Step 1: Survey Response Collection

This survey collected results via online submission using PaperForm. The survey began on September 27, 2021 and remained open through October 19, 2021. The survey was openly published on LinkedIn and, specifically targeting members of the ServiceNow Community. A list of Survey Questions and summary of responses can be found here. The survey was a multi part form which would save after each part, allowing for some surveys to be submitted incomplete. In total, 193 partially complete surveys were submitted with 180 surveys sufficiently complete for use in the analysis. Most of the partially completed surveys either began the survey without submitting any information or completed only the first page of the survey before abandoning.

Step 2: Response Processing

Response processing involved sanitizing user inputs and discarding incomplete or likely anomalous data as well as computing some inferred data. The following steps were executed in this process.

Remove Submissions With Undisclosed Compensation

The first step involved removing all survey responses that did not disclose the individual’s compensation. Without the compensation, the survey was unable to be used in any of the performed analysis. After this cleanup step, 186 usable survey responses remained.

Remove Spam Entries

During processing, multiple entries from the same IP Address were detected. Upon further investigation, it was discovered that 6 submissions were spam. A seventh submissions from the IP Address was determined to be the correct submission and it was kept in the analysis.

Standardize Free Text Responses

Some of the survey questions used free text response and needed to be standardized. The following adjustments were made to standardize these fields:

  • Standardized company names by ensuring consistent usage of company suffixes such as Co, Inc, LLC, Consulting, etc
  • Changed Higher Education and Oil and Gas entries on “Which of the following best describes your current employer” question to “ServiceNow Customer / End User”

Infer Additional Features

The following features were inferred from provided responses:

  • Percent Salary
  • Percent Hourly
  • Percent Bonus
  • Percent Commission
  • Percent Other
  • PPP Adjusted Compensation
  • Country GNI per Capita

Corrected Experience

Adjusted total experience to reflect the maximum of IT Experience or ServiceNow experience if it was less than either since total experience could not be less than more specific experience. This affected 17 responses.

Purchasing Power Parity Adjustment

In order to enable comparisons across international currencies, all currency values were converted from the survey respondents local currency to International Dollars (Intl$ or GK$) using the 2020 Purchasing Power Parity index. This index is published by the Eurostat-OECD as a method of equalizing and comparing the purchasing power of different currencies in their local markets. This adjustment makes it much easier to directly compare compensation amounts across international borders by converting local currency to the equivalent purchasing power of the US Dollar (USD). In this report, currency values of Intl$ indicate that the currency has been converted using the PPP index. The conversion rates used in this survey report are published below:

Currency Purchasing Power Parity 2020 (local currency / Intl$) Notes
Australian Dollar (AUD) 1.471741
British Pound (GBP) 0.699569
Bulgarian Lev (BGN) 0.702639
Canadian Dollar (CAD) 1.206376
Euro (EUR) 0.706133
Indian Rupee (INR) 21.989558
Moroccan Dirham (MAD) 3.97 Used 2018 since 2019/2020 were unavailable
Pakistani Rupee (PKR) 38.7 Used because data not available on OECD
Philippine Peso (PHP) 19.5 Used because data not available on OECD
Tunisian Dinar (TND) 0.9 Used,decreased%20to%203.66%25%20in%202020. because data not available on OECD
US Dollar (USD) 1

GNI Dollars per Capita (Atlas) 2020

Another calculated measure to improve comparison across international borders is GNI Dollars per Capita. Respondents country of employment was mapped to their Gross National Income per Capita using the Atlas method as of 2020. This indicator provides a continuous variable for comparing different countries by their economic power. This specific indicator was selected during exploratory analysis of the 2019 Salary Survey as it provided the lowest variance of the international economic indicators. Below is the table of values used in this analysis:

Country GNI Dollars per Capita (Atlas) Year Notes
Australia 53,730 2020
Belgium 47,960 2020
Bulgaria 9,540 2020
Canada 43,440 2020
Germany 46,980 2020
India 1,900 2020
Luxembourg 70,840 2020
Morocco 2,980 2020
Netherlands 53,010 2020
New Zealand 42,610 2019
Pakistan 1,280 2020
Philippines 3,430 2020
Prefer not to disclose 37,895 2020 Used Euro Area
Russia 10,690 2020
Tunisia 3,100 2020
United Kingdom 42,130 2019
United States 65,910 2019

Step 3: Top Influencing Features Analysis

The primary objective of this survey was to identify the features that have the most influence on an individual’s compensation. To identify these features, this analysis used the CART Regression Tree algorithm with the ANOVA F Value as the Split Criteria. Although somewhat non-standard (Variance is often preferred for regression tree split criteria), ANOVA F Value provided a number of advantages. First, F Value measures two distinct properties that are useful in splitting data: similarity within a subgroup (within group variance) and distinction from other subgroups resulting from the split (between group variance). The result was more meaningful splits that had less overlap between subgroup compensation values. Additionally, F Value has a built in stop criteria in the form of F Critical. Since F Critical is a useful statistic for determining statistical significance, regression tree splitting could stop whenever the F Statistic dropped below the F Critical value for the best split. Overall, using ANOVA F Value as a split criteria created more informative splits and yielded predictive accuracy on par with the more traditional value of Variance.

Once the regression tree was built, the leaf nodes were populated with the median value of the subgroup. Median was preferred to mean or mode in this case since the compensation values were lognormally distributed as opposed to normally distributed. This is consistent with other research on salary distributions. In a lognormal distribution, simple arithmetic mean tends to over-estimate the central tendency due to the skewed nature of the distribution. For this reason, median was used as the predicted value on the leaf nodes.

Once the regression tree was constructed, feature importance was calculated using the process of Leave One Covariant Out (LOCO) method with the Mean Absolute Percent Error (MAPE) metric. The basic process here is to select a feature to calculate the importance, rebuild the regression tree without that feature, then calculate and compare the MAPE of each model to determine the feature importance. At this point, the features can be ranked from highest importance to lowest. Similar to the rationale for using median, MAPE was selected as the metric due to the lognormal nature of compensation. A raw deviation of Intl$ 10,000 between the predicted and actual value is of limited utility on its own. That deviation would be considered insignificant if the actual value was Intl$ 425,000 but would be quite significant if the actual compensation value was itself only Intl$ 10,000. In that way, MAPE is an effective measure of deviation for compensation for the same reason that a pay raise is compared in terms of the percent raise. In this year’s survey, Median Absolute Percent Error (MdAPE) was added since it is more robust to extreme outliers.

The key to keep in mind is that a feature’s importance is measured by how much more prediction error is introduced when the feature is left out of the model. More error implies more importance.

Generating Features From Responses

The survey responses data set contained both categorical and continuous (ordered numerical) response types. From these responses, different features could be extracted. To be as complete as possible, many feature variations were generated for each response type.

A simple categorical response would produce multiple features including an n-nary feature split that split the dataset into one subset for each possible response as well as potential binary encodings which would generate two subsets, one with all results matching a condition and another with all results failing to match the condition.

An example of this is the respondent’s gender. The gender feature would split a dataset into four subsets: respondents who were Male, Female, Nonbinary, and Prefer not to disclose. Another feature generated from the gender repsonse is Is Male. The effect of splitting on this feature would be to create two subsets: one for all male respondents and another for all other respondents (female, nonbinary, and those who prefered not to disclose). Likewise, binary encoding features could be generated for Is Female, Is Nonbinary, and Prefers not to disclose. More complex conditions could allow for alternative groupings as well. By testing multiple ways of splitting and grouping categorical data, hidden interactions could potentially be identified that may not be immediately obvious by simply analyzing the n-nary categorical split. For example, if the calculated ANOVA F Value for the Is Male feature was the highest (and therefore best feature to split the dataset) while the Is Female feature was ranked significantly lower, then this would indicate that the salaries of non-male identifying respondents were more similar to one another than to male identifying respondents in that particular dataset.

Similar to categorical responses, continuous responses (ordered numerical) generate multiple different features as well. Similar to categorical, one option is to create an n-nary categorical feature split which creates one subset for each possible response in the survey results. Another option is to generate a binary encoded split at each possible response value resulting in two subsets for each response, one subset for all responses where the response is less than or equal to the value and one subset for all responses greater than the value. Lastly, alternative binary encodings similar to categorical data are also possible.

An example of a continuous response is Years of Experience. If the survey responses included 1, 2, 3, 4, and 5 years of experience as responses then there are multiple features that can be generated from this single response. The first generated feature would treat the response as a categorical value, splitting the data into subsets of respondents with 1, 2, 3, 4, and 5 years of experience respectively. Similar to the categorical, binary encoded features could also be generated to group respondents into those with and without a specific number of years of experience. Lastly, because this is a continuous response, a set of binary encoded features are possible such as: <= 1 year of experience and * > 1 year of experience*. A feature of this sort can be generated at 1, 2, 3, 4, and 5 years of experience to generate each possible grouping.

The analysis tool created for this survey automates the generation of these features and allows rapid computing of the F Values at each decision tree split. The result is that this analysis was able to effectively compare over 1700 different possible ways of splitting and grouping the data at every level in the decision tree with significantly reduced manual effort.

Creating the Regression Tree

Following is the process the analysis used for generating regression trees:

  1. Start with all responses as the current dataset
  2. Convert input responses in the current data set to response features
  3. For each feature
    1. Split the current data set by the feature
    2. Calculate the ANOVA F Statistic for the split
    3. Lookup the ANOVA F Critical value for the split
    4. Count the number of responses in each subset of the split
    5. Calculate if all compensation values in each subset fall between 0.75 * subset median value and 1.25 * subset median value (representing a 50% Salary Window)
  4. Find the feature with the largest ANOVA F Statistic that meets the following criteria
    • ANOVA F Statistic > ANOVA F Critical
    • Count in each subset is >= 4 OR Feature is based on Country of Employment
  5. For each subset created by the feature selected in step 4
    1. Repeat steps 3 through 4 with the subset as the current dataset until one of the following conditions is met
      • No suitable feature is found in step 4
      • All compensation values within the subset are within the 50% Salary Window as calculated in step 3.5

Calculate Prediction Model Mean Absolute Percent Error (MAPE)

The result of the above process is a decision tree model where the first node represents the whole dataset and each feature selection is a branch which results in two or more subset nodes culminating in the leaf nodes. Computing the median compensation value of all responses in a leaf node yields the predicted value for responses matching that leaf node’s feature criteria.

Next, the Mean Absolute Percent Error for the model needs to be calculated for use in comparing models and generating feature importance. In this survey, there was insufficient data for providing a separate validation data set, so the same data set was used for both generating the model and computing the MAPE. In this year’s survey, the MAPE calculation was retained for comparison to last year’s values. The process for computing MAPE for any given model is as follows:

  1. Generate the prediction model
  2. Convert input responses in the current data set to response features
  3. For each response
    1. Calculate the predicted compensation value using the prediction model
    2. Calculate the absolute percent error for the prediction using (ResponseValue - PredictedValue) / ResponseValue
  4. Sum all absolute percent errors from Step 3
  5. Calculate MAPE using TotalPercentError / NumberOfResponses

Calculate Prediction Model Median Absolute Percent Error (MdAPE)

Next, the Median Absolute Percent Error for the model needs to be calculated for use in comparing models and generating feature importance. This year’s survey added the MdAPE calculation as a way to moderate the impact of outliers in the dataset and calculate a better central tendency. In this survey, there was insufficient data for providing a separate validation data set, so the same data set was used for both generating the model and computing the MdAPE. The process for computing MdAPE for any given model is as follows:

  1. Generate the prediction model
  2. Convert input responses in the current data set to response features
  3. For each response
    1. Calculate the predicted compensation value using the prediction model
    2. Calculate the absolute percent error for the prediction using (ResponseValue - PredictedValue) / ResponseValue
  4. Find the median value of the absolute percent error values computed in Step 3

Calculate Feature Importance

To calculate a feature’s importance, as discussed above the Leave Out One Covariant method is used in this analysis. This approach is summarized in the following procedure:

  1. Generate the optimum prediction model as specified above
  2. Omit a feature or set of features related to a common question response
  3. Generate a new prediction model without the feature omitted in step 2 (LOCO model)
  4. Calculate the MAPE for the optimum model and the LOCO model
  5. Calculate Feature Importance for the omitted feature via: (LocoMAPE - OptimumMAPE)
  6. Repeat steps 2 through 5 for each Feature included in the optimum prediction model
  7. Sort the features in descending order of Feature Importance

The highest Feature Importance values indicate the most significant features in the optimum prediction model. Note that when a Feature Importance is negative, although leaving the feature out reduced the error it is generally implied that the decrease in error occurred by random chance. Features with negative feature importance are therefore the least significant. Feature importance was also calculated using MdAPE.

Multiple Regression Prediction

It was discovered during exploratory analysis that leaf nodes can use multiple regression of continuous features instead of a single predicted value or a split. For example, one of the generated models split on Country of employment and then used a polynomial regression with years of experience as the x value to compute the predicted compensation. This generated significantly simpler models with only marginal increases in MAPE. The use of these models further demonstrated the importance of specific features.

Percent of Attributable Predictive Capability

The percent of the predictive capability that can be attributed to the top influencing features is calculated as a ratio of the improvement in the prediction model MAPE that can be attributed to the features in the Top Regression Model over the total improvement in MAPE that can be attributed to using all features in the data set for prediction (Optimum Model). The lower bound and upper bound of the percentage are calculated based on the Educated Random Prediction Model and the Random Prediction Model respectively. Below are the equations used:


  • RPM: Random Prediction Model MAPE
  • ERPM: Educated Random Prediction Model MAPE
  • TRM: Top Regression Model MAPE (Model trained in 2020 survey)
  • OM: Optimum Model MAPE
  • LB: Lower Bound of the Attributable Predictive Capability
  • UB: Upper Bound of the Attributable Predictive Capability


  • LB = (ERPM - TRM) / (ERPM - OM)
  • UB = (RPM - TRM) / (RPM - OM)

Prediction Models

The following fields are used in the Prediction Model table to summarize the findings of the Top Influencing Features analysis:

  • Prediction Model: The name of the prediction model
  • Model Trained: The year the prediction model was trained which also indicates the year the Salary Survey data set used to train the model was collected
  • MAPE: Mean Absolute Percent Error of the model when it was used to predict the survey result set (Read more about the Regression Tree training methods)
  • MdAPE: Median Absolute Percent Error of the model when it was used to predict the survey result set.
  • MAPE (2020 Survey): Mean Absolute Percent Error of the model from the results of the 2020 Salary Survey.

The following are a description of the Prediction Models used in the analysis:

  • Optimum Model: A prediction model trained using all available features in the 2021 Salary Survey Data Set
  • Top Features Model: A prediction model using only the features with the highest feature importance using the LOCO method. For 2021, these features included Country of Employment, Compensation Structure, Years of Experience, Employer features, and Relationship responses. For 2020, these features included Country of Employment, Compensation Structure, and Years of Experience
  • Top Regression Model: A prediction model trained in the 2020 survey that was reused for validation. This model used a polynomial regression to represent the leaf nodes of the regression tree. The polynomial regression used years of experience as the independent variable and total compensation as the dependent variable. This approach yielded a simplified and more generalized variation of the Top Features Model.
  • Traditional Salary Table Model: A prediction model that used a traditional salary table broken down by country, current job role, and years of experience at 0 to 3 years, 4 to 7 years, and 8 years or more instead of a training method (see Nelson Frank Salary Survey table for a similar breakdown). This model was trained in the 2020 survey and was reused for validation.
  • Educated Random Prediction Model: A prediction model used as a control that used a random guess about an individual’s compensation between Intl$ 62,748 and Intl$ 232,978 which represented a range the excluded the extreme outliers as visualized on the generated histogram.
  • Random Prediction Model: A prediction model used as a control that used a random guess about an individual’s compensation between the data set’s minimum and maximum compensation values (Intl$ 1,500 and Intl$ 571,781)

Step 4: Targeted Feature Analysis

Once the Top Features were computed and understood, less significant features were analyzed using more traditional comparative analysis methods.

Box and Whisker Plots

The box and whisker plots in this analysis are used to make comparative analysis between the distributions of population subgroups. The GNI Per Capita > 55840 criteria is used frequently in these plots as a convenient way to include countries that otherwise received an insufficient number of submissions to be included in their own comparison. The box and whisker is used instead of a histogram plot in order to balance anonymity with informative results. When relevant, the narrative attempts to inform when the histogram plots reveal noteworthy tendencies that are not reflected in the box and whisker.

On Average Compensation

The analysis makes numerous statements regarding one group’s compensation compared to another on average. These statements are made using either a percent or cents per international dollar to indicate by how much one group is compensated above or below another. These values are computed using the ratio of the median compensation of the subgroups. For example, in the gender comparison, the value range is computed by the following (in the case of gender, there are two values computed for the low GNI per Capita countries and the high GNI per Capita countries):

compensationRatio = femaleMedianCompensation / maleMedianCompensation

This is a common practice in salary research due to the lognormal distribution of typical salary datasets, including the ServiceNow Influence Salary Survey. In these distributions, the arithmetic mean tends to overestimate the central tendency of the group compared to the median, which is more robust to outliers.

Peer Group Comparison

Similar to the on average statements, a number of peer group comparisons are made using column charts. In these cases, individuals in the survey are grouped according to their Country and Years of Experience. This grouping reflects the top influencing features decision tree model. The median compensation of each group is then calculated from the individual compensation values within each group. Next, individual compensation values are compared to the median value of the individual’s group and labelled as Below Median, At Median, or Above Median. Finally, the column charts are generated by displaying the percent of the total population below, at, or above the respective median values compared to the same breakdowns for population subgroups such as gender, ethnicity, education, etc.

In this way, generalizations can be made about what impact belonging to a specific feature subgroup has on the probability of an individual receiving at or below median compensation compared to highly similar peers. Mathematically, the impact is summarized by calculating the Euclidean distance as a similarity measure for each feature value bar and then calculating the sum weighted distance for the feature. The features are ranked according to this value at the end of the Top Influencing Features section. Comparing this metric reduces the ambiguity of the On Average Compensation comparison values which do not consider the top influencing features and can therefore be misleading due to possible covariance.

Covariance Comparison

In some specific cases, comparisons are drawn between subgroups using metrics other than compensation. These charts are produced in order to make determinations about possible covariance between features. For example, a common argument to justify the gender pay gap is that female workers are prone to working fewer hours and requiring more time off than their male counterparts. The statistical argument made here is that gender and work effort are covariant and that the feature responsible for lower or higher compensation is not gender but instead is the covariant feature of work effort. In order to test various claims of covariance, some of the analysis leverages charts that compare potentially covariant features to assess the claim.