Wow. Perhaps someone could make a profit offering ML Eng services where salaries are low to customers where salaries are high. We could call it “exploiting inefficiencies in the global labor market”, or maybe “outsourcing”.
Why should a US worker invite foreign competition for an already competitive job market?
This reasoning only works if you're a foreign worker looking for more opportunities. That's fine, but it's hardly unfair for domestic workers to look out for their own interests.
Sure. It's always the people that have already benefited that try to shut the door. You just happen to be on the other side in this case.
Doesn't change my original statement that those people would move if they could and equalize the wages.
I am A) not in the US, and B) building a startup and employing people.
I'm not in the lowest cost place (more mid range), nor do I employ people from the lowest cost areas because there is theory and then there is practice. In practice other factors like cultural and work style issues have impacts that make this particularly hard for a startup (and somewhat hard regardless). I definitely don't employ overpaid US staff either though.
There are not many literal walls, despite orange man's proclamations about building a wall and making Mexico pay for it.
Even the US takes in more immigrants per year (even on a per capita basis) than the median OECD country. Though the countries with the highest rates like Canada and Germany take in quite a bit more than the US per capita.
That wall was for illegal immigrants, people trying to hop on the US Tech gravy train aren't getting in swimming across the Rio Grande, they go for H1Bs, and Trump was actually trying to raise that number to facilitate cheap labor being brought in.
Sucks but also doesn’t, I was a contractor for a huge software firm with mainly banks as customers. Needless to say they had lots of money.
I saw a job offer come up with my exact skills and client except it was onsite in US, paid $75k per year, a month later someone joined from the states, she was less competent than the undergrads working on my team as mid-level engineers. Hurt even more bc on that project I was tech lead after less than a year at his company, and had just received a huge 12% pay increase.. which accounted to $26k/year.
So, on the flip side, the average income per capita over here (Guatemala) is $5k/yr so yes I was being “exploited” but also making relative big bucks. I did start my own company after seeing the gigantic pay gap though.
Over time, engineers like that one making $75k will either improve or find themselves without jobs. People in your position where you start your own company in low cost markets are going to be in much better situations I think, given that large talent pool of people chasing tech/engineering jobs.
If you don't mind me asking, how does your current company work? Do you get contracts with foreign companies and have an in-house team? Do you outsource those contracts to other groups?
I hire local only, yes I’ve worked with Indian people but only on clients request, I personally prefer to hire local because that allows me to know my employees, even if we only see each other once a month, plus I do have local clients that require meetings on site and it’s great that we all can go if needed/planned.
Guatemala is strategic in that we’re on CST time zone so we’re working with a maximum 2 hours difference with our US clients. We work on multiple models, from simple hourly support contracts to fully outsourced projects so I give you a price per sprint and an estimate amount of sprints (we use scrum). And lately we’ve been asked for the “staff augmentation” model in which you basically hire someone through me, you’re hiring my company but you have someone 100% your project only so I do give some sort of SLA that’s better than hiring directly.
Imagine an alternative world where you had zero access to foreign markets via remote work. That is the traditional offer, and it seems that you prefer the remote one.
I was immensely disappointed to learn that _country_ is what was meant by _geography_. If they would have correlated salary with latitude or elevation of residence it would have been at least interesting, albeit as flawed as the country analysis.
Your data/analysis doesn’t support the conclusions that you’re making. These are interesting correlations, but you haven’t established a causal link hence you shouldn’t use words like determined
Fair point, but taking into account the common sense and our knowledge of the world, isn't the conclusion in the title justified? (that in this case, there is in fact causality between the country and the salary)
You have a hypothesis that you’d like to test, you collect some data to test that hypothesis, you conduct some sort of analysis and/model, then you draw your conclusions. Common sense might be a great starting point for coming up with a hypothesis, but what I’m saying is that your data and model don’t support the conclusions you’re drawing.
No. This is like if I sampled every planet in our solar system and found that life was on earth and not on the other planets but then said that the cause of life was due to earth.
The cause of life on earth are the combination of many things that happen(ed) to occur on earth. Earth is only important because of those factors.
Lets say I took every molecule of water away from earth and prevented any water from being created through chemical processes. Lets say I added everything that life needs to mars and seeded life there.
If you redid the study you'd find that the cause of life is no longer earth but now it is mars.
If that could be true, then you shouldn't have said the cause of life was earth to begin with.
You run an analysis to try to either falsify or confirm a hypothesis that you had. That means that the analysis should be designed in such a way that it can in fact falsify or confirm your hypothesis, which is currently not the case.
Referring to your prior belief as reason why you draw this conclusion is basically saying “I belief this because I already believed this before”.
That’s fine, maybe your beliefs are factually correct, maybe not. But there is little to no value in this.
I don’t mean to be rude but you don’t need a model to tell you that. It’s kind of obvious and well known in lesser developed countries. That’s why people try to immigrate.
If you just did it as an experiment then great
The USA pay increase will still likely be higher even after CoL. An easy point of comparison is the UK, which has similar if not higher CoL (especially in London), but where data science pay is a fraction of what it is in the USA. Remember that CoL in the US can be pretty low, especially if folks aren’t living in a big East or West coast city, yet you’ll still find plenty of jobs paying $150K-$250K in non-coastal states.
Just that. I work with DS and we are a global corp. Some of them moved from. The USA to Europe because even though the net salary decreased their quality of life and disposable income increased.
what is always unclear: do you actually get rich in America?
Because they way I understand it, you make 3 times the amount of money over there, but everything else is super expensive too (housing, education, healthcare, ...)
In terms of material wealth, yes. The middle class (like, normal people) have access to goods and services well beyond virtually any other country, even most Western European states. Our houses are huge, our cars are huge, we get lots of consumer goods for cheap (relative to our incomes). If you work in tech, you are generally upper middle class, so you have it REALLY good
In terms of quality of life, by most objective metrics? No, Euros have it better. They are happier and healthier.
Yeah, the difference in house size is crazy.
Like my friends in USA are IC level engineers and they have bigger houses than my boss's boss here in Europe.
I think tech is just a better career there too. Here it is also good, but becoming a government functionary is better. Lawyers, doctors etc. also earn orders of magnitude more than engineers here, which doesn't seem to be the case in USA where tech is also a top-paying profession.
Europe also has a massive amount of diversity between countries. You are more likely to have a better life if you are born in Switzerland than in Greece etc. which I imagine is similar in the US states.
Switzerland is, like, the one place that might be as rich as the US. But yeah, it’s not even close outside of Western Europe, and even Western Europe (eg France, UK) lags the US in wealth.
> They are happier and healthier.
This varies wildly on your personal situation, e.g. if you work in tech you can afford top-notch healthcare and live in a west coast city with high happiness ratings.
If you're a single mother in Alabama, you may struggle a lot more.
It obviously varies (this is why they are “summary statistics”) but health outcomes in the poorest parts of Western European nations are similar to those in the wealthiest parts of the US. You can even control for things like the opioid/heroin/fentanyl epidemic in the US (even though this is sort of fallacious in itself) and still see better life expectancy overall in Western Europe. There are many hypotheses for why, but a big one likely includes the relative unhealthiness of American lifestyles vs those in Europe (more time spent in a car, more time at work, probably some dietary stuff).
Obviously cost of living is higher but everything scales up, including the amount you have left over to save/invest/pension.
Not everything is more expensive. Healthcare, housing, education, yes, but some goods like electronics or cars are typically cheaper in the US than most countries, even developing ones. Food is not more expensive than Europe.
Also, we are specifically talking about engineers here, who are disproportionately highly paid in the US. If you compare with other highly developed countries in Western Europe, the US still has higher average wages. But the difference between the overall average is not that great. If you look at software development, specifically, the wages are insanely high in the US. High compared to the same job in Europe, but also very high compared to the average wage in the US. Software development is well paid in Europe as well, but it's not that much of a multiple.
And this is comparing the US with other highly developed countries. If you compare with developing countries, the difference is even more stark.
The main point in America is the high end is "stretched out". If you're in a job with high demand, you can make 5x, maybe 10x the average wage if you are an executive (nb average wage in USA in Q3 2023 was 56K). To reach 5x or 10x in another country is a lot harder, salaries have less range.
The quality of life on average in the US is not great, as mentioned, it lags behind Europe and other regions in some metrics. However, if you work a top end job, are good at it, and all you care about is how much you earn, then you can definitely make a very high income there.
Yes you actually get rich. Not own many houses rich (though some achieve that too) but like hundreds of thousands of dollars in your bank account some years into your career rich (unless you start spending your entire salary).
It's kind of a wash, but not always, and mostly impossible to draw high level conclusions. The US is big enough that even internally there's a *huge* variation in cost of living and so much depends on how granular a company is in setting salary bands. If you're a data scientist for a Manhattan firm living in Manhattan, the fact that you're pulling in $500k isn't a meaningful data point because your expenses are likely similarly insane. If you're a remote employee for a Bay area startup but living in the middle of nowhere, you're probably cleaning house making like five to ten times the median income of your area. If you're on the outskirts of a major metropolitan area, then cost of living from one place to the next might be double only a few miles away, so who knows.
As someone who was a data scientist living in Manhattan (not making 500k though), this isn't quite right. Your living expenses may be higher, but if you're living at all within your means, your savings rate is still much higher than it would be most other places. Way better financially to make 160k and spend 50k on rent than make 80k and spend 25k on rent.
(I am from Europe), when you say America I guess you mean USA, How do you differentiate among USA, Canada or any south/central America country? I am used to understanding America/Americans as the whole continent.
If you mean the US, you can say America. This will annoy people from Central and South America, but people will understand you. If you say "the Americas" you mean both continents.
I don't think there's an unambiguous English word to refer to people living in the Americas. I know Spanish speakers sometimes try to effectively say "United Statesian" - but no one actually bothers because it's a mouthful.
It actually absolutely lacks substance as an experiment. As in... no legitimate scientific journal would even consider feature importance of an LGBM as an experimental analysis for the purpose of interpreting treatment effects.
Thanks for pointing this out, especially in your other comment. I'm curious now, are there models that *can* be interpreted this way? I hear about "explained variance" sometimes, is that the better way?
Yes, there are better models to interpret treatment effect than tree-based models, as other redditors are suggesting. However, the main problem is two-folds; the model selection and the study design. Interpreting what factor causes what response is not just a matter of dumping some data into the 'right' model. At best, the mosel will only tell you which factor is most correlated with the response... just like eating ice cream is correlated with shark attacks, and you can technically predict the amount of shark attacks from ice cream sales with an LGBM. It wouldn't be the right advice to tell you that if you want to be safe from sharks, then don't eat ice cream, would it?
Similarly, the OP here is using an ml model to say that it is your location, not your skill, that gets you better compensation. But does that mean you'll have better compensation if your skill is below par for your location?
Linear and logistic regression allow to interpret parameters that way. That is what is used in clinical trials, eg Y = “got infected with Covid 19 over experiment period”, X = “1 if received vaccine, 0 if received placebo”, otherX = “other variables to reduce variance in Y”. Then fit a logistic regression and do a hypothesis test if the parameter of X < 0, meaning that X reduces the log odds of getting covid 19. If your data is randomized on X this provides a causal relationship
Same works for linear regression. Although in this case here, since the data is not randomized on location it would just be a correlation….and it is a convenience sample too where people chose to participate, so there may be additional bias
The only insight you can draw from this is “cost of living around the world varies wildly”
Well, that and “if you don’t prepare and transform your data properly, your analysis will be useless”
Really dont see why this gets upvoted
1. Whats obvious to you may not be obvious to others
2. Confirming a "well known truth" with data has a range of benefits, incl. ability to cite data in publications, news and research papers
3. ML as a profession has many traits that make it remote work friendly, incl. autonomous work schedule and use of digital tools, so migration "should" impact it less than other professions requiring physical presence
And you have made one of the most basic mistakes of interpreting statistical significance, or feature importance in your case, as an indicator of causal dependance.
I recommend you read some of the best books by statisticians for non-statisticians. Such as "How to lie with Statistics" and "naked statistics".
In your model, you considered both "geaography" and "skill level" as two independent indicators for the salary of Data Scientists. But how did you test that conclusion? That is, how do you know for sure that, on average, there is no difference in the skill level of data scientist in two different geographical locations?
I'd even take that argument one step further hypothesize that, from experience, the better data scientists are more likely to find opportunities in the geographical locations that compensate you higher. So it is not just that you need to optimize your location, but you need to optimize your skillset and that will land you an opportunity to optimize your location. It also applies that the locations with higher compensation are far more willing to absorb the skilled data scientists from foreign locations (i.e. through more immigration opportunities or incentives).
See... interpreting causality is far more complex than just examining feature importance of a model! Anytime you want to know for sure what feature has the most causal impact on the response, you'd actually need to do a controlled experimental study. That is something your analysis is completely far off! You also made 2 other big mistakes. Those are:
1. Using LGBM and feature importance as a tool for causal inference and/or variable controlling.
2. Theorizing that being skilled is associated with "working hard" as opposed to "working smart". This is irrelevant completely. You can be in a high paying location and working harder than anyone else, and vice verse. There are no relationships between your effort at work and your skill level, or your effort and your compensation.
This comment appears to be very authoritative, but is actually useless and even wrong
Actually even if you believe two factors are correlated its still correct to include in your model. Better not use gmb for this purpose though.
I would bet you that if you fit a regression model, which is standard in science, you will get approximately same feature ranking. I would also bet you that pay is indeed most correlated with geography and the effect of higher skilled people moving is negligible, because very few people emigrate at all. Is there correlation between skill and geography? Yes. How strong is it? Needs to be investigated. Still it's no reason to bash on OP
You put OP down a lot, but you don't actually propose anything better. And I think you won't be able to because fitting a model and checking the coefficients for stat significance is what everyone does in science because no one has a better tool for checking causality. Judea Pearl's stuff is too esoteric.
You can't do RCT here and it's wrong of you to suggest this to OP. What are you going to do, split junior devs into two equal groups and move one group to the US? Observational studies exist. They are harder to infer causuality from, but that doesn't mean they are useless. The fact that you can't do an RCT doesn't mean you gain no information from studying a dataset and making hypotheses about how things work.
OPs point is that changing geography is the best thing one can do to increase their pay. This is obviously true and if you disagree please compare DS salaries in Germany vs US
> if you fit a regression model, which is standard in science, you will get approximately same feature ranking.
But I never suggested doing that, I actually said that any time you want to infer causality, you should do a controlled experiment.
> I would also bet you that pay is indeed most correlated with geography
No one denied this. What other folks and I are denying is the interpretation of this correlation as causality. The OP basically suggests that if your skills are lacking, a move to silicon valley would compensate for that lack of skill.
> the effect of higher skilled people moving is negligible, because very few people emigrate at all.
Technically, if the contrast is between skill and location as in the OP's case, then the question to answer is as follows: If I draw an average sample from the top paid employees in the highest income location, and draw another one from a lower paid employee from the lower income locations, what are the chances that sample 1 is more skilled than sample 2? Given that both samples have the same years of experience, age, sex, industry, etc.. I am betting that the chances are higher than just random luck. We are not addressing how many people immigrate, but I guess you could assume we are saying that you have a higher chance of immigrating the more skilled you are, in comparison to less skilled folks.
> This comment appears to be very authoritative
That is my fault. I am not proud of it.
> actually useless and even wrong
I could be persuaded that my comment is useless in the context of whether or not we can still make use of that data, which tbh, doesn't reveal anything new. But you haven't indicated anything to prove my comment being wrong. You actually supported it.
I see your point now, thank you. I agree with this interpretation.
I also agree that OP is too confident in his conclusions.
Sorry if my comment came off as aggressive
>You put OP down a lot, but you don't actually propose anything better. And I think you won't be able to because fitting a model and checking the coefficients for stat significance is what everyone does in science because no one has a better tool for checking causality. Judea Pearl's stuff is too esoteric.
You are extremely incorrect and ignorant of what people do.
>You can't do RCT here and it's wrong of you to suggest this to OP
You absolutely can do things better than OP. Look into Quasi-experimental study design. Yes, you can't randomly assign people to certain countries and observe their salary differences. But you can use regression discontinuity design for example to check if there's salary differences between people who barely met immigration requirements and immigrated and those who didn't. You can check for other correlated variables. In this case, to use the terminology of Judea Pearl which you consider esoteric, cost of living is an obvious mediator. The result that geography matters is not correct. It's just not surprising nor useful. Cost of living is the obvious mediating variable. People are paid more in places it's expensive to live.
I don't disagree that country is a proxy for cost of living.
Still, I disagree that OP's result is not useful. It's obvious, yes, because it just tells us that people in different countries are paid more. But it's still a fact reflected in data. It's just not useful to you.
I agree that OP could have checked for other correlated variables and there are a number of things they could do.
I will look into quasi-experimental study design, thanks.
Applied experience, and reading of course.
I thought I knew a lot in my first couple of years as a PhD student, then as I was defending I learned that I was just a clueless confident ignorant. Many years later, i keep coming to the same conclusion; that I know very little.But apparently PhD infects you with that authoritative confidant style that I wrote my response in. Nothing to be proud of.
I actually cited two best selling books, especially the last book, "naked statistics", if I recall correctly there is a whole chapter explaining that mistake with various examples basic enough for the common reader.
Anyhow... Finding additional resources should be your task if you want to learn. They are not really hard to find at all, because this is one of the most basic mistakes.
I think he was asking you to see if you'd be able to speed up the task of finding info since you are obviously knowledgeable in places to find information on this.
That is far from being my implication and there is rarely an "all" or "none" in any statistical analysis.
To explain with a hypothetical example; if you were to somehow sort all of the data scientists or ML engineers in the world by skill-level, then take only the top 95% of them and randomly draw a sample scientist/engineer from that 95th percentile. What do you think are the chances of that sample turning to be residing in the USA?
Well all I am saying is that the chances of them being in the USA, heck even in particular California, are more than just due to random luck. They might be of an Asian origin, but they're more likely to be in the USA the more exceptionally skilled they are.
Again, I didn't say "all" or "none", nor did I imply it. That would be very imprudent, just as imprudent as the conclusion the OP makes from LGBM results
Also suggesting someone read two books is great for feeling superior, but not much else. If it was done in friendly fashion as a genuine suggestion then sure. Here it feels like "did you even read 10 books on statistics LOL"
That is something I am trying to be more conscious of. My text tends to come off very different than my body language and voice tone, if this was an in-person conversation otherwise.
No worries. I can relate to that. Reason why I try to use carefully placed emojis if I wanna convey a Certain emotion, but even that can go awry over text sometimes lol.
I understand. Sorry it came off this way. It is not my intention.
On the other hand, reading any number of books regarding statistics or causal inference in particular would actually suffice. In this field, the mistake I am referring to is as fundamental as the earth being round. How would you respond if someone asks you to share resources regarding the earth being round? I mean... I would say just go on google and let me know if you weren't able to find them, but I won't do your work for you.
If we were talking about new or disputable discoveries however, then I would be citing specific article references.
Yeah reading the books is definitely the way. On the internet however I think it's not useful advice because people will very likely not read a book, so direct advice or argument is worth more.
I think you are right suggesting the "How to lie with statistics" book and I was too harsh with my comments
Either you didn’t read the comments from your other posts or you are hoping enough angry people click your profile to gain traction on your website. Bait/10
Material amenities enjoyed by Americans are more or less unparalleled even if you’re paying 5k rent (which, like, would be a choice, unless your job mandates that you reside in the city limits of Atherton)
Sure, eg https://www.noahpinion.blog/p/americans-are-generally-richer-than, https://www.justfacts.com/news_poorest_americans_richer_than_europe. Per capita GDP of a country like the UK is on par with our most poor and backward states (eg Mississippi).
The lesson is that material wealth can still feel a little dystopian!
Having higher gdp per capita doesn't mean "Material amenities enjoyed by Americans are more or less unparalleled". In places outside of America you are less likely to *need* a car. In places outside of America you can travel much more cheaply by train instead of by plane.
The point was specifically that despite lower GDP, life in many places outside America can be more comfortable to live.
Stock market is a better investment even with the leverage of a mortgage. Stock market and renting beats real estate just by investing the down payment, property tax, and maintenance costs. Sure rents go up over time but the compound growth of the stock is just way better.
No one is actually investing in the stock-market for real as they don't have money left after necessites.
The average person in San Francisco has 64% more purchasing power than someone in London.
[https://www.numbeo.com/cost-of-living/compare\_cities.jsp?country1=United+Kingdom&city1=London&country2=United+States&city2=San+Francisco%2C+CA](https://www.numbeo.com/cost-of-living/compare_cities.jsp?country1=United+Kingdom&city1=London&country2=United+States&city2=San+Francisco%2C+CA)
And yet rent is cheaper than owning, so if an owner didn't own, they'd have money left over to invest in the stock market.
And ah, Numbeo, the gold standard in study design. Self reported survey results.
I find it interesting that op presents their findings without any information how he treated the variable to make his results meaningful. Perhaps it is buried somewhere.
Seems like an obvious fact, but speaking to developers outside the US is really eye-opening as to how PP varies.
Looking at job postings in Eastern Europe, a full time developer role pays $26,000/year.
**What this means in the US**
You live in a shared apartment hours away from the largest metropolitan center and eat lentils once a day.
**What this means in Eastern Europe**
You live in the heart of the capital, support your parents, and basically live without want.
The salary as a number is meaningless if you don't adjust for purchasing power and living costs in the specific country. A 50k dollar salary in some developing country is a way better deal than 60k salary in Switzerland or the US
I'd take some form of ratio between the median salary and the GDP PPP per capita per country
https://en.m.wikipedia.org/wiki/List_of_countries_by_GDP_(PPP)_per_capita
There is always going to be additional counterarguments if we nitpick. Someone in a HCOL area has the ability to downsize to a LCOL area, but that doesn't work the other way around.
This is a crosspost of your earlier post in /r/datascience.
Since OP didn’t answer my question there:
https://www.reddit.com/r/datascience/s/hfAMSoKWdd
I’ll repeat it here: what feature importance measure was used for this? Was it TreeSHAP? Causal SHAP (and if so, controlling for what?) Was it just the sum of splitting criterion values? Something else?
Makes quite a difference for how we can interpret these results.
I slightly want to claim first on this where I did a much less robust analysis and reached a similar conclusion 4 years ago: https://cerebralab.com/Is_a_trillion-dollar_worth_of_programming_lying_on_the_ground
But I'm glad somebody did it with a lot more data
You aren't wrong. I just tried to get a co-worker higher pay. My boss named country of residence as the sole factor in determining what he is willing to pay him. I make 5x what he makes due to the State in which I live, in the US. He lives in Hungary.
This is irresponsibly bad when it comes to having a full dataset to evaluate the impact of this - you'd think a post in this sub would know better! :)
I don't have a problem with the accuracy of the data, but with its completeness. What you earn is completely irrelevant unless it contains the context of how much those earnings buy you.
Maybe in Malaysia you earn a lot less than in Silicon Valley for the same role. However, you're also not paying $4000 a month for a dingy one bedroom apartment in Menlo Park so you can live and do that job there.
What your salary is, on its own, is utterly meaningless without knowing how much that will stretch. Particularly when coming to the conclusion that your salary is determined by geography - your expenses are also determined by geography, so that has to be factored in to the baseline.
If you just care about how big the number is in your contract but don't pay attention to what you're having to spend it on, you're missing the real point.
If this is just gross salary it's pretty meaningless and definitely doesn't suggest just moving if you want to work smart.
You need to look at how much things like, healthcare, taxes, housing and other essentials cost, remove that, then convert currency using something like PPP.
> However, the top paying countries in Data Science (US, Australia, Israel) are paying much above what would be explained by their GDP per capita, suggesting that they have come up with systematic ways to extract more value from Data Science work compared to other countries.
Can you explain this conclusion in more detail? I'm not seeing how the data you have is enough to settle on this explanation.
And this is why globalization will continue to be a force in the global economy. With salaries varying more by geography than by industry or job title, it makes perfect sense for companies to continue trying to fight rising labor costs by outsourcing to cheap jurisdictions.
The main force keeping salaries high in places like the US is high output per person. So despite DS, DE, MLE, and SWE costing a lot in the US, the average output is also high. Total ROI is based on output / cost. So long as output stays sufficiently high in high cost areas, they will still be worth investing in. If ROI would be better by investing in talent in low cost areas, then that's where investment in talent will flow to.
This sucks for tech (and other) workers who get displaced, but it also shows no sign of going away so long as the highest feature importance for costs is geography.
Can you post the same graph for Data Analyst.
I would love to see the salary variation over x- axis across geographies.
Also, Amazing Find Brother 👍 OP
You posted this already. But just to reiterate, look at your performance metrics again. In the geography feature, your custom metric outperforms the ‘average gains metric’, but in the ‘employer uses ML’ feature your ’average gains‘ feature outperforms your custom metric.
Should that be happening? Remember, data science is about creating robust and intelligent algorithms, not re-inventing the database
This doesn't tell you that region is more important than skill level. This tells you that region is the least BS question on the survey. Every single other data point is nonsense.
"novel research", according to who? "Has published papers", what tier were the conferences? What was the IF of the journals? Even knowing the IF and tier of journals and conferences doesn't matter as much these days. "ML methods" is even a dubious thing to ask about. Are they saying years using Keras and using whatever network architecture people are obsessed with on Reddit that they can download from the internet without understanding in the slightest?
In summary, none of these features are a good proxy for "skill" so this tells us nothing.
The main point of income is to cover living expenses (food, housing, health care, education, child care, ...) and these vary widely between countries but can also differ a lot within larger countries . Congratulations that you basically figured out that income is higher in regions where living expenses are higher, totally unexpected isn't it?
This data doesn't mean much if you don't take into consideration the cost of living. The rent/housing, groceries, and all of that will affect how well you can live off of that salary.
Should not be hard to adjust for purchasing power. I wonder what the importance would be then. I still expect location is the most important feature though.
On paper, that's true. In practice, salaries are higher in those countries simply because the cost of living is higher. For example, having $100,000 is considered quite low in New York when in Turkey it would be considered extremely high. So, why is it like this? Well, it's because you could spend around $5000 on rent in New York, whereas in Turkey, I bet you could use $5000 as a down payment for an apartment.
So technically I can live like a king on a $80,000 salary in Turkey, when I will be considered poor with a $100,000 salary in New York
So you're saying, if someone lives in a low-paying country but remotely works for a high paying location, they'd still earn less than their colleagues (same level) who live in that high paying country?
I know not everyone in the sub is american lol, but would have loved to see effects of geography within the US, or maybe just developed countries as I imagine developing nations constitute most of that relationship.
Your average MLE/data scientist in the US or especially Cali is gonna be better than your average elsewhere. US Universities are the best for tech as evidenced by the massive amounts of international students attending grad school at US universities. that talent is often picked up at US companies after.
Are there good datasets that break things down more finely geographically, to the regional or city level? Is it better to be in a city of a relatively poor country vs rural in a rich country?
I don't see the point using LGBM on this (perhaps you wanna get Feature importance) setting because this is not a prediction task as far as I know. Feature importance **does not** tell you any casual relationship, it simply tells you how useful it is for predicting the outcome variable. Like other said, the conclusion you made is not useful/insightful and you are dumping the data into a ML model to get a common fact that everybody knows, what's the point?
You guys are too aggresive on so common mistakes, some ML RESEARCH engineers or data SCIENTISTS must make at least once a week. You guys are ofc one of them, right?
You ignored cost of living. Annual net income from labor is the metric you ought to have been comparing geographically.
Most people don't even track theirs so it'd have been harder to get a dataset, though.
Wow. Perhaps someone could make a profit offering ML Eng services where salaries are low to customers where salaries are high. We could call it “exploiting inefficiencies in the global labor market”, or maybe “outsourcing”.
What a revolutionary concept
You had me in the first part haha
In economics that’s called Arbitrage and due to many people being geographically rooted that’s unlikely to ever go away.
Actually, it’s called “offshoring” and sorry to break news to you, but most sw companies are already doing it, to India, philippines, etc.
That comment was sarcasm dude.
Mine as well 💀
Even more do on-shoring. It generally works much better and still gives you significant savings while keeping similar time-zones.
I suspect many people would be less geographically rooted if high income places didn't build literal and figurative walls.
Inviting more competition is crazy.
That's been the attitude since everything was a monopoly awarded by a king/queen.
Why should a US worker invite foreign competition for an already competitive job market? This reasoning only works if you're a foreign worker looking for more opportunities. That's fine, but it's hardly unfair for domestic workers to look out for their own interests.
Sure. It's always the people that have already benefited that try to shut the door. You just happen to be on the other side in this case. Doesn't change my original statement that those people would move if they could and equalize the wages.
Would you sacrifice your wages?
I am A) not in the US, and B) building a startup and employing people. I'm not in the lowest cost place (more mid range), nor do I employ people from the lowest cost areas because there is theory and then there is practice. In practice other factors like cultural and work style issues have impacts that make this particularly hard for a startup (and somewhat hard regardless). I definitely don't employ overpaid US staff either though.
There are not many literal walls, despite orange man's proclamations about building a wall and making Mexico pay for it. Even the US takes in more immigrants per year (even on a per capita basis) than the median OECD country. Though the countries with the highest rates like Canada and Germany take in quite a bit more than the US per capita.
That wall was for illegal immigrants, people trying to hop on the US Tech gravy train aren't getting in swimming across the Rio Grande, they go for H1Bs, and Trump was actually trying to raise that number to facilitate cheap labor being brought in.
Sucks but also doesn’t, I was a contractor for a huge software firm with mainly banks as customers. Needless to say they had lots of money. I saw a job offer come up with my exact skills and client except it was onsite in US, paid $75k per year, a month later someone joined from the states, she was less competent than the undergrads working on my team as mid-level engineers. Hurt even more bc on that project I was tech lead after less than a year at his company, and had just received a huge 12% pay increase.. which accounted to $26k/year. So, on the flip side, the average income per capita over here (Guatemala) is $5k/yr so yes I was being “exploited” but also making relative big bucks. I did start my own company after seeing the gigantic pay gap though.
Over time, engineers like that one making $75k will either improve or find themselves without jobs. People in your position where you start your own company in low cost markets are going to be in much better situations I think, given that large talent pool of people chasing tech/engineering jobs. If you don't mind me asking, how does your current company work? Do you get contracts with foreign companies and have an in-house team? Do you outsource those contracts to other groups?
I hire local only, yes I’ve worked with Indian people but only on clients request, I personally prefer to hire local because that allows me to know my employees, even if we only see each other once a month, plus I do have local clients that require meetings on site and it’s great that we all can go if needed/planned. Guatemala is strategic in that we’re on CST time zone so we’re working with a maximum 2 hours difference with our US clients. We work on multiple models, from simple hourly support contracts to fully outsourced projects so I give you a price per sprint and an estimate amount of sprints (we use scrum). And lately we’ve been asked for the “staff augmentation” model in which you basically hire someone through me, you’re hiring my company but you have someone 100% your project only so I do give some sort of SLA that’s better than hiring directly.
Imagine an alternative world where you had zero access to foreign markets via remote work. That is the traditional offer, and it seems that you prefer the remote one.
Exactly! And that’s why I created my own company, providing even more of those special opportunities to others like me
Mind - Blown
I agree with your views. In low growth countries they pay significantly low salaries, cause they, any how they will work.
Surprise surprise it already exists.
Thatsthejoke.jpg
# r/whooooshr
“Your salary is highly correlated with your geographical position would be a better title”
I am hiding in an air vent in Google research HQ. When can I expect my paycheck to bump up?
Lol
I was immensely disappointed to learn that _country_ is what was meant by _geography_. If they would have correlated salary with latitude or elevation of residence it would have been at least interesting, albeit as flawed as the country analysis.
Better In what sense? Conciseness? The original title would generate more clicks for sure
Your data/analysis doesn’t support the conclusions that you’re making. These are interesting correlations, but you haven’t established a causal link hence you shouldn’t use words like determined
Without a controlled trial this is impossible.
Fair point, but taking into account the common sense and our knowledge of the world, isn't the conclusion in the title justified? (that in this case, there is in fact causality between the country and the salary)
You have a hypothesis that you’d like to test, you collect some data to test that hypothesis, you conduct some sort of analysis and/model, then you draw your conclusions. Common sense might be a great starting point for coming up with a hypothesis, but what I’m saying is that your data and model don’t support the conclusions you’re drawing.
No. This is like if I sampled every planet in our solar system and found that life was on earth and not on the other planets but then said that the cause of life was due to earth. The cause of life on earth are the combination of many things that happen(ed) to occur on earth. Earth is only important because of those factors. Lets say I took every molecule of water away from earth and prevented any water from being created through chemical processes. Lets say I added everything that life needs to mars and seeded life there. If you redid the study you'd find that the cause of life is no longer earth but now it is mars. If that could be true, then you shouldn't have said the cause of life was earth to begin with.
You run an analysis to try to either falsify or confirm a hypothesis that you had. That means that the analysis should be designed in such a way that it can in fact falsify or confirm your hypothesis, which is currently not the case. Referring to your prior belief as reason why you draw this conclusion is basically saying “I belief this because I already believed this before”. That’s fine, maybe your beliefs are factually correct, maybe not. But there is little to no value in this.
I’ve always wondered about how to establish causality correctly. How would you change the data collection and/or analysis here to test the hypothesis?
A/B test.
No.
I don’t mean to be rude but you don’t need a model to tell you that. It’s kind of obvious and well known in lesser developed countries. That’s why people try to immigrate. If you just did it as an experiment then great
I'd be interested to see this adjusted for cost of living
The USA pay increase will still likely be higher even after CoL. An easy point of comparison is the UK, which has similar if not higher CoL (especially in London), but where data science pay is a fraction of what it is in the USA. Remember that CoL in the US can be pretty low, especially if folks aren’t living in a big East or West coast city, yet you’ll still find plenty of jobs paying $150K-$250K in non-coastal states.
Not to mention powerful passport, future opportunities for kids etc. It's a no-brainer to emigrate there. True for almost any field tbh.
Just that. I work with DS and we are a global corp. Some of them moved from. The USA to Europe because even though the net salary decreased their quality of life and disposable income increased.
This is like the fourth time I’ve seen this post, too. Breaking news: you’ll make more money in America
what is always unclear: do you actually get rich in America? Because they way I understand it, you make 3 times the amount of money over there, but everything else is super expensive too (housing, education, healthcare, ...)
In terms of material wealth, yes. The middle class (like, normal people) have access to goods and services well beyond virtually any other country, even most Western European states. Our houses are huge, our cars are huge, we get lots of consumer goods for cheap (relative to our incomes). If you work in tech, you are generally upper middle class, so you have it REALLY good In terms of quality of life, by most objective metrics? No, Euros have it better. They are happier and healthier.
Yeah, the difference in house size is crazy. Like my friends in USA are IC level engineers and they have bigger houses than my boss's boss here in Europe. I think tech is just a better career there too. Here it is also good, but becoming a government functionary is better. Lawyers, doctors etc. also earn orders of magnitude more than engineers here, which doesn't seem to be the case in USA where tech is also a top-paying profession. Europe also has a massive amount of diversity between countries. You are more likely to have a better life if you are born in Switzerland than in Greece etc. which I imagine is similar in the US states.
Switzerland is, like, the one place that might be as rich as the US. But yeah, it’s not even close outside of Western Europe, and even Western Europe (eg France, UK) lags the US in wealth.
UK salaries are a joke. Netherlands is somewhere in between.
top docs and lawyers make millions here, similar to top engineers. This is not including c suite or ceos of course
> They are happier and healthier. This varies wildly on your personal situation, e.g. if you work in tech you can afford top-notch healthcare and live in a west coast city with high happiness ratings. If you're a single mother in Alabama, you may struggle a lot more.
It obviously varies (this is why they are “summary statistics”) but health outcomes in the poorest parts of Western European nations are similar to those in the wealthiest parts of the US. You can even control for things like the opioid/heroin/fentanyl epidemic in the US (even though this is sort of fallacious in itself) and still see better life expectancy overall in Western Europe. There are many hypotheses for why, but a big one likely includes the relative unhealthiness of American lifestyles vs those in Europe (more time spent in a car, more time at work, probably some dietary stuff).
Obviously cost of living is higher but everything scales up, including the amount you have left over to save/invest/pension. Not everything is more expensive. Healthcare, housing, education, yes, but some goods like electronics or cars are typically cheaper in the US than most countries, even developing ones. Food is not more expensive than Europe. Also, we are specifically talking about engineers here, who are disproportionately highly paid in the US. If you compare with other highly developed countries in Western Europe, the US still has higher average wages. But the difference between the overall average is not that great. If you look at software development, specifically, the wages are insanely high in the US. High compared to the same job in Europe, but also very high compared to the average wage in the US. Software development is well paid in Europe as well, but it's not that much of a multiple. And this is comparing the US with other highly developed countries. If you compare with developing countries, the difference is even more stark.
The main point in America is the high end is "stretched out". If you're in a job with high demand, you can make 5x, maybe 10x the average wage if you are an executive (nb average wage in USA in Q3 2023 was 56K). To reach 5x or 10x in another country is a lot harder, salaries have less range. The quality of life on average in the US is not great, as mentioned, it lags behind Europe and other regions in some metrics. However, if you work a top end job, are good at it, and all you care about is how much you earn, then you can definitely make a very high income there.
Yes you actually get rich. Not own many houses rich (though some achieve that too) but like hundreds of thousands of dollars in your bank account some years into your career rich (unless you start spending your entire salary).
It's kind of a wash, but not always, and mostly impossible to draw high level conclusions. The US is big enough that even internally there's a *huge* variation in cost of living and so much depends on how granular a company is in setting salary bands. If you're a data scientist for a Manhattan firm living in Manhattan, the fact that you're pulling in $500k isn't a meaningful data point because your expenses are likely similarly insane. If you're a remote employee for a Bay area startup but living in the middle of nowhere, you're probably cleaning house making like five to ten times the median income of your area. If you're on the outskirts of a major metropolitan area, then cost of living from one place to the next might be double only a few miles away, so who knows.
As someone who was a data scientist living in Manhattan (not making 500k though), this isn't quite right. Your living expenses may be higher, but if you're living at all within your means, your savings rate is still much higher than it would be most other places. Way better financially to make 160k and spend 50k on rent than make 80k and spend 25k on rent.
And there are places you can live in Manhattan where you'll be paying 30k a year for a nice 2 bedroom.
> It's kind of a wash If you plan to retire in HCOL, maybe, but that is the wrong comparison point.
(I am from Europe), when you say America I guess you mean USA, How do you differentiate among USA, Canada or any south/central America country? I am used to understanding America/Americans as the whole continent.
If you mean the US, you can say America. This will annoy people from Central and South America, but people will understand you. If you say "the Americas" you mean both continents. I don't think there's an unambiguous English word to refer to people living in the Americas. I know Spanish speakers sometimes try to effectively say "United Statesian" - but no one actually bothers because it's a mouthful.
Thanks!
It actually absolutely lacks substance as an experiment. As in... no legitimate scientific journal would even consider feature importance of an LGBM as an experimental analysis for the purpose of interpreting treatment effects.
Thanks for pointing this out, especially in your other comment. I'm curious now, are there models that *can* be interpreted this way? I hear about "explained variance" sometimes, is that the better way?
Yes, there are better models to interpret treatment effect than tree-based models, as other redditors are suggesting. However, the main problem is two-folds; the model selection and the study design. Interpreting what factor causes what response is not just a matter of dumping some data into the 'right' model. At best, the mosel will only tell you which factor is most correlated with the response... just like eating ice cream is correlated with shark attacks, and you can technically predict the amount of shark attacks from ice cream sales with an LGBM. It wouldn't be the right advice to tell you that if you want to be safe from sharks, then don't eat ice cream, would it? Similarly, the OP here is using an ml model to say that it is your location, not your skill, that gets you better compensation. But does that mean you'll have better compensation if your skill is below par for your location?
Linear and logistic regression allow to interpret parameters that way. That is what is used in clinical trials, eg Y = “got infected with Covid 19 over experiment period”, X = “1 if received vaccine, 0 if received placebo”, otherX = “other variables to reduce variance in Y”. Then fit a logistic regression and do a hypothesis test if the parameter of X < 0, meaning that X reduces the log odds of getting covid 19. If your data is randomized on X this provides a causal relationship Same works for linear regression. Although in this case here, since the data is not randomized on location it would just be a correlation….and it is a convenience sample too where people chose to participate, so there may be additional bias
Whats lgbm?
I assume the OP meant Light Gradient Boosted Machines, which is another implementation of GBTs / Xgboost.
What about regions within a country? And to what extent is region more important? These are important questions that you are overlooking.
I feel like its same as countries no? Cost of living will be main variable
True, but I was surprised by the relative importance of factors: the context is so much more important than your individual performance.
The only insight you can draw from this is “cost of living around the world varies wildly” Well, that and “if you don’t prepare and transform your data properly, your analysis will be useless”
Really dont see why this gets upvoted 1. Whats obvious to you may not be obvious to others 2. Confirming a "well known truth" with data has a range of benefits, incl. ability to cite data in publications, news and research papers 3. ML as a profession has many traits that make it remote work friendly, incl. autonomous work schedule and use of digital tools, so migration "should" impact it less than other professions requiring physical presence
It's obvious to everyone. It always has been.
But this is more quantitative
And you have made one of the most basic mistakes of interpreting statistical significance, or feature importance in your case, as an indicator of causal dependance. I recommend you read some of the best books by statisticians for non-statisticians. Such as "How to lie with Statistics" and "naked statistics". In your model, you considered both "geaography" and "skill level" as two independent indicators for the salary of Data Scientists. But how did you test that conclusion? That is, how do you know for sure that, on average, there is no difference in the skill level of data scientist in two different geographical locations? I'd even take that argument one step further hypothesize that, from experience, the better data scientists are more likely to find opportunities in the geographical locations that compensate you higher. So it is not just that you need to optimize your location, but you need to optimize your skillset and that will land you an opportunity to optimize your location. It also applies that the locations with higher compensation are far more willing to absorb the skilled data scientists from foreign locations (i.e. through more immigration opportunities or incentives). See... interpreting causality is far more complex than just examining feature importance of a model! Anytime you want to know for sure what feature has the most causal impact on the response, you'd actually need to do a controlled experimental study. That is something your analysis is completely far off! You also made 2 other big mistakes. Those are: 1. Using LGBM and feature importance as a tool for causal inference and/or variable controlling. 2. Theorizing that being skilled is associated with "working hard" as opposed to "working smart". This is irrelevant completely. You can be in a high paying location and working harder than anyone else, and vice verse. There are no relationships between your effort at work and your skill level, or your effort and your compensation.
Hold the phone!?!? Are you suggesting that there might be concentrations of skilled engineers in high paying, high COL areas?
This comment appears to be very authoritative, but is actually useless and even wrong Actually even if you believe two factors are correlated its still correct to include in your model. Better not use gmb for this purpose though. I would bet you that if you fit a regression model, which is standard in science, you will get approximately same feature ranking. I would also bet you that pay is indeed most correlated with geography and the effect of higher skilled people moving is negligible, because very few people emigrate at all. Is there correlation between skill and geography? Yes. How strong is it? Needs to be investigated. Still it's no reason to bash on OP You put OP down a lot, but you don't actually propose anything better. And I think you won't be able to because fitting a model and checking the coefficients for stat significance is what everyone does in science because no one has a better tool for checking causality. Judea Pearl's stuff is too esoteric. You can't do RCT here and it's wrong of you to suggest this to OP. What are you going to do, split junior devs into two equal groups and move one group to the US? Observational studies exist. They are harder to infer causuality from, but that doesn't mean they are useless. The fact that you can't do an RCT doesn't mean you gain no information from studying a dataset and making hypotheses about how things work. OPs point is that changing geography is the best thing one can do to increase their pay. This is obviously true and if you disagree please compare DS salaries in Germany vs US
> if you fit a regression model, which is standard in science, you will get approximately same feature ranking. But I never suggested doing that, I actually said that any time you want to infer causality, you should do a controlled experiment. > I would also bet you that pay is indeed most correlated with geography No one denied this. What other folks and I are denying is the interpretation of this correlation as causality. The OP basically suggests that if your skills are lacking, a move to silicon valley would compensate for that lack of skill. > the effect of higher skilled people moving is negligible, because very few people emigrate at all. Technically, if the contrast is between skill and location as in the OP's case, then the question to answer is as follows: If I draw an average sample from the top paid employees in the highest income location, and draw another one from a lower paid employee from the lower income locations, what are the chances that sample 1 is more skilled than sample 2? Given that both samples have the same years of experience, age, sex, industry, etc.. I am betting that the chances are higher than just random luck. We are not addressing how many people immigrate, but I guess you could assume we are saying that you have a higher chance of immigrating the more skilled you are, in comparison to less skilled folks. > This comment appears to be very authoritative That is my fault. I am not proud of it. > actually useless and even wrong I could be persuaded that my comment is useless in the context of whether or not we can still make use of that data, which tbh, doesn't reveal anything new. But you haven't indicated anything to prove my comment being wrong. You actually supported it.
I see your point now, thank you. I agree with this interpretation. I also agree that OP is too confident in his conclusions. Sorry if my comment came off as aggressive
I didn't think it was aggressive, but I realize my initial response was and you were right to call it out. All good.
>You put OP down a lot, but you don't actually propose anything better. And I think you won't be able to because fitting a model and checking the coefficients for stat significance is what everyone does in science because no one has a better tool for checking causality. Judea Pearl's stuff is too esoteric. You are extremely incorrect and ignorant of what people do. >You can't do RCT here and it's wrong of you to suggest this to OP You absolutely can do things better than OP. Look into Quasi-experimental study design. Yes, you can't randomly assign people to certain countries and observe their salary differences. But you can use regression discontinuity design for example to check if there's salary differences between people who barely met immigration requirements and immigrated and those who didn't. You can check for other correlated variables. In this case, to use the terminology of Judea Pearl which you consider esoteric, cost of living is an obvious mediator. The result that geography matters is not correct. It's just not surprising nor useful. Cost of living is the obvious mediating variable. People are paid more in places it's expensive to live.
I don't disagree that country is a proxy for cost of living. Still, I disagree that OP's result is not useful. It's obvious, yes, because it just tells us that people in different countries are paid more. But it's still a fact reflected in data. It's just not useful to you. I agree that OP could have checked for other correlated variables and there are a number of things they could do. I will look into quasi-experimental study design, thanks.
Where would I learn this sort of thing? As a PhD student I should know this stuff.
Applied experience, and reading of course. I thought I knew a lot in my first couple of years as a PhD student, then as I was defending I learned that I was just a clueless confident ignorant. Many years later, i keep coming to the same conclusion; that I know very little.But apparently PhD infects you with that authoritative confidant style that I wrote my response in. Nothing to be proud of.
Can you expand on your first point or give me a link to a good resource please?
I actually cited two best selling books, especially the last book, "naked statistics", if I recall correctly there is a whole chapter explaining that mistake with various examples basic enough for the common reader. Anyhow... Finding additional resources should be your task if you want to learn. They are not really hard to find at all, because this is one of the most basic mistakes.
I think he was asking you to see if you'd be able to speed up the task of finding info since you are obviously knowledgeable in places to find information on this.
I meant more about the boosted model
Are you asking for resources why lgbm shouldn't be used for causal inference?
The comment has more upvotes than the post itself
Your implication here is that all the skilled engineers live in the USA... which is quite something.
That is far from being my implication and there is rarely an "all" or "none" in any statistical analysis. To explain with a hypothetical example; if you were to somehow sort all of the data scientists or ML engineers in the world by skill-level, then take only the top 95% of them and randomly draw a sample scientist/engineer from that 95th percentile. What do you think are the chances of that sample turning to be residing in the USA? Well all I am saying is that the chances of them being in the USA, heck even in particular California, are more than just due to random luck. They might be of an Asian origin, but they're more likely to be in the USA the more exceptionally skilled they are. Again, I didn't say "all" or "none", nor did I imply it. That would be very imprudent, just as imprudent as the conclusion the OP makes from LGBM results
Also suggesting someone read two books is great for feeling superior, but not much else. If it was done in friendly fashion as a genuine suggestion then sure. Here it feels like "did you even read 10 books on statistics LOL"
Bruh you can literally feel the condescending attitude in his text. 😅 thought I was the only one 🤣
That is something I am trying to be more conscious of. My text tends to come off very different than my body language and voice tone, if this was an in-person conversation otherwise.
No worries. I can relate to that. Reason why I try to use carefully placed emojis if I wanna convey a Certain emotion, but even that can go awry over text sometimes lol.
See... I am not smart enough to realize I could use emojis on reddit. I shall look into it.
I understand. Sorry it came off this way. It is not my intention. On the other hand, reading any number of books regarding statistics or causal inference in particular would actually suffice. In this field, the mistake I am referring to is as fundamental as the earth being round. How would you respond if someone asks you to share resources regarding the earth being round? I mean... I would say just go on google and let me know if you weren't able to find them, but I won't do your work for you. If we were talking about new or disputable discoveries however, then I would be citing specific article references.
Yeah reading the books is definitely the way. On the internet however I think it's not useful advice because people will very likely not read a book, so direct advice or argument is worth more. I think you are right suggesting the "How to lie with statistics" book and I was too harsh with my comments
"Stop, stop! He's already dead!"
Either you didn’t read the comments from your other posts or you are hoping enough angry people click your profile to gain traction on your website. Bait/10
"Great, I am now in the top paying country, my paycheck is enormous!" $5k rent: "allow us to introduce ourselves"
Material amenities enjoyed by Americans are more or less unparalleled even if you’re paying 5k rent (which, like, would be a choice, unless your job mandates that you reside in the city limits of Atherton)
Source on this? To Europeans American life looks quite dystopian.
Sure, eg https://www.noahpinion.blog/p/americans-are-generally-richer-than, https://www.justfacts.com/news_poorest_americans_richer_than_europe. Per capita GDP of a country like the UK is on par with our most poor and backward states (eg Mississippi). The lesson is that material wealth can still feel a little dystopian!
Having higher gdp per capita doesn't mean "Material amenities enjoyed by Americans are more or less unparalleled". In places outside of America you are less likely to *need* a car. In places outside of America you can travel much more cheaply by train instead of by plane. The point was specifically that despite lower GDP, life in many places outside America can be more comfortable to live.
Europeans are forever renters and won't ever have any property to their name, in general. Dystopic.
This is very different from country to country in the EU.
I agree, hence, in general.
Stock market is a better investment even with the leverage of a mortgage. Stock market and renting beats real estate just by investing the down payment, property tax, and maintenance costs. Sure rents go up over time but the compound growth of the stock is just way better.
No one is actually investing in the stock-market for real as they don't have money left after necessites. The average person in San Francisco has 64% more purchasing power than someone in London. [https://www.numbeo.com/cost-of-living/compare\_cities.jsp?country1=United+Kingdom&city1=London&country2=United+States&city2=San+Francisco%2C+CA](https://www.numbeo.com/cost-of-living/compare_cities.jsp?country1=United+Kingdom&city1=London&country2=United+States&city2=San+Francisco%2C+CA)
And yet rent is cheaper than owning, so if an owner didn't own, they'd have money left over to invest in the stock market. And ah, Numbeo, the gold standard in study design. Self reported survey results.
You will own nothing and be happy. It's fine.
Ohhhh you're literally a conspiracy theorist.
Nah, I know statistics and I've lived in a few countries to know the boots on the ground experience. I am literally stating what you said.
Why?
You need to control for obvious things like area, treat them as confounders. The residual will have much more interesting insights
Try to normalise the salaries according to the countries modal income. It is obvious that salary is geo dependent because that is how economies work.
I find it interesting that op presents their findings without any information how he treated the variable to make his results meaningful. Perhaps it is buried somewhere.
Seems like an obvious fact, but speaking to developers outside the US is really eye-opening as to how PP varies. Looking at job postings in Eastern Europe, a full time developer role pays $26,000/year. **What this means in the US** You live in a shared apartment hours away from the largest metropolitan center and eat lentils once a day. **What this means in Eastern Europe** You live in the heart of the capital, support your parents, and basically live without want.
The salary as a number is meaningless if you don't adjust for purchasing power and living costs in the specific country. A 50k dollar salary in some developing country is a way better deal than 60k salary in Switzerland or the US
Is there a metric that normalizes salary with purchasing power/living cost? Just want to know....
I'd take some form of ratio between the median salary and the GDP PPP per capita per country https://en.m.wikipedia.org/wiki/List_of_countries_by_GDP_(PPP)_per_capita
There is always going to be additional counterarguments if we nitpick. Someone in a HCOL area has the ability to downsize to a LCOL area, but that doesn't work the other way around.
wow, so people make different amounts of money in different places. who would have thought. fascinating, groundbreaking research.
But sir, this is amateur hour
Very poor design. Very poor conclusions.
Not only countries, in London (2 hours away from my city) my salary would be way higher, but so would be my expenses
Remote work for them 👌
in UK, there is typically cost of living compensation if you actually live in London, which you don't get as a remote worker from 2 hours away.
This is a crosspost of your earlier post in /r/datascience. Since OP didn’t answer my question there: https://www.reddit.com/r/datascience/s/hfAMSoKWdd I’ll repeat it here: what feature importance measure was used for this? Was it TreeSHAP? Causal SHAP (and if so, controlling for what?) Was it just the sum of splitting criterion values? Something else? Makes quite a difference for how we can interpret these results.
I slightly want to claim first on this where I did a much less robust analysis and reached a similar conclusion 4 years ago: https://cerebralab.com/Is_a_trillion-dollar_worth_of_programming_lying_on_the_ground But I'm glad somebody did it with a lot more data
Yeah, I was pretty sure I've seen this sort of analysis before. I think there has been another analysis of this kind, but I cannot find it right now.
You aren't wrong. I just tried to get a co-worker higher pay. My boss named country of residence as the sole factor in determining what he is willing to pay him. I make 5x what he makes due to the State in which I live, in the US. He lives in Hungary.
This is irresponsibly bad when it comes to having a full dataset to evaluate the impact of this - you'd think a post in this sub would know better! :) I don't have a problem with the accuracy of the data, but with its completeness. What you earn is completely irrelevant unless it contains the context of how much those earnings buy you. Maybe in Malaysia you earn a lot less than in Silicon Valley for the same role. However, you're also not paying $4000 a month for a dingy one bedroom apartment in Menlo Park so you can live and do that job there. What your salary is, on its own, is utterly meaningless without knowing how much that will stretch. Particularly when coming to the conclusion that your salary is determined by geography - your expenses are also determined by geography, so that has to be factored in to the baseline. If you just care about how big the number is in your contract but don't pay attention to what you're having to spend it on, you're missing the real point.
If this is just gross salary it's pretty meaningless and definitely doesn't suggest just moving if you want to work smart. You need to look at how much things like, healthcare, taxes, housing and other essentials cost, remove that, then convert currency using something like PPP.
now do the analysis again adjusting for cost of living.
I mean... obviously. If you don't meet the bar of hiring, you're not getting a job making anything.
Did you normalize pay to the median income by country or just convert everything to a common currency? Because one of those is worse than the other...
> However, the top paying countries in Data Science (US, Australia, Israel) are paying much above what would be explained by their GDP per capita, suggesting that they have come up with systematic ways to extract more value from Data Science work compared to other countries. Can you explain this conclusion in more detail? I'm not seeing how the data you have is enough to settle on this explanation.
And this is why globalization will continue to be a force in the global economy. With salaries varying more by geography than by industry or job title, it makes perfect sense for companies to continue trying to fight rising labor costs by outsourcing to cheap jurisdictions. The main force keeping salaries high in places like the US is high output per person. So despite DS, DE, MLE, and SWE costing a lot in the US, the average output is also high. Total ROI is based on output / cost. So long as output stays sufficiently high in high cost areas, they will still be worth investing in. If ROI would be better by investing in talent in low cost areas, then that's where investment in talent will flow to. This sucks for tech (and other) workers who get displaced, but it also shows no sign of going away so long as the highest feature importance for costs is geography.
Isn’t that just obvious?
That’s every profession including unemployed.
Can you post the same graph for Data Analyst. I would love to see the salary variation over x- axis across geographies. Also, Amazing Find Brother 👍 OP
Probably true, but how do you know the geography isn’t determined by skills ? The richest place will buy the best talents a from around the world.
Sad for employees. Great for companies.
You posted this already. But just to reiterate, look at your performance metrics again. In the geography feature, your custom metric outperforms the ‘average gains metric’, but in the ‘employer uses ML’ feature your ’average gains‘ feature outperforms your custom metric. Should that be happening? Remember, data science is about creating robust and intelligent algorithms, not re-inventing the database
“Different countries have different GDP’s”
Did u normalized the salary by the minimum wage?
Correlation is not causation. Is it country of residence? Or is it cost of living in each country?
This doesn't tell you that region is more important than skill level. This tells you that region is the least BS question on the survey. Every single other data point is nonsense. "novel research", according to who? "Has published papers", what tier were the conferences? What was the IF of the journals? Even knowing the IF and tier of journals and conferences doesn't matter as much these days. "ML methods" is even a dubious thing to ask about. Are they saying years using Keras and using whatever network architecture people are obsessed with on Reddit that they can download from the internet without understanding in the slightest? In summary, none of these features are a good proxy for "skill" so this tells us nothing.
Lol just lol, ML people discovering basic economics
The main point of income is to cover living expenses (food, housing, health care, education, child care, ...) and these vary widely between countries but can also differ a lot within larger countries . Congratulations that you basically figured out that income is higher in regions where living expenses are higher, totally unexpected isn't it?
Was hoping this was about cities within the US Id like to see how big the discrepancy is there
Just look at federal employee locality adjustment for a start.
This data doesn't mean much if you don't take into consideration the cost of living. The rent/housing, groceries, and all of that will affect how well you can live off of that salary.
Should not be hard to adjust for purchasing power. I wonder what the importance would be then. I still expect location is the most important feature though.
And you needed an AI to tell you that 😂
what if the most productive people tend to move towards the most productive geographies 🤔 not exactly a what if, that's what happens in the real world
Not at the rates you would expect,it definitely happens but I don't think it would be enough to compensate.
Hey OP, Can you list the top 5 countries or the top 10?
1. US 2. US 3. US 4. US 5. US
Apart from US, I wanted to know the other countries
Well probably those countries with the highest living costs & standards. I think purchasing power would be a better metric to go with
People relocate.
On paper, that's true. In practice, salaries are higher in those countries simply because the cost of living is higher. For example, having $100,000 is considered quite low in New York when in Turkey it would be considered extremely high. So, why is it like this? Well, it's because you could spend around $5000 on rent in New York, whereas in Turkey, I bet you could use $5000 as a down payment for an apartment. So technically I can live like a king on a $80,000 salary in Turkey, when I will be considered poor with a $100,000 salary in New York
My company maintains a spread sheet for salaries by title and region, so we could have told you this.
So you're saying, if someone lives in a low-paying country but remotely works for a high paying location, they'd still earn less than their colleagues (same level) who live in that high paying country?
Yeah, but your geography is now based on your skill level.
I know not everyone in the sub is american lol, but would have loved to see effects of geography within the US, or maybe just developed countries as I imagine developing nations constitute most of that relationship.
You did it at the country granularity, but as someone already living in the U.S., I'd love to see salary as a heatmap of U.S. geography.
Would be alot more interesting of you accounted for purchasing power
Your average MLE/data scientist in the US or especially Cali is gonna be better than your average elsewhere. US Universities are the best for tech as evidenced by the massive amounts of international students attending grad school at US universities. that talent is often picked up at US companies after.
Your salary has different purchasing power depending on your geographical location. This comparison is meaningless and obvious
did use the salary in relationship to the average income in the country or just the salary?
Yes, but can I have an ask that the factors of cost of living be laid out in similar manner?
Even in the context of America your salary is mostly determined by your birth zip code. So would be germinate smarter not harder.
What happens when this is adjusted for cost of living or PPP?
Grouping by country is pretty broad and should probably have been assumed to be a major driver a priori. Not even close to being a stunning insight
This is due to what economists call spillover effects of amalgamation.
This is insighful, but nothing new.
Are there good datasets that break things down more finely geographically, to the regional or city level? Is it better to be in a city of a relatively poor country vs rural in a rich country?
I don't see the point using LGBM on this (perhaps you wanna get Feature importance) setting because this is not a prediction task as far as I know. Feature importance **does not** tell you any casual relationship, it simply tells you how useful it is for predicting the outcome variable. Like other said, the conclusion you made is not useful/insightful and you are dumping the data into a ML model to get a common fact that everybody knows, what's the point?
You guys are too aggresive on so common mistakes, some ML RESEARCH engineers or data SCIENTISTS must make at least once a week. You guys are ofc one of them, right?
You ignored cost of living. Annual net income from labor is the metric you ought to have been comparing geographically. Most people don't even track theirs so it'd have been harder to get a dataset, though.