Factors influencing IT salaries in Europe

Factors influencing IT salaries in Europe

Salaries are not set independently by management teams. They are influenced by factors such as the talent market environment, the company’s positioning, and the country’s economic situation, among others. Countries with a high cost of living, for example, will need to pay higher wages to attract talent.

For this article, we conducted a study to determine which factors have the greatest influence on salaries. We attempted to find positive and negative correlations: relationships or connections between two variables. One of them is always salary in this case. To put it another way, we tested whether changing some variables (such as the cost of living) resulted in a consistent enough increase (or decrease) in salaries to establish a relationship.

Given these characteristics, the study also attempts to predict wages with a high degree of accuracy.

Introduction to the study

10,000 salaries for five different IT positions were chosen for this study. The study area was limited to Europe and included 745 cities from 35 different countries. The positions examined are divided into five categories: frontend, backend, full-stack, java, and software developers.

Personality traits and need structure differ across cultures. As a result, the analysis is limited to European countries. Although European countries have cultural differences, these will be insignificant when compared to cultural differences with other continents.

It tries to determine what influences salaries based on economic variables (such as cost of living, inflation rate, GDPpc, and so on) or company characteristics (such as company sector, size or funding).

Economic factors may influence compensation in companies as well as influence their competitors. As previously stated, countries with a high cost of living typically offer higher wages. Other variables considered influential by economists include the inflation rate, GDP growth, and population, among others. This study determines whether or not current IT salaries in Europe respond to this stimulus.

Internal company characteristics determine salaries. Because small businesses, for example, do not have as many resources as large corporations and have a more limited salary budget.

The effects of economic variables on salaries

When statistical regressions are examined, one variable clearly has a greater influence on the compensation under consideration than the others. This is the case of the cost of living plus rent index.

When the correlation was salaries versus cost of living plus inflation rate, the results improved. GDPpc also had a high correlation with low wages. Other variables have been shown to be unrelated to wages. All correlations discovered are positive correlations. It means that, when these variables increase, salaries increase proportionally.

Scatterplot and regression line of the salaries regression on the cost of living plus rent index.
Scatterplot and regression line of the salary regression on the cost of living plus rent index.

There was no interesting regression in the regressions with just low or just high salaries. Nonetheless, following a similar idea yielded better results. This was the case when, instead of analyzing the lowest – or highest – salaries in general, we looked at the lowest – or highest – normalized salaries. This ensured that the lowest and highest points of each city were taken, rather than the extreme ones in general.

In these regressions, the variables inflation rate – for high normalized salaries – and GDPpc – for low normalized salaries – began to stand out. Correlations found using normalized salaries were also positive correlations.

Scatterplot and logarithmic regression line of the regression of Salaries on GDPpc for the first quantile of normalized salaries.
Scatterplot and logarithmic regression line of the regression of Salaries on GDPpc for the first quantile of normalized salaries.

How company characteristics affect salaries

Different types of businesses may follow different compensation trends.

The following boxplots are used to analyze company characteristics. In the first graph, categories are increasingly ordered by company funding in euros, and in the second graph, by employee count. At each level, the colored areas cover salaries ranging from the 25th to the 75th percentile. Outliers are represented by the lines and dots outside of the colored areas. It is useful to see where the majority of salaries are distributed.

Salaries distribution boxplot based on company funding.
Salary distribution boxplot based on company funding.

The median wages show that there is no clear relationship between these variables. However, the outliers do follow a pattern. The greater a company’s initial funding, the more outliers with high salaries appear.

Salaries distribution based on company size as represented by a boxplot.
Salary distribution based on company size as represented by a boxplot.

This is yet another example of two variables that will not correlate statistically. The distribution is consistent across all businesses, regardless of size. However, the upper tails of the largest companies – those with more than 1,000 employees – are on the rise. The trend is clearer in the final category, companies with more than 10,000 employees, which have the highest average salaries by difference.

Conclusions derived from graphs

Two main conclusions can be drawn regarding how company characteristics affect salaries:

  • Extremely high wages can only be afforded by companies with a large amount of money.
  • Salaries are distributed similarly in companies with up to 2,000 employees. Once a company grows larger than that, the average salary rises.

Additional conclusions

This article examines the variables that resulted in a correlation. Rent, GDP per capita, and inflation all have strong positive correlations. Variables such as the country’s population or GDP growth rate, on the other hand, have little influence on salaries.

However, with variables that do affect compensation, it was possible to train a model to predict wages. It was discovered that by knowing the economic variables of a country and the characteristics of the company we are targeting, it is possible to predict the average salary of a country with a 90-95 per cent accuracy.

This article was created by analyzing the TalentUp database. The database contains information on 250 million candidates, 56 million job postings, 16 million salaries, and 7 million businesses.

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About Author

Èlia Adroher i Llorens

Content Writer. Èlia studied International Business Economics with a focus on digital marketing. She is also interested in learning about data analysis.