, published release schedules, and transparent revisions. The next round of state-level figures arrives October 20th. Officials may see them in advance under embargo, but no one can alter, suppress, or delay them.
Here is the part we most want to say plainly: It is not the American people's obligation to trust their government or government data. The government has to earn that trust, and keep earning it. The long technical documentation behind every official number is necessary because it is how independence is proven and credibility is built. But documentation is far from sufficient. People have to be able to see themselves in the statistics their government produces. If anything, the answer runs the other way: our statistical agencies may need to produce even more relevant statistics about our economy, our people, and our society, not fewer. Statistics that are more representative, higher in quality, easier to reach, and easier to understand are how public statistics stay relevant, trusted, and used.
One of us has spent a career advocating for national efforts on data quality and trustworthiness, so the data infrastructure keeps pace with the demands of modern society. The other has spent a career inside the government, as a federal chief data officer at the Office of Personnel Management and as chief data scientist at the Government Accountability Office, building the systems that produce context-relevant and rigorously validated numbers. From both vantage points the lesson is the same: data earns trust when it stands apart from the politics of the moment, and when people can find themselves in it.
The United States has already pointed itself in this direction. Alongside democracies around the world, it joined the Luxembourg Declaration affirming the value of data and evidence. The next step is to formally measure not only whether people trust the government, but why. U.S. participation in the OECD's international survey on the drivers of trust would do exactly that, connecting headline numbers to the things that actually move trust: reliability, fairness, responsiveness, and whether decisions draw on the best available evidence.
To date, Republican and Democratic administrations alike have not participated in the OECD's international survey, despite the reality that while America is exceptional in many ways, low trust in government is not unique to America. And if official statistics matter this much, the agencies and data officials who produce them need secure, sustained funding: enough to produce, manage, and protect this information, and to keep it accessible to people and machines alike as more of public life runs on data—even if the numbers are politically inconvenient.
This month's inflation numbers were not flattering for many political campaigns. They arrived anyway, measured the same way everywhere, released on a published schedule for Principal Federal Economic Indicators. That is independence in practice. Trust will not come from telling Americans to believe the numbers; trust comes when the numbers are clear enough, and close enough to people's lives, that people can see themselves in them.
Nick Hart is President and CEO of the nonpartisan Data Foundation. He is a fellow of the National Academy of Public Administration and the Bipartisan Policy Center.
Taka Ariga is Vice President for Data Policy and Impact at the Data Foundation. He is a former Chief Data Scientist at the Government Accountability Office.
