Pearson Correlation: From Intuition to Precision

Imagine you're tracking two things over time — say, the number of ice creams sold at your school canteen and the outside temperature. On hot days, both go up; on cool days, both drop. They seem to move together. That's the basic idea of correlation: a measure of how two variables move in relation to each other.

But not all co-movement is equal. Sometimes one variable goes up while the other goes down — like the price of a good and the quantity demanded (law of demand). Sometimes they seem to have no connection at all — like the number of students in your class and the price of tea in China. Pearson correlation gives us a single number that captures the strength and direction of this linear relationship.

The Precise Meaning

Pearson correlation coefficient, usually denoted by $r$, measures the linear relationship between two variables $X$ and $Y$. It answers: If I know how far $X$ is from its average, how far (and in which direction) is $Y$ from its average, on average?

The formula is:

Where:

• $X_i$, $Y_i$ are individual observations
• $\bar{X}$, $\bar{Y}$ are the means (averages) of $X$ and $Y$
• $\sum$ means "sum over all observations"

The numerator is the covariance — it tells you whether deviations from the mean tend to be in the same direction (positive product) or opposite directions (negative product). The denominator is the product of the standard deviations of $X$ and $Y$, which scales the result so that $r$ always lies between $-1$ and $+1$.

Why It Matters in Economics

Economics is full of pairs of variables that we suspect move together. Pearson correlation gives us a first, clean check on whether that suspicion holds water.

Example 1: Consumption and Income. Keynes said consumption depends on income. If you plot household consumption against household income for a sample of families, you'd expect a positive $r$ — higher income families tend to consume more. A value close to $+0.8$ or $+0.9$ would be strong evidence for that relationship.

Example 2: Price and Quantity Demanded. The law of demand says price and quantity demanded move in opposite directions. A negative $r$ between price and quantity (holding other factors constant) would confirm this. But here's the catch — in real market data, price and quantity are determined simultaneously by supply and demand, so a simple correlation might not show the expected negative sign. That's why economists use more advanced tools (like regression) to isolate the relationship.

Example 3: Investment and Interest Rates. You'd expect a negative correlation — when interest rates are high, borrowing is expensive, so investment falls. But the relationship might be weak ($r$ close to 0) because investment also depends on expectations, technology, and government policy.

Visualising It

Draw a scatter plot with $X$ on the horizontal axis and $Y$ on the vertical axis. If the points cluster around a straight line sloping upward, $r$ is positive and strong. If they cluster around a line sloping downward, $r$ is negative and strong. If they form a shapeless cloud, $r$ is near zero.

But here's the nuance: a perfect circle of points has $r = 0$ even though $X$ and $Y$ are clearly related (non-linearly). Pearson correlation only captures linear relationships. Two variables could be perfectly related by a U-shaped curve and still have $r = 0$.

A Quick Worked Example

Suppose you have data on five families:

Mean income $\bar{X} = 30$, mean consumption $\bar{Y} = 22.6$.

Compute deviations and products:

• For family A: $(10-30)(8-22.6) = (-20)(-14.6) = 292$
• For family B: $(20-30)(15-22.6) = (-10)(-7.6) = 76$
• For family C: $(30-30)(22-22.6) = (0)(-0.6) = 0$
• For family D: $(40-30)(30-22.6) = (10)(7.4) = 74$
• For family E: $(50-30)(38-22.6) = (20)(15.4) = 308$

Sum of products = $292 + 76 + 0 + 74 + 308 = 750$

Sum of squared deviations for income: $(-20)^2 + (-10)^2 + 0^2 + 10^2 + 20^2 = 400 + 100 + 0 + 100 + 400 = 1000$

Sum of squared deviations for consumption: $(-14.6)^2 + (-7.6)^2 + (-0.6)^2 + 7.4^2 + 15.4^2 = 213.16 + 57.76 + 0.36 + 54.76 + 237.16 = 563.2$

That's almost perfect positive correlation — consumption rises almost exactly linearly with income in this small sample.

The Bottom Line

Pearson correlation is your first tool for spotting linear relationships in economic data. It's simple, intuitive, and powerful — but limited. Use it to check whether two variables move together, but never to claim that one causes the other. In economics, where everything is connected to everything else, that caution is worth its weight in gold.