Veria - Data Analysis

Wage trends in Germany from 2015 to 2025

Model: Kimi K2.6 Checked on: 09/07/2026

Based on Eurostat’s Labour Cost Index data for Germany, nominal wages and salaries (excluding apprentices) in the broad economy (NACE B‑S: industry, construction and services) show a clear upward trend from 2015 to 2025, with a marked acceleration after 2021.

Key trend figures (annual index, 2020 = 100)¹²

Year	Index	Annual growth
2015	87.5	+2.3 %
2016	89.3	+2.2 %
2017	92.3	+3.3 %
2018	94.9	+2.9 %
2019	97.8	+3.0 %
2020	100.0	+2.2 %
2021	101.2	+1.2 %
2022	105.5	+4.2 %
2023	110.0	+4.3 %
2024	115.7	+5.1 %
2025	119.9	+3.7 %

Summary of the trend

Steady, moderate growth (2015‑2020): Nominal wages rose at a fairly steady pace of roughly 2‑3 % per year, accumulating a 14 % increase over the five years before the pandemic.
Pandemic slowdown (2021): Growth dropped to just 1.2 % in 2021, the lowest rate in the observed period.
Sharp acceleration (2022‑2025): From 2022 onward wage growth picked up considerably, reaching 4‑5 % annually. 2024 saw the fastest increase at 5.1 %, while 2025 remained strong at 3.7 %. Over the full decade, the nominal wage level rose by about 37 %.

The quarterly, seasonally and calendar‑adjusted series confirms this pattern: a visible step‑up in year‑over‑year growth starting in 2022 and continuing through the latest available quarters³¹.

Visualisations of the index and growth rates are provided in the generated charts: the quarterly index trajectory ¹, the annual growth‑rate bars ¹, and the quarterly year‑over‑year growth profile ¹.

Code Run 1

Dataset: lc_lci_r2_q Dataset: lc_lci_r2_a

Images

germany_wage_index_quarterly.svg

germany_wage_growth_annual.svg

germany_wage_growth_quarterly.svg

Dataset filters for `lc_lci_r2_q`

Filter	Value(s)
geo	DE
nace_r2	B-S
lcstruct	D11
unit	I20, PCH_SM
s_adj	SCA
sinceTimePeriod	2015

Dataset filters for `lc_lci_r2_a`

Filter	Value(s)
geo	DE
nace_r2	B-S
lcstruct	D11
unit	I20, PCH_PRE
sinceTimePeriod	2015

Program output

Annual Index values:
 year  value
 2015   87.5
 2016   89.3
 2017   92.3
 2018   94.9
 2019   97.8
 2020  100.0
 2021  101.2
 2022  105.5
 2023  110.0
 2024  115.7
 2025  119.9

Annual Growth rates:
 year  value
 2015    2.3
 2016    2.2
 2017    3.3
 2018    2.9
 2019    3.0
 2020    2.2
 2021    1.2
 2022    4.2
 2023    4.3
 2024    5.1
 2025    3.7

Plots saved successfully.

Code

import pandas as pd
import matplotlib.pyplot as plt

# Load quarterly data
df_q = pd.read_csv(DATASET_FILE0, low_memory=False)
# Filter for B-S, D11, I20, SCA
df_q_idx = df_q[(df_q['nace_r2'] == 'B-S') & (df_q['lcstruct'] == 'D11') & (df_q['unit'] == 'I20') & (df_q['s_adj'] == 'SCA')].copy()
df_q_idx['period'] = pd.PeriodIndex(df_q_idx['time'], freq='Q')

df_q_pch = df_q[(df_q['nace_r2'] == 'B-S') & (df_q['lcstruct'] == 'D11') & (df_q['unit'] == 'PCH_SM') & (df_q['s_adj'] == 'SCA')].copy()
df_q_pch['period'] = pd.PeriodIndex(df_q_pch['time'], freq='Q')

# Load annual data
df_a = pd.read_csv(DATASET_FILE1, low_memory=False)
df_a_idx = df_a[(df_a['nace_r2'] == 'B-S') & (df_a['lcstruct'] == 'D11') & (df_a['unit'] == 'I20')].copy()
df_a_idx['year'] = df_a_idx['time'].astype(int)

df_a_pch = df_a[(df_a['nace_r2'] == 'B-S') & (df_a['lcstruct'] == 'D11') & (df_a['unit'] == 'PCH_PRE')].copy()
df_a_pch['year'] = df_a_pch['time'].astype(int)

print("Annual Index values:")
print(df_a_idx.sort_values('year')[['year','value']].to_string(index=False))
print("\nAnnual Growth rates:")
print(df_a_pch.sort_values('year')[['year','value']].to_string(index=False))

# Plot 1: Quarterly Index
fig, ax = plt.subplots(figsize=(10,6))
df_q_idx_sorted = df_q_idx.sort_values('period')
# Convert period to timestamp for plotting
x = df_q_idx_sorted['period'].dt.to_timestamp()
ax.plot(x, df_q_idx_sorted['value'], marker='o', linestyle='-', markersize=3, color='navy')
ax.set_title('Germany: Nominal Wages & Salaries Index (2020=100)\nNACE B-S, Seasonally and Calendar Adjusted')
ax.set_xlabel('Quarter')
ax.set_ylabel('Index (2020=100)')
ax.grid(True, alpha=0.3)
fig.autofmt_xdate()
fig.tight_layout()
fig.savefig('germany_wage_index_quarterly.svg', format='svg')
plt.close()

# Plot 2: Annual Growth Rate
fig, ax = plt.subplots(figsize=(10,6))
df_a_pch_sorted = df_a_pch.sort_values('year')
ax.bar(df_a_pch_sorted['year'], df_a_pch_sorted['value'], color='steelblue', edgecolor='black', linewidth=0.5)
ax.set_title('Germany: Annual Growth Rate of Nominal Wages & Salaries (%)\nNACE B-S, Year-over-Year Change')
ax.set_xlabel('Year')
ax.set_ylabel('Percentage Change (%)')
ax.axhline(0, color='red', linewidth=0.5)
ax.grid(True, alpha=0.3, axis='y')
fig.tight_layout()
fig.savefig('germany_wage_growth_annual.svg', format='svg')
plt.close()

# Plot 3: Quarterly year-over-year growth
fig, ax = plt.subplots(figsize=(10,6))
df_q_pch_sorted = df_q_pch.sort_values('period')
x = df_q_pch_sorted['period'].dt.to_timestamp()
ax.plot(x, df_q_pch_sorted['value'], marker='o', linestyle='-', markersize=3, color='green')
ax.set_title('Germany: Quarterly Year-over-Year Growth of Nominal Wages & Salaries (%)\nNACE B-S, Seasonally and Calendar Adjusted')
ax.set_xlabel('Quarter')
ax.set_ylabel('Percentage Change (%)')
ax.axhline(0, color='red', linewidth=0.5)
ax.grid(True, alpha=0.3)
fig.autofmt_xdate()
fig.tight_layout()
fig.savefig('germany_wage_growth_quarterly.svg', format='svg')
plt.close()

print("\nPlots saved successfully.")

Eurostat dataset: Labour cost index by NACE Rev. 2 activity - nominal value, annual data
Eurostat dataset: Labour cost index by NACE Rev. 2 activity - nominal value, quarterly data