Minimum Wage and Wage Distribution: Germany's 2026 Data

Germany raised its statutory minimum wage to €13.90 per hour on 1 January 2026, and the Federal Statistical Office (Destatis) published estimates of how many jobs were sitting below that line beforehand. It is a good case to work through, partly because Destatis published two different estimates for the same policy change, and explained why they differ.

What a wage floor does to the shape

Four things happen at once, and it helps to separate them.

The left tail is truncated. Wages below the floor stop existing as a legal category. In a compliant labour market, the density to the left of the threshold goes to zero.

A mass point appears at the floor itself. Every job that previously paid less is now paying exactly the minimum. A continuous variable suddenly has a discrete lump in it, which is why kernel density plots of hourly pay near a minimum wage look so strange the first time you draw one.

There is usually some spillover just above the floor. Employers who paid slightly above the old minimum often lift those wages too, to preserve internal pay hierarchies. The effect fades as you move right, and how far it reaches is an empirical question rather than something you can assume.

The upper half does not move. Which means the mean shifts up slightly, the variance falls, and the median may not budge at all. If your dataset is dominated by workers well above the floor, a minimum wage rise that genuinely changed millions of paychecks can leave the median untouched. That gap between what the mean says and what the median says is the whole reason you report both.

Germany, January 2026

The increase took the floor from €12.82 to €13.90 per hour. For a 40-hour week, that works out to roughly €2,409 gross per month at the standard conversion of 4.333 weeks per month. If you are checking figures like this against German contract hours, a stundenlohnrechner handles the hourly-to-monthly step directly.

Based on its April 2025 earnings survey (Verdiensterhebung), Destatis estimated that up to 4.8 million jobs were paid below €13.90, which is roughly 12 percent of all employment relationships, or close to one in eight. Paying those jobs at the new rate raises the total wage sum for the affected group by up to 6 percent, around €275 million per month.

The effect is concentrated, not spread evenly. In hospitality, 47 percent of jobs sat below the new floor. Agriculture, forestry and fishing came in at 37 percent, and arts, entertainment and recreation at 33 percent. Public administration, defence and social security was just above 1 percent. Women were affected more often than men: around 14 percent of jobs held by women versus around 11 percent of those held by men. Regionally, eastern Germany was at roughly 14 percent against 12 percent in the west, with Saxony highest at just under 15 percent and Bavaria lowest at 10 percent.

The second spike: thresholds create their own bunching

The minimum wage is not the only place German pay data clusters. There is a second mass point, and this one sits in monthly earnings rather than hourly.

Germany's Minijob threshold is tied to the minimum wage by formula. Under §8 SGB IV, the limit equals the minimum wage multiplied by 130, divided by 3, rounded up to the next full euro. For 2026, that is 13.90 × 130 ÷ 3 = 602.33, rounded up to €603 per month. At minimum wage, that buys about 43.4 paid hours a month.

Because crossing that line changes a worker's social security treatment, a very large number of contracts are written to land just below it. That produces a sharp, entirely institutional cluster at €603 in the monthly earnings distribution. Above it sits a second regime, the Midijob transition band running from €603.01 to €2,000, where contribution rates phase in gradually rather than jumping.

Converting between the monthly threshold and an hourly rate is the step most analyses get wrong, since the answer depends on contracted weekly hours. Use a (https://stundenlohnrechnerr.de/) to handle the hourly-to-monthly conversion precisely. The threshold also rises automatically with the minimum wage, so it reaches €633 in 2027 when the floor moves to €14.60. Any time series of monthly earnings therefore has a cluster that moves between years, which is easy to mistake for a shift in behaviour when it is really a change in the rule.

One policy, two estimates

Here is the part worth slowing down for. In July 2025, Destatis had estimated that the same increase would affect up to 6.6 million jobs. Six months later, the figure was 4.8 million. Nothing about the policy changed.

Destatis is explicit that both numbers are upper bounds. The method assumes employment levels and structure stay constant and ignores any pay rises after the survey date, which means the true count is lower than the published figure. Apprentices, interns and workers under 18 are excluded because the minimum wage rules exempt them.

None of that makes either estimate wrong. It makes them conditional. An estimate is only as good as its reference period and its stated assumptions, and a figure quoted without either is close to useless. Most articles citing this policy quote one number and leave the survey year out entirely. When a source gives you both, use them.

Measuring the bite: the Kaitz index

Comparing minimum wages across countries in raw currency tells you very little. The standard measure is the Kaitz index: the minimum wage divided by the median wage. It expresses the floor as a share of typical pay, which is what actually determines how much of the distribution the floor touches.

Destatis publishes the index on its minimum wage topic page, and the OECD uses the same ratio for cross-country comparison. If you are comparing floors between countries, this is the number to compare, not the euro amount.

What this means when you analyse pay data

The points below follow directly from the structure described above.

Do not assume normality near the floor. A Shapiro-Wilk test on hourly wages in a low-pay sector will reject, and it should. The distribution genuinely is not normal there, so reaching for a non-parametric method is the right response rather than a fallback.

Report the median and percentiles, not just the mean. A floor that moved 4.8 million paychecks can barely register in the mean of a full-employment sample, and will register even less in the median.

Treat the spike as structure, not error. When binning hourly pay, a bin boundary placed on top of the minimum wage will split or hide the mass point. Set your boundaries deliberately.

Watch for the second threshold. In German data specifically, a cluster in monthly earnings at the Minijob limit is institutional, not a data quality problem. Removing it as an outlier deletes the signal you were looking for.

Label the reference period on every figure. As the two Destatis estimates show, the same policy can produce very different counts depending on which survey year you start from.

Data sources

Statistisches Bundesamt (Destatis), press release No. 025 of 22 January 2026, based on the April 2025 Verdiensterhebung; Destatis press release No. 256 of July 2025, based on the April 2024 Verdiensterhebung; §8 SGB IV (Geringfügigkeitsgrenze); §22 MiLoG. Monthly equivalents calculated at 4.333 weeks per month.