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Anyone who has studied “Econ 101” will know that most economic models assume that decision makers have perfect information. In reality, this is rarely the case. The data may be incomplete, one party may have more details than the other, or everyone may be equally misinformed. In fact, official national statistics, which are vital for policymakers, businesses and investors, can sometimes miss the mark. At this time, many countries are having particular difficulties in counting the number of employed people.
The Resolution Foundation expert group recently He claimed Britain’s Office for National Statistics may have lost almost a million workers in its employment figures since 2019. He believes the statistics body may have vastly overestimated the extent of worker inactivity. If true, it would undermine the “missing workers” narrative that has been the basis of several UK government policy promises, as well as its central bank’s interest rate decisions.
there is a puzzle in the United States too. Job creation has stagnated in the last two years, according to its household survey. However, nonfarm payrolls, compiled from a survey of businesses, show relentless employment growth. The data has also been volatile and subject to significant revisions. in August The Labor Department said the U.S. economy created more than 800,000 fewer jobs in the NFP than initially reported in the year through March.
One reason for the uncertainty may be that over the past decade, response rates to data questionnaires in the UK, US and EU have been trending downward, exacerbated by the pandemic. Lockdowns have also altered projections for population, immigration, and business creation and death, which help statisticians aggregate employment data from survey samples. This has led to biases, poor estimates, and conflicting accounts from different data sources.
Counting workers is also a problem in the developing worldalthough for more systemic reasons. Estimating India’s unemployment has long been a challenge given that a significant portion works in the informal sector. In China, opacity is another limitation.
Bad employment data leads to bad decisions. Employment figures underpin tax, spending and social welfare decisions, and are central to monetary policymakers’ assessments of how hot the economy is. Companies use it to make salary and hiring decisions. Investors are also relying on it. US NFP figures drive interest rate pricing in global financial markets.
What can be done? Governments must ensure funding keeps pace with demand for bigger, more timely and more accurate data. In June, The U.S. Bureau of Labor Statistics said budget cuts may mean it has to cut the sample size of its household survey. Data experts are also being snapped up by higher-paying tech companies. Authorities should also do a better job of holding statisticians accountable. The ONS has been particularly slow to act descending responsesand move on to online surveys.
Even with incentives or better survey design, declining response rates can be difficult to reverse. Some studies They suggest that people are fatigued by too many questionnaires. Either way, it will be important to capture labor market data through other sources. Statistical agencies should partner more with the private sector (including job boards like Indeed and LinkedIn) to obtain real-time statistics to support their estimates. Governments also need to share more timely administrative figures with data agencies. In some countries, national ID cards have helped agencies better understand population and workforce data.
National statistical agencies must improve their accounting of employment figures. More resources, effective oversight, and broader access to other data sources will not make the numbers perfect. But at least it will give a greater idea of how imperfect they really are.