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Technology, Data Collection, and Analysis Association of Private Enterprise Education April 6-8, 2008

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Most people understand that data can be mis- represented via visual sleight-of-hand.

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Similar misrepresentation occurs when not enough data is collected, the wrong type of data is collected, or the data is aggregated.

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Lesson #1: A single observation is meaningless. Corollary:An anecdote is both meaningless and dangerous.

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On January 25, 1994, Bill Clinton gave his first State of the Union Address. The next day, the Dow-Jones Industrial Average rose. Pundits took this as evidence of the market’s approval of policies Clinton outlines in the Address.

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A single data point contains no meaning.

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A mean is what you get when you collect a bunch of individual data points. Lesson #2: A mean is meaningless. Corollary:A mean is dangerous because obtaining it involves simple math and people trust math they can do.

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If a single data point is meaningless, then comparing a single data point to a mean is meaninglessness wrapped in the illusion of meaning.

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Comparing a single observation to a time series reveals information because a time series reveals variance. Lesson #3: A variance is meaningful. Corollary:A variance is dangerous because obtaining it involves complicated math and people don’t trust math they can’t do.

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Variance over time reveals the significance of a single observation.

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Comparing a single observation to a cross-section reveals information because a cross-section reveals variance. Lesson #4: Variance can be revealed both in time series and in cross-sectional data.

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Comparing a single observation to both a time series and a cross-section reveals a lot of information because the two dimensions give different information on variance Panel data. Lesson #5: Panel data is extremely meaningful. Corollary:If you thought variances were dangerous, panel data is downright witchcraft.

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Variance over time reveals significance relative to the past. Variance over cross-section reveals significance relative to others.

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Lesson #6: A time series data with few observations is as meaningless as a single observation. This is too complicated. Why not just use time series?

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A comparison of two points in time reveals that greater trade is associated with greater unemployment.

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The fact that trade reduces unemployment is only revealed after examining many observations.

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Lesson #7:Even with many observations, time does not cure all ills.

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Twenty years’ worth of data reveal a positive relationship between government spending and the HDI. Mali

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Austria Twenty years’ worth of data reveal a positive relationship between government spending and the HDI.

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Recall Lesson #1: A single observation is meaningless. If a single observation is meaningless, then perhaps so too is a single time series. Let’s look at the average time series across countries…

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Mean Over All Countries The apparent relationship between HDI and the size of government is seen in a different light after examining many time series.

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Recall Lesson #2: A mean is meaningless. How are all the individual countries behaving?

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We get more information from looking at many individual countries than from looking at means. Standard Errors of Means Over All Countries

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Panel data does not lend itself well to graphing. But, panel data contains rich information that is found in neither time series nor cross-sectional data. Econometric techniques can extract that data.

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Panel data enables us to filter out noise that occurs across time and across countries to see underlying relationships. Government Spending that Maximizes HDI

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Panel data can be visualized, but doing so requires animation. gapminder.org

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Moral of the Story Data yields the greatest information when the data is: Disaggregated reporting averages hides information Time series reporting a snapshot hides trends Cross-sectionalreporting one instance of a time series hides atypical trends For discerning truth from noise, disaggregated panel data is the tool of choice.

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