Being part of a global market, we adjust our pricing for global online training courses so that students participating from countries with lower-cost economies can pay a fair affordable price. This opens up our training to a global market making it affordable for anyone to consume our online services.
This is done using a technique from economics called purchasing power parity (PPP). We make PPP adjustments for many countries of the world, please check the table below to find the adjustment for your country. If you don’t find your country listed, please contact us for a quotation. Kanban University has used this technique since 2012 to fairly price certificates for training issues by our network of Accredited Kanban Trainers (AKTs) globally. Recently Stephanie Dziad and Todd Little at Kanban University have done extensive research to update our PPP ratios. We are now leveraging this research to adjust prices for consumers of our online training. This article will share how we calculate PPP using Romania as an example.
Background on PPP
Purchasing Power Parity (PPP) is a trick used by economists to normalize the value and costs of goods and services across different economies. It is accepted practice to report Gross Domestic Product (GDP) in PPP adjusted US Dollar equivalent. This enables the economy of one country to be more accurately compared to that of another. It is also used to assess whether or not the market is fairly pricing currency exchange rates, and to predict whether a shift in a currency may happen against a reserve currency like the US Dollar.
Various methods of calculating PPP have been devised. Perhaps the best known is the Big Mac Index which looks at the true cost of buying a Big Mac in different countries. The basic version looks at the cost of a Big Mac in that country and asks ¨How many hours did the average worker have to work to earn a Big Mac?¨For example, a worker in Seattle might have to work only 15 minutes to earn the cost of a Big Mac. Hence, 0.25 hours. While a worker in Venezuela might have to work 4 hours to earn a similar Big Mac. This means that relatively speaking a Big Mac feels 16 times more expensive in Venezuela than Seattle.
We can use PPP to understand ¨How much more expensive does our training feel to [someone from a specific country]?¨ This helps us to learn how we might price adjust online training to make it affordable.
However, the Big Mac index is notoriously inaccurate. For example, McDonald’s in America is a low-cost restaurant chain offering burgers to the masses at low prices. In some developing nations, however, McDonald’s is positioned as a premium brand for rich upper-middle class knowledge workers and the restaurants are placed in flashy shopping malls. As a result, a burger is unusually expensive in these countries. For our purposes, we have needed a more reliable and fairer mechanism for calculating PPP adjustments.
Initially we pursued a very empirical and qualitative approach based on own staff experience visiting partner firms around the world. While visiting, our staff can look at the cost of basic goods and services. They can also speak with people at client firms and get an understanding of their lifestyle and often their pay and how it relates to levels in the United States. While this served us initially, in order to scale, we needed a more rigorous approach.
The Development Manager Index
In 2012, David Anderson researched PPP from academic papers in economics and developed our own domain specific version – the Development Manager Index. This isn´t based on the number of hours someone must work to earn the Big Macs they are making, rather it is based on the fully burdened cost of employing a software development department manager in a major city in that country. We use Seattle in the United States as our baseline.
The profile is someone with around 15 years industry experience and 2 or more years of experience managing a department of 15 to 30 people.
Often salaries are expressed as net amounts of take-home pay per month, so we have to research the tax deductions and extrapolate an annual gross amount. We also normalize for big cities so that we don’t compare a rural area or small-town salary with a big city in the United States or vice-versa. In countries with really only one major city, such as Peru, with its capital of Lima, we use the capital city, but we try as much as possible to use a major provincial industrial city rather than the capital city as our baseline: so Hamburg in Germany, not Berlin, and Barcelona in Spain, not Madrid, Sao Paulo in Brazil, not Brasilia, but we would use Lisbon in Portugal.
Our choice of the “Development Manager Index” is appropriate because that we sell management training in the technology industry. Hence, we are measuring what employers are willing to pay proportionately as a percentage of the employee’s salary on training and development. While we recognize that training is valued more in some cultures than others and hence in some markets, employers will pay more, our PPP adjustment doesn’t nor should it, account for these differences.
First research the median gross salary including taxes, social security, and any insurances such as private health care, or national insurance, and mandatory pension contributions. The objective is to achieve ¨the cost of employment¨.
Next convert this into US Dollar equivalent using current effective exchange rates.
Thirdly, research the cost of living and the buying power of that money. There is various data available for this purpose and we chose this one which uses a basket of goods and services and normalizes for $1.00. We are making an assumption that training prices in that country reflect the typical cost of goods in the economy. This adjustment is particularly relevant for Kanban University because their trainers are local to their own country and pricing training accordingly.
Now compare this to the Seattle baseline. If we were to measure the salary in say ¨bags of sugar¨, we should now have a calculation for how many bags of sugar the company could purchase, if they spent the cost of employment only on sugar instead. If there is a disparity, this shows how much of a true disparity there is in pay levels between the different economies.
Now an example…
Romania (in 2012)
First, we contacted Romanian colleagues to obtain more insight into their economy and employment market. They were able to give excellent salary information. One man in particular had spent many years living in Italy recently returned to Romania where he works as the enterprise architect for the offshore arm of an American media company. His idea of a net monthly salary expressed in Euros was in the range 2000-2200. This grosses up to 3600-4000 per month. Expressed in US Dollars this is 4680 to 5200. If we take the mid-point of this, we get $59,280 as an annual gross salary.
Next, we make the cost of living adjustment of 0.70. In other words, Romania feels 30% less expensive to live in than the United States. So the true buying power of the salary would feel like $59,280 / 0.70 = $84,685. Let’s round that off to $85,000.
The Seattle baseline at that time would have been around $140,000. So, 140/85 = 1.64. Thus it is 64% more expensive to employee a software development manager in Seattle in the United States than it is in Romania. Inverting it gives us a ratio of 0.607 or approximately 60%. It is 40% cheaper to employ a development manager in Romania than in Seattle.
We therefore use this ratio of 0.60 as our PPP adjustment.
The result doesn’t go all the way to compensating for economy of the country. At a purely raw level, the salary in Romania was $59,280 USD. This is only 42% of the Seattle cost. So paying 60% still feels more expensive. However, the Romanian is able to consume training from the source directly and our costs are in US Dollars or Euros. We are not operating in Romania. The PPP adjustment equation has the effect of splitting the difference. 60/42 = 1.42 or 42% premium. So, the Romanian pays 42% more than they might for inferior local training. While 0.60 means that we are discounting 40% for Romania.
Comparing our Dev Mgr Index with the Big Mac Index
The fairest mechanism for establishing a Big Mac Index PPP adjustment is to look at how long a worker in a McDonald’s has to work in order to earn enough to buy a Big Mac in that country. Using this evidence, it shows us that there is a 6x difference between the United States and Romania in terms of the true cost of a Big Mac. This would suggest a PPP adjustment ratio of 0.16. This is a significantly different result to our 0.6 calculated using the Development Manager Index. The Big Mac Index would provide a ridiculously skewed result in our domain. Over the years, our research has shown that, in countries such as Chile, that the Development Manager Index is a much fairer method to calculate PPP and equitably adjust prices.
Check out the PPP adjustment ratio for your country. If you are interested in taking our training, please contact our sales team and they will make appropriate adjustments in price based on your location. If your country is not shown on our table, please ask.
|NORTHERN AMERICA||United States||1||USD|
|LATIN AMER. & CARIB||Argentina||0.35||ARS|
|LATIN AMER. & CARIB||Brazil||0.4||BRL|
|LATIN AMER. & CARIB||Chile||0.45||CLP|
|LATIN AMER. & CARIB||Colombia||0.3||COP|
|LATIN AMER. & CARIB||Costa Rica||0.6||CRC|
|LATIN AMER. & CARIB||Mexico||0.4||MXN|
|LATIN AMER. & CARIB||Panama||0.6||PAB|
|LATIN AMER. & CARIB||Paraguay||0.3||PYG|
|LATIN AMER. & CARIB||Peru||0.35||PEN|
|LATIN AMER. & CARIB||Uruguay||0.35||UYU|
|LATIN AMER. & CARIB||Venezuela||0.2||VES|
|EAST EUROPE & FSU||Estonia||0.6||EUR|
|EAST EUROPE & FSU||Latvia||0.6||EUR|
|EAST EUROPE & FSU||Lithuania||0.6||EUR|
|EAST EUROPE & FSU||Belarus||0.5||BYN|
|EAST EUROPE & FSU||Georgia||0.4||GEL|
|EAST EUROPE & FSU||Russia||0.5||RUB|
|EAST EUROPE & FSU||Russia (Moscow)||0.5||RUB|
|EAST EUROPE & FSU||Ukraine||0.45||UAH|
|EAST EUROPE & FSU||Bosnia and Herzegovina||0.45||BAM|
|EAST EUROPE & FSU||Bulgaria||0.45||BGN|
|EAST EUROPE & FSU||Croatia||0.6||HRK|
|EAST EUROPE & FSU||Czech Republic||0.6||CZK|
|EAST EUROPE & FSU||Hungary||0.5||HUF|
|EAST EUROPE & FSU||Kosovo||0.4||EUR|
|EAST EUROPE & FSU||Poland||0.5||PLN|
|EAST EUROPE & FSU||Romania||0.45||RON|
|ASIA||United Arab Emirates||0.8||AED|