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The famous fictional detective Sherlock Holmes declared, “It is a grave mistake to formulate a theory before you have the data.” What is true when making criminal deductions is also true when making policy. But problems arise when the data is not completely reliable to begin with. The Bank of England is well aware of this. The Monetary Policy Committee kept interest rates on hold at 5.25% at its meeting on Thursday, citing increased uncertainty in labor market data, a key determinant of inflation, making it unlikely to make a decision during this cycle of hikes. It is said that
Britain’s Office for National Statistics was forced to release patchy experimental employment data last month after low response rates to the Labor Force Survey increased the risk of bias. That’s down from 50 percent 10 years ago to 15 percent today. This study is also based on old population estimates. The employment numbers issue also follows significant revisions to UK gross domestic product data by the ONS. These updates make it clear that the economy is no longer likely to become an outlier in the G7 with the worst post-pandemic recovery.
Bad statistics lead to bad decisions. It is extremely difficult for central bankers to set interest rates and make economic forecasts during times of high uncertainty, let alone when the underlying data is weak. It’s also important for the government. Jeremy Hunt, the Chancellor of the Exchequer, announced measures to tackle the rise in worker inactivity in the UK in his Spring Budget. The LFS issue currently raises questions about the nature and scope of the problem. Companies can also lose money by making decisions based on flawed statistics.
Trust in official data is essential to public debate. Depending on the sources you use, you can also form different points of view. Unreliable statistics also undermine trust in expertise.
Despite its bad reputation, ONS remains a respected international authority on data. The pandemic and declining survey response rates are challenging all statistical agencies. In fact, the ONS was considered a world leader in tracking Covid-19. But it is precisely because of the essential role it plays in analysis and policy-making, and precisely because more and faster data is needed, that improvements are essential.
First, ONS needs to know how to set priorities and allocate resources. On Thursday, the company highlighted its continued efforts to improve LFS data collection and methodology. It plans to transition to the Transformed Workforce Survey early next year. Nevertheless, given the decade-long decline in response rates, we need to understand why survey reform has been slow.
Second, existing efforts to improve data need to be accelerated. This includes exploring new ways to incentivize respondents and take advantage of modern technology. We just moved to our first online survey with TLFS. More diverse real-time data sources can also support GDP and labor market modeling, especially when research is lacking or other data lags. This requires greater cooperation from government agencies to share more data faster to avoid major revisions like GDP.
Third, agencies must attract the best talent. Data scientists are often attracted to high-paying private sector roles in London, far from ONS headquarters in south Wales. And finally, we need to improve our communication. It would be helpful to show the confidence bands around the statistics more clearly.
ONS has already addressed many of these. However, it needs further support from governments to maintain its international standing. It has already proven to be efficient even on a limited budget. Therefore, the pressure on it needs to be reconsidered. After all, when important economic decisions are at stake, the benefits of better data can be invaluable.
Letter of response to this editorial:
Labor statistics were designed for the era of a closed economy / Guy Standing, Professor and Researcher, Soas, University of London, London WC1, UK
