Salary increase calculator

Recently I got a 11% salary increase in Germany which actually shall cover the inflation of 10% in Germany plus some extra increase. The 11% increase is actually based on the gross salary. Due to the progressive taxation, actually the higher my salary is the more tax I will pay.

In this study, I'll look at how much my gross salary should be increased such that my net salary covers the inflation rate.

Data scrapping

Salary calculator URL structure

To figure it out how much taxes and social contributions are deducted from the gross salary, I am going to scrap the data from this salary calculator for Germany. I choose this salary calculator because of the URL structure: https://lohntastik.de/gns/gross-net-salary-calculator/<Income_Period>/<Gross_salary>/<Tax_Class>/<Federal_State>

<Income_Period>: it shall be 1 for yearly income period and 2 for monthly income period. I will chose 2 in my case

<Gross_salary>: This shall be the yearly or monthly gross salary,

<Tax_Class>: The tax class, this shall be a number between 1 and 6. I choose tax class 1 (allocated for singles) for this study,

<Federal_State>: Id of the federal state in which the employee is based. I keep this as 1.

If you wan to know what the net salary would be for a monthly gross income of €3000 for a non-married person living in Baden-Württemberg, you just need to scrap the result of the following URL: https://lohntastik.de/gns/gross-net-salary-calculator/2/3000.0/1/0

Scrapping the data

I am going to use BeautifulSoup to extract the data from the webpage. First I just write a function which input is the gross salary and returns the net salary.

import requests from bs4 import BeautifulSoup from re import sub from decimal import Decimal def getNet(bSalary): vgm_url = 'https://lohntastik.de/gns/gross-net-salary-calculator/2/%f/1/0' % bSalary html_text = requests.get(vgm_url).text soup = BeautifulSoup(html_text, 'html.parser') tax_summary = soup.find("table", {"id": "tax-summary"}).find("tfoot").find_all("tr") data = {} for row in tax_summary: cells = row.find_all("td") skey = cells[0].get_text().strip() if(skey=="Salary After Taxes" and len(cells)>1): return float(sub(r'[^\d\-.]', '', cells[1].get_text())) return 0 print(getNet(3000))

Next I write a for cycle to download the data from a gross salary of €1500 to €8000 with a resolution of €100.

import time res=[]; for i in range(1500, 8100, 100): res.append([i, getNet(i)]) time.sleep(0.1)

Create a dataframe from the data and print it.

import pandas as pd df = pd.DataFrame(res, columns =['Gross', 'Net'], dtype = float) df

Making a 3rd order polynomial fit so it can be queried the gross salary based on the net

import numpy as np import matplotlib.pyplot as plt p = np.polyfit(df.Net,df.Gross,3) grossFit=np.polyval(p, df.Net) plt.plot(df.Net,df.Gross,color='red',label='Scrapped data') plt.plot(df.Net,grossFit,color='blue',label='Fit data') plt.legend() plt.show()

Getting the gross

Now that I have the data, I can get what the gross salary shall by to get x% of net salary increases:

iNetSalary

5400 / 8000

iSalaryIncrease

11 / 30

Calculate the increase net salary:

netSalaryIncreas = iNetSalary *(1+iSalaryIncrease/100) print("The net salary of €%d after %d%% increase will be a net salary of €%d!" % (iNetSalary, iSalaryIncrease, netSalaryIncreas)) print("This corresponds to an %5.2f%% increas from the €%d gross salary to €%d gross salary!" % ((1-np.polyval(p, iNetSalary)/np.polyval(p, netSalaryIncreas))*100, np.polyval(p, iNetSalary), np.polyval(p, netSalaryIncreas)))

Conclusion