Communicating uncertainty in GDP estimates

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Communicating uncertainty in GDP estimates

The UK economy has been affected by some unprecedented events in recent years, with the effects of the COVID-19 pandemic followed immediately by the huge rise in energy costs due to the war in the Ukraine. For the ONS, these have increased the challenge of producing economic statistics that are both fast and accurate. Dramatic changes in the underlying data tend to bring added uncertainty to our initial estimates. As Craig McLaren explains, we are exploring ways to better portray this in our statistical outputs.   

Excess deaths – a new methodology and better understanding

The COVID-19 pandemic heightened interest in patterns and levels of ‘excess’ deaths, typically defined as deaths over the number that might be expected to occur in an ‘average’ year. But with different organisations using different methods to calculate excess deaths, it can be difficult to build a clear picture. Working across government and the devolved nations, we have now agreed a common UK-wide approach to producing national estimates of excess mortality. As Julie Stanborough explains, this new methodology will give us a better understanding in this complex area.

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How the ONS is transforming its statistics for local areas

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The ONS produces a huge amount of high-quality data covering the economy, environment, crime and immigration. Over the last few years, we’ve also been focusing our efforts on producing much more granular estimates so we can measure how our society and economy is changing at the local level. Emma Hickman writes about how this work is progressing and what insights we’ve produced. 

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Understanding our future population: Why projections are not predictions

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There are many reasons for wanting to know the size of the future population of the UK. How many school places will we need? How many hospitals? How many people will claim a State Pension? But looking into the future is challenging. As James Robards explains, our population projections take into account current and past trends, and as those patterns change, we adjust our projections accordingly.

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