Location Based Pay Template

Employees receive their salary adjusted to the relevant average for the geographical area where they live.

• ‘Pair fairly, but not equally’
• ‘Individual, market based approach’

• GitLab’s compensation philosophy is one of the most well-known examples of this.

• Cost effective - The main advantage of paying staff according to local rates is that it enables you to remain competitive in both high and low-wage areas. Even if your employees don’t receive equal wages, they can still enjoy a very similar standard of living. It also means you don’t overpay for talent in lower-wage areas, saving your business money and allowing you to hire more talent.
• Paying staff at local rates takes into account localised market rates for labor or for the cost of living - and often a combination of the two. This allows you to adapt to market changes as and when necessary.

• On the flip side, it can be time-consuming and expensive to keep location data up to date. Additionally, some people also think this approach to salary benchmarking isn’t fair. They might say: “Should someone be “penalised” for working in a lower-wage area? What about equal pay for equal work?”.
• This brings up the challenging question of “what is fair?” Is it paying the same regardless of where someone lives? Fair is such a subjective term, so it can mean a lot of different things.

1. Choose your external data source for salary benchmarking
• Some popular options are Mercer, Pave, Radford, Figures, Ravio, Mercer, and Willis Towers Watson. These providers will have good data and coverage across job families and levels in certain countries such as the UK, US, Germany, France, and other EU countries. However, they often lack data for countries in other regions e.g. Middle East, Australia, Singapore, Hong Kong.

2. Select a baseline market
• Select a robust baseline market as a pivot for the rest of your global benchmarks. Often companies choose the location where the majority of their employees are based, such as their HQ, and anchor off this.
• The baseline location should have sufficient benchmarking data available for the majority of roles within your organisation.
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Why should I use a baseline market and not use market data from each country that I’m located in?

• Using the same underlying data set for each job family and applying location multipliers is a pragmatic solution for several compelling reasons:
1. Ensures Consistency: Keeps compensation structures uniform across locations and job families, fostering trust and fairness.
2. Simplifies Administration: Reduces complexity by managing one dataset, making it easier to implement, update, and communicate changes.
3. Enhances Transparency: Employees can understand salary adjustments better, improving satisfaction and minimising disputes.
4. Facilitates Scalability: Adaptable to new markets and job families, streamlining expansion efforts without redesigning compensation structures.

In summary, this approach simplifies compensation management, maintains data quality, enhances fairness, and supports adaptability and growth, making it a pragmatic choice for the organisation.

Given the reasons above, I recommend only choosing 2 baseline markets max.

3. For the other countries you employ in
• You will need to gather data from local markets, including:
1. Your chosen salary benchmarking provider
• I recommend pulling a report from your provider with all job families in the base market i.e. UK as well as each location available to then evaluate the differential to base market market.
• This is a ‘Cost of labour’ comparison i.e. the cost of attaining work-related skills, knowledge, and experience.
2. Local recruitment surveys
• Examining and cross referencing accurate local recruitment reports data vs the baseline location data.
3. Listening to candidates in that location
• One of the best and most up to date sources for salary data.
4. Local recruiters
• The recruiters who have 'boots on the ground' and are tapped into the market.
5. Professional networks
• Speak to trusted advisors to understand what their thoughts are for specific roles, especially the niche ones where salary tools won’t have data.

4. Create your location multipliers
• In this step, the objective is to understand how each location compares to your baseline location i.e. is it more or less expensive to hire in a given location when compared to your baseline market.
• This analysis will allow you to create a location multipliers. We then apply these to the baseline market data set. These essentially apply a premium or discount to the baseline market dat for a given location.

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Should you apply the same location multiplier across all levels?
You may want to considering whether applying the same location factor across all levels is the right approach. Analysis shows that location differentials can vary between levels across two different locations. For example location differentials may look like:
• 0.3x for levels 1-5
• 0.7x for levels 6-8 (exec levels)
What does this mean? If you were to apply a factor of 0.3x across all levels, you would end up well below the market rate for levels 6-8. On the flip side, applying a factor of 0.7x across all levels, would result in you paying well above market for the lower level roles.
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Speak to Alistair about how to set up different location factors in the compensation calculator for levels 1-6 vs exec levels.

5. Convert to local currency
• Finally, convert the salary into the local currency. As currency fluctuates regularly, I recommend using the average exchange rates of the past 2 years to avoid extreme lows or highs.
• Currency conversions are calculated using GoogleFinance, which update every 20mins.
• You have a choice of using the following rates: ‘Live’, ‘1 year rolling average’ ‘2 year rolling average’.

Employees receive their salary adjusted to the relevant average for a group of geographical areas defined by the organisation to be similar

• ‘Time zone approach’
• ‘Hybrid approach’
• ‘Grouping locations into tiers’ or bands’

• Buffer 2022
• Slite

• Using a banded approach helps close the gap found in traditional compensation approaches where multiple location multipliers result in a fragmented approach to pay. For example, without a banded approach, you may have two people on two marginally different salaries even though one is based in Spain and the other in Portugal.
• However, one of the disadvantages of using this approach is that you may end up “overpaying” some people when compared to the local market rate approach.
• This gets you closer to the ‘equal work, equal pay approach’.

• However, one of the disadvantages of using this approach is that you may end up “overpaying” some people when compared to the local market rate approach.

• First, carry out all steps from Option 1 for each location where your employees are based.
• Then, decide how many location cost bands you would like, as well as the location multipliers for each band.
• Below is an example using five bands. Your approach to choosing a location multipliers for each band can be both “scientific” and based on what you feel is right (i.e., your company goals, values, and beliefs). Read more about how Buffer eliminated the ‘Low’ and ‘Average’ bands in 2022.

• Each location you add to the Justly calculator is automatically categorised into one of the four bands based on their location multiplier e.g. a location with a location multiplier of 0.67 would fall into the ‘Low’ band.
• The Low band is the ‘floor’ and enables you to create a minimum location multiplier i.e. no one will be paid less than 0.7x of the baseline market even if the market data you gather indicates it should be lower. The cost implications of this approach are modelled out in the calculator.
• Finally, convert the salary into the local currency. As currency fluctuates regularly, I recommend using the average exchange rates of the past 2 years to avoid extreme lows or highs.

Employees receive their salary adjusted to the location of their organisations HQ

• ‘Equal pay for equal work’
• ‘Location agnostic pay’
• ‘Everyone in the same role at the same level is paid the same regardless of location’

• Basecamp
• Helpscout
• Gumroad

• Easiest salary calculation e.g. a Snr Account Manager in the UK is paid the same as a Snr Account Manager in Spain.
• Everyone has the freedom to pick where they want to live, and there’s no penalty for relocating to a cheaper cost-of-living area.
• Geography plays no role whatsoever in determining the intrinsic value of the work. All work has a fixed value to the business, irrespective of geography i.e. work carried out in India has the same value as the same work delivered in San Francisco (when looking at two people with exactly the same role and level).
• It helps solve the challenges around figuring out what a ‘fair’ location factor is. Keeping salaries consistent means you don't need to do very small adjustments which makes implementing and running it a very attractive.
• If you are moving towards remote working, a global approach may be more appealing to attract and retain international applicants.

• If you value engaging with people in a way that is sympathetic to their local circumstances, paying well above rate for roles in a low-wage area could be seen as imposing a US/UK-centric view on a community.
• Entering a foreign market has the potential to disrupt the community. For example, offering above market rate may attract talent away from essential local jobs.
• To quote GitLab directly:

“Paying the same wage in different regions would lead to:

1. If we start paying everyone the highest wage our compensation costs would increase greatly, we can hire fewer people, and we would get less results.
2. A concentration of team members in low-wage regions, since it is a better deal for them, while we want a geographically diverse team.
3. Team members in high-wage regions having much less discretionary income than ones in low-wage countries with the same role.
4. Team members in low-wage regions being in golden handcuffs and sticking around because of the compensation even when they are unhappy, we believe that it is healthy for the company when unhappy people leave.
5. If we start paying everyone the lowest wage we would not be able to attract and retain people in high-wage regions, we want the largest pool to recruit from as practical.”

• Companies will take one geographical benchmark and align to that. Which geographical market depends on their financial position. For example Basecamp takes San Francisco (super expensive), where as HelpScout takes an average of cheaper locations (Boston, New York, and Seattle).
• In the Justly calculator all locations and their location indexes would equal 1.00 for the baseline market you choose.
• Finally, convert the salary into the local currency. As currency fluctuates regularly, we recommend using the average exchange rates of the past 2 years to avoid extreme lows or highs. You can choose the exchange rate period in the calculator.

• In this first step you’ll need some reliable and relevant market data for each of the locations you wish to analyse. We recommend using a job family like software engineering as this tends to usually have the most data points.
• The data source you wish to compare each location against MUST be the same to ensure you are comparing ‘apples to apples’. You should not compare two data sources against each other i.e.
• ‘Radford’ London vs ‘Pave’ New York ❌
• ‘Radford’ London vs ‘Radford’ New York ✅
• We start by normalising all available salary data for each location in to one currency. This will allow you to compare apples to apples when comparing salaries across different locations.
• In the example below, we’re just looking at data for one job family. You may want to combine job families to create larger data sets on which to create you location differentials.

• In Step 2 we compare salaries across the different locations entered in Step 1 and calculate location differentials i.e. whether pay is higher, lower or the same in different locations versus your reference market (aka base market).

• Step 3 brings the location differentials to life, by visualising how the pay differs between your reference market and other locations.

• The reference market is always 0% and designated by the vertical black line that runs through the graph.
• All locations are either listed as ‘+’ or ‘-’ differentials to the reference market you selected in Step 1.

• Using the same data from visual 1, the world map shows geographically the distribution of the location differentials.
• Smaller the circle, the higher the negative cost differential compared to the reference market. The larger the circle, the cost differential increases compared to the reference market.

• Now we have our location differentials, we can plug them into the model to understand the cost implications of paying employees using the 3 strategies described earlier in this guide.
• N.B. In order to compare apples to apples across each location and pay strategy, you’ll only enter salary data for one level.

• When normalising salaries in this step, you have the following options:
1. Live
2. 1 year average
3. 2 year average
4. Manual input

• You can choose between 3 or 4 bands.
• 4 bands gives you greater granularity on the groupings and thus allow you to create a wider set of multipliers.
• 3 bands, moves you closer to the ‘equal work for equal pay’ philosophy as you’re eliminating the lower band and bringing employees who would otherwise sit in a lower band into a higher paying bracket. This will obviously cost you more.
• You can set the width of your pay bands and the location multiplier of each band by modifying the blue cells in the tables that sit below Option 2.

1. Select a pay strategy
2. Enter a job family name
3. Enter salary data from your reference market for the job family

• The location multiplier associated with the pay strategy you’ve chosen
• Conversion rate you chose in step 4.