The actions of the Techtopus would have had Karl Marx choking on his cornflakes



The actions of the Techtopus, as detailed here at Pando by Mark Ames, would have had Marx choking on his cornflakes. Or at least screaming “I told you! I told you this would happen!” For this is exactly what he meant by “monopoly capitalism” — and was the end state of capitalism that he wanted to so desperately warn us against.

To recap Ames: various tech bosses colluded in making sure that they didn’t try to poach staff from each other. To the point that at least one of them, Eric Schmidt, said that they wouldn’t even accept people from certain companies who weren’t poached but had tried to reach Google under their own steam. This is clearly not conducive to rising incomes for those who would parlay competition for their services into a higher paycheck. It’s also illegal.

Marx’s explanation of why it’s such a bad idea was closely linked in with that famous idea of the reserve army of the unemployed. If all you need is homogenous labour then having some group of destitute people around without jobs means that you never need to raise the workers wages, whatever level of profit you’re making. If anyone starts to agitate for higher wages, better conditions or even a five minute toilet break once a day, anything at all that will cut into profits, you can simply fire them and take on some of that reserve army. As Marx went on to point out, this calculation changes markedly if there are no unemployed people about. You can’t fire your bolshie workers because there aren’t any others to replace them with. So, you’ve got to negotiate. And you’ll also find that the other capitalists are in the same position: they’ve got to negotiate with their own workforces.

It’s worth noting something important about what determines wages here. It’s not that you get paid what you’re worth. You also don’t get paid your marginal productivity (although it might end up that you do). What actually determines your compensation package is what are other people willing to pay you. Your current employer obviously has to offer more than your alternatives (and alternatives in every sense, hours, conditions, vacation, pay, the whole schlemiel) otherwise you’d be off over there.

Given this it is the competition between the various capitalists for the profits they can make by employing labour, that leads to a generally rising standard of living for us peons. And given that this is the point of the economy — a generally rising standard of living for each and every schmuck — this is why we like there being that competition among the capitalists.

Marx warned, though, that the capitalists might cooperate and he called this monopoly capitalism.  There is a certain irony here in that Marx was insisting that it would be free labour markets that prevented this from happening. Ironic given the contemporary left’s distaste for markets themselves. Another such irony is that the only place where this happen entirely was in Stalinist Russia, where national wages were deliberately fixed in order to raise the returns to capital.

We today wouldn’t call this a monopoly: that word means single producer, single provider of something. We use “monopsonist” to describe a single purchaser of something but this was unavailable to Marx, being coined some time after his death. In more detail, given that the tech companies are accused of collaborating to act rather like a single purchaser we would call this oligopsonistic. (Yes, sorry, economic jargon can be just as ugly as any other professional jargon.)

But Marx was against such collusion precisely because it prevented the workers’ wages from rising as a result of competition for their services. And we’re still against it today for exactly the same reason.

We can also take a more Hayekian view of the same problem. Start with Hayek’s point that the only calculating engine we’ve got to make sense of the economy and the market is that market itself. We can’t plan it: one factoid* is that at any moment there are one billion products for sale in Manhattan. That’s one billion types of things, not one billion things themselves. We also don’t know what is the utility function that we want to optimise: the country has 300 million people so there’s 300 million such utility functions. And a brass, 1 inch, 3/8ths, reverse thread screw in Manhattan is not the same as one in Houston or LA. So we’ve 300 million unknown utility functions, one billion products and some number (50? 300? Who knows?) of geographies that we’ve got to run our planning program over. In something approaching real time too.

No, it’s not going to work is it? So, the market and the price system it is then. Which is where we start to get very pissed indeed with people who start sticking sticks into the spokes of that system. Imagine, just as an entirely made up example, some group of people conspired in a manner that artificially reduces the wages of tech engineers. OK, returns to capital for those employing tech engineers rises, how lovely for the capitalists. But what else happens? Clearly, the tech engineers themselves are getting a worse deal than they would have done in a free labour market. But there’s much more to it than that. The failure of wages to rise means that fewer people with the requisite skills end up doing tech engineering: they’re stuck being quants on Wall Street perhaps. And fewer people will respond to those price incentives and acquire those requisite tech skills. We thus have talent, twice, being wasted doing less productive things than being tech engineers. The definition of labour being used to do something less productive than it could be doing is “making us all poorer”.

This effect ripples right out through the economy as well. 500 potential engineers not moving to the Valley means, in the end, 500 hamburger slingers not moving one notch up the labour scale as the gaps haven’t appeared above them. Further, there will be great pressure by the capitalists to find some other source of labour to fill their offices. Perhaps agitation to allow in some number of foreigners who will do the job instead of those Americans who have not been attracted by the capped incomes.

Oh, wait, this is what has been happening, isn’t it?

In the end the point is that if we are going to have a market economy then it’s actually got to be a market economy. And that means coming down like a tonne of bricks on those who collude to try to fix prices. Yes, even if it’s the fixing of the incomes of one of the best paid parts of the society already.

I don’t know what the fines are going to be like in this case but my instinctive assumption is that they won’t be anything like high enough.

*ie, something fun that may or may not be true but is at least illustrative

[Illustration by Hallie Bateman for Pando]



Friday Commentary: Data Insights Or Cornflakes – What do you Base your Decisions on?


Friday Commentary: Data Insights Or Cornflakes – What do you Base your Decisions on?

An insight is actually a source of information and is not data points. It is obtained by the analysis of data that directly impacts the business. Analysts thrive on data; executives need insights.

Understanding the importance of an insight is critical to actually listen to it. The more cornflakes (data points) that get spread around, the less each one means to us. Delivering data is a very difficult task in today’s data noisy society. Headlines in the newspaper or magazines report statistics and forecasts to create powerful headlines and sell more. We buy papers with the headlines; “Lose 20% of you weight in only three weeks” or “40% of your tax money is wasted in bureaucracy”.

Whatever the data points are, they are there as a reference. They are facts to build a story on. The average person in the street actually trust data completely without checking the validity of the source on so many occasions that we forget that we are losing the control over the impact is has on us. A classic example is that we rely in Google to check facts and figures and it seems that the increase of info graphics have made us more interested in data as it looks cool and interesting, but we are still not checking the source of data points.

Often the general press serve us data points that match with what we wish to hear. Some companies even seem to buy research with the intention and hope to see a certain outcome and ask the research company to help us find the answers. Buying cornflakes of different data points and then stringing them together into a story we wish to read, is a very false way of doing business research and it misguides the end interpreter.

data-insightsIn my everyday work reports and data points surround me. Most of my clients want the story before they know if the facts are true or built on solid thinking foundations. It still amazes me how many large cooperation’s and global organisations want to run before they can walk. In some ways, they seem to think that it is better to report something than nothing at all. I have seen media campaign tracking studies and web analytics reports that miss-guide a client into spending millions of Euros in the wrong direction. It makes me wonder if this result is because the agency felt obligated to show some “facts” and findings just to keep the client happy, or if it was because the agency simply didn’t have enough knowledge and resource to look beyond the obvious headlines themselves. Interpretation of data takes deep thinking time, good analytical expertise, and a deductive reasoning and some agencies are struggling to perform on the level needed for accuracy and unfortunately many agencies or consultants are not brave enough to stand up and say that they might not have the answer.

A recent survey by InfoChimps of 300 IT professionals revealed “80% of respondents said the top two reasons analytics projects fail are that managers lack the right expertise in house to “connect the dots” around data to form appropriate insights, and that projects lack business context around data.” Reference:

My top 5 advice on what is critical for success for data insights:

1. Understand that insights need to be built on a data collection that is solid and have a longer perspective than just spending time looking at a week or a month in comparison. Data collection sometimes needs months to become comparable and statistically accurate before any insights and conclusions can be pulled from the source.

2. Managers and teams need to be able to understand the insight and reasoning behind it. Successful business leaders and marketers today are successful because they understand the reasoning and caveats that come with it. They listen to the story and don’t try to build their own wishful biases that are not there.

3. Always know what goal you have and define your KPI:s that can define the progress and/or success. For example, calculate the value of your customers (new versus existing) in order to understand the cost you are willing to pay per incoming lead.

4. Surround yourself with good data insights people (internal and out sourced) who understand the data points; and critically analyse them before they start any interpretation. That is, they are neutral to the outcome. Ensure these people have a leading role in the organisation and can influence decisions taken because of their data credibility.

5. Never trust data before you know its source, accuracy limitations (for example, the survey respondent rate) is and the purpose for it being sent to you – did you request it, or is there a sales pitch behind it? Critical management doesn’t mean that you are negative, it just means that you want to know the background before you start spending your time analysing the numbers.


Sara Clifton has a degree in international marketing and has practised global digital marketing and is specialised in integrating search with the overall marketing plan. She successfully runs her 6 year old company; Search Integration, that specialises in digital media, search and analytics

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