Location Based Pay Template
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Location Based Pay Template

Create a formulaic and scalable approach to paying in different locations

Intro

One of the biggest challenges global-first businesses face is salary benchmarking. In other words: how do you pay a globally distributed team fairly and cost-effectively?

For local-first teams (i.e., those based in just one location), this process is fairly straightforward. You simply gather market rate data for the local area by role type and decide how competitive your compensation packages should be.

In contrast, the process is much more complicated for global-first teams because you have to consider location as a variable. After all, what’s considered a good salary in one country or city isn’t in another.

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There are three options when paying a global team

Toggle below to learn more about each option, why choose one over the other and finally how to implement it.

If you’re working on a project with me then… ✅ I’ll run you through how each of these options work in your compensation calculator and make recommendations on which option is best for your organisation.

✅ I will pre-load location factors in the calculator for each of your locations based on what I see commonly used with my clients.

Option 1 - Pay individuals at the local market rate

Employees receive their salary adjusted to the geographical area where they live.
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  • ‘Pair fairly, but not equally’
  • ‘Individual, market based approach’

Examples of companies taking this approach

Why do it?

  • 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.

Why not do it?

  • 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.

Recommended approach to paying local rates

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1. Select a baseline market
  • Select a robust baseline market to act as your blueprint for building 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 market should have sufficient benchmarking data available for the majority of roles within your organisation.
  • I recommend only choosing 1-2 baseline markets to build your other locations from. For context, Gitlab and Buffer only use one baseline market (San Francisco) for all their locations.

Why should I use a baseline market to build from and not data in each location?

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.
    • Let’s consider a hypothetical scenario where a company has 8 job families and operates in 4 locations:
      • Option 1: Baseline market approach
        • Salary bands to build = 8
        • With a baseline market approach, the company only needs to build salary bands for the 8 job families in their baseline market.
        • They then apply location factors to adjust for the cost of labour in each location.
      • Option 2: Location-by-location approach
        • Salary bands to build = 32
        • They would need to manage and maintain salary bands specific to each job family and each location. In this case, it would be:
        • Number of Job Families * Number of Locations = 8 * 4 = 32 different salary bands
  3. Facilitates Scalability: Adaptable to new markets and job families, streamlining expansion efforts without redesigning compensation structures.
  4. Enhances Transparency: Employees can understand salary adjustments better, improving satisfaction and minimising disputes.
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2. Gather data from your other locations

For countries outside of your baseline market you will need to gather data from local markets:

  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.
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3. Analyse the data and determine your location factors
  • 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.
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  • This analysis will allow you to create a location multipliers aka location factors.
  • When analysing the location factors, you may well want to adjust them up or down based on certain circumstances:
    • Where you are having challenges with talent acquisition and retention.
    • What you feel is ‘fair’ or the ‘right thing to do’.
    • Insufficient data points are available to render the location analysis as significant.
  • Once you have your location factors signed off, they can be applied to the baseline market data set to calculate pay across the various locations.
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4. Convert to local currency

Finally, convert the salary into the local currency. As currency fluctuates regularly, I recommend using a rolling 1 year average 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’.

Option 2 - Pay individuals based on a location band or tier

Employees receive their salary adjusted to a group of geographical areas defined by the organisation to be similar
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This approach again uses localised data but then groups similar location costs into bands.

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

Examples of companies taking this approach

Why do it?

  • 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’.

Why not do it?

  • 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.

How to do it

  • 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.
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  • 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 a rolling 1 year average to avoid extreme lows or highs.

Option 3 - Pay individuals one global rate

Employees receive their salary adjusted to the location of their organisations HQ
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  • ‘Equal pay for equal work’
  • ‘Location agnostic pay’
  • ‘Everyone in the same role at the same level is paid the same regardless of location’

Examples of companies taking this approach

Why do it?

  • 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.

Why not do it?

  • 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.”

How to do it

  • 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, I recommend using a rolling 1 year average 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’.

How does this template help you?

The template allows you to carry out a cost of market evaluation compared to your desired reference market in a matter of minutes and quickly create location multipliers for new locations where you may have a blind spot.

✅ Visualise and understand how compensation differentials vary globally from your data sources

✅ Understand the cost implications for adopting one of the 3 pay strategies above so you can make a data informed decision on how to pay in each location, while taking into consideration your organisation's values and philosophical beliefs

✅ Model the salary costs in any currency, and in any location in the world, in order to build salary ranges that align to your organisations philosophical beliefs.

✅ Build out a local salary structure for a new location (city, country or region) in a matter of minutes

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Step 1: Collect and normalise data salary data
  • 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.
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Step 2: Compare location differentials (table)
  • 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).
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Step 3: Compare location differentials (visualisation)
  • Step 3 brings the location differentials to life, by visualising how the pay differs between your reference market and other locations.

Visual 1 - Horizontal Bar Chart

  • 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.
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Visual 2 - World Map

  • 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.
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Step 4: Compare pay strategy costs
  • 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.
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Conversion rates

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

Creating your bands

  • 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.
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Step 5: Set your pay strategy for all levels across all your locations

In this final step, you’ll have the opportunity to create a salary range for all the levels in one job family, across all of the locations you entered in Step 4.

How:

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

The template will then calculate local market salaries using:

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

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Bonus: Cost of living and rent index analysis
  • In addition to analysing the cost of labour (Steps 1-5), you may also want to look at the difference in cost of living and rent index for an employees city compared to your reference/base market.
  • Looking at the ‘purchasing power’ in different locations can provide you with more data when deciding on the final location factor to set.
  • The example below shows London vs the rest of the market.
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  • The example below shows Brisbane vs the rest of the market.
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  • The template can take care of this analysis for you. All you need to do is copy and paste in data from Numbeo and then select a reference market you wish to analyse.
  • Below is a video on how to update the data from Numbeo.
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FAQs

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Where do I find market data for the template?
  • The template comes with fake data to demonstrate how it can work. You will need to provide your own data to power the template.
  • If your current salary benchmarking provider doesn’t have coverage for certain locations, then you’ll need to turn detective and hunt down comparable data sources in order to create comparable sets of data and your location factors. For example:
    • Explore signing up to another salary benchmarking provider that does have data in the location you wish to benchmark against.
    • Looking at Numbeo (cost of living data) is something that GitLab does and the parallels between cost of living and rent index vs cost of labour (data from a salary benchmarking provider) are typically quite close.
    • Looking at local market recruitment reports from recruiters who work in those locations.
  • Warning When crowdsourcing data, you should never compare providers and locations against each other i.e. comparing data in London from LinkedIn against data from Radford in Italy. You simply won’t be comparing ‘apples to apples’ and consequently the location differentials will be incorrect.
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Which of the three options should you choose?

When deciding which option is right for your organisation, aside from the obvious financial implications each of them carries, one of the underlying questions to ask yourself is ‘what is fair and equal?’ Is it about fair purchasing power or equal pay for equal work?

Take a look at the following two scenarios

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Sarah is a senior software developer at Truffle Ltd. She currently lives in London making ÂŁ95k per year. She decides she wants a change and moves to Lisbon.

The rate for her role and level in Lisbon is ÂŁ65k per year.

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Paavan is a senior software developer Truffle Ltd. He currently lives in Lisbon making £65k per year. He decides he wants a change and moves to London. 

The rate for his role and level in London is ÂŁ95k per year.

In the scenario above, one move lowered the salary, while the other increased it. In both situations, their quality of life remained the same. Is this what real freedom of location looks like?

Things to think about

  • What would you do in these scenarios?
    • Would you decrease Sarah’s salary?
    • Would you increase Paul’s salary?
  • What’s fair in either of these scenarios?
  • Why is one’s personal lifestyle choice rewarded and the other penalised?
  • Does Sarah become less valuable to the company overnight?
  • Alternatively, does Paul become more valuable to the company just because he chose to move?
  • What happens if Truffle Ltd asks Paul to move to a more expensive city?

There are no right answer to these questions. When it comes to deciding how, and indeed if, to factor location into your compensation philosophy, your decision to do so will depend on numerous factors, and typically boils down to two things:

  1. Your organisational values
  2. What the organisation can afford.
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Should you apply the same location multiplier across all levels?
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Which currency conversion rate should I use?
  • 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’.
  • We recommend using a 1 year average to avoid extreme lows or highs.
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I’m getting #VALUE! errors on the sheet?
  • This typically happens when the GoogleFinance function which drives the currency conversions has a ‘moment’ and doesn’t update.
  • Simply reloading the sheet and this will fix the problem. If it doesn’t there is a chance that you’ve modified non-blue cells and possibly broken something. In this case, take a look back through the revision history to return or make a copy of the sheet when it was working as expected. Alternatively book in a call with me using this link so I can help you problem solve the issue (N.B. this is a paid call).
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How should I communicate the choices I make in regards to location based pay?
  • Hello compensation philosophy! Don’t let employees find out through water cooler chats that their pay is different based on where they live. Make sure you document the ‘how’ and ‘why’ about your pay philosophy in a compensation philosophy document.
  • Justly can help you do this. Get in touch using the links below if you’d like more information.
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I’d like help with customising this template

If you’ve purchased a template and would like help customising it, then you can book in a 30min session with me as a jumpstart to using it and making it work for you particular needs.

30mins - ÂŁ75+VAT

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