Cross variables · Atlas Abierto

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Provinces compared: 52
Spearman correlation (ρ): -0.92
Pearson correlation (r): -0.84
No data: 0

Sources: Mean net income per person · Population below 60% of the median (from 2022)

Show all values

Place	Mean net income per person · 2024 (€)	Population below 60% of the median (from 2022) · 2024 (%)
Araba/Álava	18,742	14.6
Albacete	13,873	23.7
Alicante/Alacant	13,025	29.4
Almería	12,244	31
Ávila	14,402	24.7
Badajoz	12,924	26.4
Illes Balears	17,158	14.7
Barcelona	17,884	16
Burgos	16,879	15.4
Cáceres	13,614	24.8
Cádiz	13,077	27.1
Castellón/Castelló	14,693	21.9
Ciudad Real	13,408	25.9
Córdoba	13,337	23.9
Coruña, A	16,133	16.7
Cuenca	13,624	25.3
Girona	15,986	18.7
Granada	13,509	27.8
Guadalajara	15,467	17.4
Gipuzkoa	20,790	10.2
Huelva	12,765	25.7
Huesca	16,090	16.2
Jaén	12,835	26.6
León	16,041	17.7
Lleida	15,608	19.8
Rioja, La	15,830	19
Lugo	15,089	19.3
Madrid	19,277	16.2
Málaga	13,737	26.1
Murcia	13,218	25.8
Navarra	17,701	15.8
Ourense	14,865	22.1
Asturias	16,646	17.3
Palencia	16,095	16.5
Palmas, Las	14,794	21.2
Pontevedra	15,002	18
Salamanca	15,458	20.5
Santa Cruz de Tenerife	14,286	23.4
Cantabria	16,068	16.9
Segovia	15,841	18.1
Sevilla	13,827	24.1
Soria	16,589	16
Tarragona	15,296	21.2
Teruel	15,364	18.5
Toledo	13,208	25.2
Valencia/València	15,180	21
Valladolid	16,620	15.8
Bizkaia	19,858	13.1
Zamora	14,861	21.7
Zaragoza	16,551	16.1
Ceuta	15,520	32
Melilla	14,655	33.7

Study report

This study measures whether mean net income per person is related to population below 60% of the median (from 2022) across 52 provinces in Spain.

According to the results, the data firmly support a negative relationship: where mean net income per person is higher, population below 60% of the median (from 2022) tends to be lower (ρ = -0.92). It's very unlikely to be chance (p < 0.001).

Roughly, the two variables share 84% of their variation (ρ²). The rest depends on other factors.

The result describes provinces, not people.

Two variables moving together doesn't show that one causes the other: a third factor may be behind both, and proving a cause needs a different kind of study.

What it means

Correlation

Measures whether two variables rise and fall together. It runs from −1 (one rises as the other falls) to +1 (they rise together). Near 0 there is no ordered relationship.

Spearman (ρ)

Correlation computed on the places' order rather than their values. It copes well with extreme values and curved relationships. It's the one used for the reading.

Pearson (r)

Correlation computed on the values. It measures whether points line up on a straight line. One very extreme place can move it a lot.

p-value

The probability of seeing a correlation this large if there were really no relationship. Below 0.05 it's usually called significant. It doesn't say whether the relationship matters.

n (places compared)

How many places have data on both variables. With few, the result is unstable; with many, almost any difference is significant.

Ecological fallacy

A correlation between provinces or municipalities says nothing about individuals. Richer municipalities having more older residents doesn't mean older people are richer.

Trend line

The straight line that best fits the points (least squares). It shows the overall direction, not a prediction for each place.

Rank chart

Replaces each value with its position (1st, 2nd…). It shows what Spearman measures, and extreme values don't dominate.

Cross two variables