With discretionary budget cuts in the works, the ability of government statistical agencies to perform their duties may be in jeopardy. At a relatively small cost — only about one-fifth of 1 percent of the federal budget — the government statistical agencies do important work that is valuable to business, policymakers, and families. Cutting their budgets would be penny wise but pound foolish. It could very well cost the economy more money than it saves by hurting the ability of businesses and government to make critical decisions that affect all our lives, and our pocketbooks.
Businesses use government data every day to make critical decisions. “Businesses, such as Kroger, rely on third-party proprietary models that use [American Community Survey] small area estimates to project sales for potential new grocery stores sites or remodeling of existing stores,” according to a 2015 report by the U.S. Department of Commerce. “Retailers, such as Target, use the ACS for neighborhood-level demographic data such as population density, owner-occupancy, and household size to determine the optimal mix of goods with which to stock its stores throughout the country,” according to the same report. Charles Schwab cites International Energy Agency (IEA) data in a 2017 report on the market outlook for the energy sector.
There are many more examples. Should you open a business in Dallas? You might want to know information about the education and demographic characteristics of Dallas’ labor market and customer base. For that, turn to official government data. Retailers use official government data to benchmark wages and benefits, monitor consumer expenditure patterns, and determine when and where to open stores and distribution centers. Potential entrepreneurs use official government data when deciding to take the plunge and start a business. Firms use official statistics to forecast future demand, customer preferences, and to help with planning and management. Official statistics on employment, prices, and gross domestic product (GDP) are monitored closely by both individual businesses and the market as a whole.
Although business decisions rely heavily on official government data, the importance of data to business seems less understood than it should be. To help remedy that, we are co-authors on a report on the vital role of official government data.
America’s desire to collect data for the common good dates back to the founding fathers, when James Madison argued that reliable data on agricultural, commercial and manufacturing interests would allow Congress to represent the interests of its citizens more effectively. Hard numbers would be useful to Congressional debaters “in order that they might rest their arguments on facts, instead of assertions and conjectures.” Indeed, collecting data is a specifically enumerated requirement of government in Article 1 of the U.S. Constitution.
We’ve come a long way since the 19th century. Today, the modern economy is especially reliant on data, and in this era of “big data,” businesses collect and analyze vast quantities of their own internal data to forecast sales, predict staffing and inventory needs, and weigh all sorts of decisions. The big data revolution is rightly celebrated as a great social and economic achievement. But a firm’s own data are not adequate to serve society’s larger purposes. Instead, private-sector data should be thought of as a fantastic complement to official statistics.
Official government data are structured in important ways that private-sector data are not. Official statistics are comprehensive, as their objective is to create an accurate set of facts about the U.S. economy and society as a whole. They are consistent over time, with many data series spanning decades, allowing careful comparisons across place and over time, and the calculation of trends. Official statistics are free to the user, whereas private businesses often have an incentive not to share their data with the general public. And members of the public are much more willing to respond to direct requests for information from the government, with its strict confidentiality standards for data, than to a private firm.
Official statistics are also very credible. Statistical agencies have a refined and codified set of practices that ensure the quality and impartiality of the data. The data are shielded from political influence. For example, the Bureau of Labor Statistics, which produces statistics on employment and unemployment, has only one political appointee. The rest are dedicated career civil servants. Helping to maintain that credibility is that many data elements are carefully scrutinized by outsiders, whether the market — which reacts to certain regular data releases as important measures of economic health — or researchers.
Adding to that credibility is the high priority for data confidentiality. Misuse of the data is closely monitored, with fines and prison terms for unauthorized access or use. The government is not allowed to use any of the statistical data it collects to help bring criminal charges against a respondent. The data are anonymized using complicated methods, making a breach of privacy very unlikely. And the culture of the statistical agencies is permeated with a deep understanding of the importance of maintaining the confidentiality of respondents. Protecting the confidentiality of respondents is always a concern, but the current safeguards are working.
Federal data are also of great use to policymakers. Social Security checks are inflated using the Consumer Price Index (CPI). If the CPI is off by even a little bit, billions of your tax dollars will be misallocated. It is important, therefore, for the CPI to be accurate. Information from the ACS helps determine how more than $400 billion in government funds are distributed each year. The Federal Reserve uses labor market, price, and other macroeconomic data to make decisions that impact the interest rate on your next car or home purchase. If those data are flawed, you could pay more for your house. Families use the data, too, especially around major life events such as choosing a college and field of study, and around minor life events, like using weather data to decide what to wear for a Saturday outing.
To be sure, there are important shortcomings of federal data collection. We should strengthen the data collection by addressing limitations. For example, it is a major problem that we do not collect systematic evidence on the share of the population with criminal records, and we also do a poor job of collecting information on the gig economy. New questions should be added to learn about the changing economy. Of great concern is declining participation in surveys, and increasing rates of refusal to answer questions and underreporting of certain behaviors.
Proposals to augment survey data with administrative data, with appropriate confidentiality safeguards, would likely improve the quality of data in a cost-effective manner. Agencies should build off the success of Census’ Longitudinal Employer-Household Dynamics program and continue finding ways to link different data sources together. “Data synchronization” should finally pass Congress and be enacted.
Improving the data requires resources. The return on this investment is significant — to businesses, policymakers, and families. It’s an investment worth making.
Diane W. Schanzenbach, Ph.D., is director of the Hamilton Project and a senior fellow at the Brookings Institution. She is also a research associate at the National Bureau of Economic Research. She is currently on leave from her position as a professor in the School of Education and Social Policy at Northwestern University.
Michael R. Strain, Ph.D., is director of economic policy studies and a resident scholar at the American Enterprise Institute. He previously worked in the Center for Economic Studies at the U.S. Census Bureau and in the macroeconomics research group at the Federal Reserve Bank of New York.
The views expressed by contributors are their own and are not the views of The Hill.