Economic statistics, like all data, needs to be defined. The way to collect data (economic data or any other type) is to operationally define the terms. Statistics don’t lie. Statistics can be faulty, when those collecting the data fail to use good operational definitions and the data quality is poor (without a definition people make guess…). People can also just make up false number. And people can try to mislead by stating statistics in a way that seem to indicate something that is not the most accurate way to view the whole situation.
The way to cope with such problems is to understand statistics and data. The data can be wrong. So you have to access that possibility. And the data can mean something different than you assume (and often the data is not presented with the operation definitions). When that is the case be careful about your assumptions (with financial and economic data and other data too). But don’t decide to just ignore data because then you condemn yourself to ignorance of the many things which data shed light onto.
In, What ‘Unemployment’ Really Means These Days, the unemployment data is explored. The post does a good job of showing how you can get different measures of the “unemployment rate” depending on how you define what you will measure. I happen to believe the existing measure is best but you need to understand that it doesn’t factor in underemployment and people giving up completely… I believe the best way to deal with those weaknesses is to have supplementary measures that enhance your understanding of the unemployment rate. And too view it as only one measure of economic health. Look also at median wages, health care coverage, hours worked, vacation time…
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