 Aggregator macro data are data about populations, groups, regions or countries. Examples of aggregate indicators are average life expectancy, employment rates, GDP, green gas emissions and census headcounts. What all these have in common is that they are produced by combining data from individual units into single counts. So in this example, data from reading performance scores from individual girls and boys has been combined to produce average scores by gender for each country. So here girls are represented by the circles and boys by diamonds. Usually aggregate data are data with a geographic unit like a country or a census ward. Aggregate data about people are either counts or have been averaged, totaled or otherwise derived from the individual level data found in surveys or census returns. So for example, we have a total headcount of everyone in Carlisle in the 16 to 24 age group who defined themselves as Muslim in the 2011 census. This is an aggregate count as it is produced as a total from the individual census returns. Country or regional level aggregate data are often time series data where the measurements are taken at regular intervals and ordered by time. Time series data are periodicity, the frequency at which they are recorded. This could be every year, quarter or month. Time series data are often presented as tables with time running across the horizontal axis. For example, here we see immunization rates reported as annual time series data for countries around the world. Aggregate data are either simple counts such as counting ballot papers in an election or derived from micro level survey data sets by applying statistical methods such as summing or averaging, then applying additional calculations such as weighting and estimations of sampling errors. These procedures are designed to provide reliable inferences about an entire population based on data collected from the set of samples surveyed. For example, unemployment is one of the most closely watched measures of a nation's economy, but how is it calculated? The standard measure of unemployment is the unemployment rate which measures the number of people who are unemployed as a proportion of the number of people who are economically active. This unemployment rate is derived from survey data. In the UK, the Office for National Statistics uses the labour force survey as a basis for calculating the unemployment rate. The labour force survey is conducted every three months and interviews around 101,000 people over the age of 16. Each respondent is classified as either unemployment, unemployed or economically inactive. The unemployment rate is then derived from the responses from this sample. So the unemployment rate is an example of how an aggregate indicator is produced from a survey. However, basing unemployment rate on a survey raises several methodological issues. Firstly, although the labour force survey covers a large representative sample, most absolutely everybody aged over 16 in the UK are included in the survey. This means a different sample would produce a slightly different result. The spread of results from different samples is known as the sampling variability. The sampling variability places a confidence interval in the final unemployment rate produced by the survey. Secondly, as it is based on a survey, the unemployment rate also needs to be adjusted to reflect the population as a whole, perhaps by adjusting the results for a particular local area by age group and gender. A second measure of unemployment is claimant count. This differs from a survey as it is a head count of everybody claiming job seekers allowance and national insurance credits. Since it is a 100% count, the claimant count is unaffected by sampling variability and so can be used as an indicator of those without work at very small levels of geography. However, if we look at claimant count and the unemployment rate together, we see claimant count is consistently lower than the unemployment rate. This is because not everyone who is unemployed claims benefits. As claimant count only measures people claiming benefit, the unemployment rate is considered to be a more accurate measure of actual unemployment as is the standard indicator. In fact, it is a legal requirement for every country in the EU to produce a labour force survey and your results on the OECD then use these surveys to publish a monthly rate of unemployment for each of their member countries. So we have seen that aggregate data, a data that has been derived from individual level data, which are pulled together so larger patterns and trends can be seen. Aggregate data are often data about different groups of people or regions collected for administrative purposes by central banks and national statistical offices. Local and central government departments use aggregate data to identify how and where they should be using public resources. They use the information to check how different groups in the community are affected by existing policies and to inform their future policy changes.