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we're here at Wen marketing Professor Ron Burman and Pablo Hernandez who is a
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professor of Economics from NYU Abu Dhabi they're here to talk about their paper which is all about the burn rate
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of startups welcome thank you Deborah well first of all can you set the scene for us what is your paper all about so
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um what our paper looks at is how the burn rate of startups influences the chances of a startup to go Bank rupt and
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burn rate which is more of an industry term is um how much money does a startup spend every month per employee so for
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example is if a company spends $300,000 every month and they have five employees
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the burn rate would be $60,000 per employee and it's a number typically the industry uses to compare different
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companies and see which ones are are burning too much or too little money yeah I wanted to add to that be
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that it's a very simple method because you just have an amount of money divided by the number of people there
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you don't go into the details of where do you spend the money and how why is that important to a
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startup so there are two reasons why burn rates are interesting uh and important for a startup to look at the
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first thing is that startups typically operate on investors money and they want to make sure they don't run out of money
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until the next time they can fund raise so they want to make sure they don't
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burn too much money too quickly and the other reason is that investors from their side are trying to figure out
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which startups are operating efficiently but also more successfully and because the startups are very different types of
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businesses the only Apples to Apples comparison is typically how much money they can make or they can spend and the
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burn rate as Pablo said is a great metric for that so what are your paper's key takeaways so the key takeaways there
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are there are quite a few but I think that the most important one is that we allow or we give to be more precise a um
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a notion that if you spend too much or too little the companies are more likely to to fail there is a balance burn rate
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um on in addition we also look at the how the entrepreneurial characteristics in particular human capital are related
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to this sound decision making this balanced uh um burn rate and we find actually that that one dimension of
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human capital which is um education is positively related to to a balanced burn rate and and hence uh lower chances of
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failure so so just to give an example um we find that more education actually reduces uh the chances that a startup
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will fail and another interesting takeaway that we find is that um something we call confidence or optim
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optimism of an entrepreneur actually helps to start up a little bit um and this is a unique part of our research
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what conclusions of any surprised you so actually let me first start with what is
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not surprising in our findings um a very well-known result is that if companies burn too much money they're probably
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going to fail and that makes sense if if you're overspending and if you're
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spending lavishly on things the company doesn't need they might go bankrupt the
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surprising result was that actually under spending money spending to little also increases the chances of failure of
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a company uh we have a few conjectures why that happens but this is one surprising result um a second surprising
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result is that a lot of past researchers looked at what's called human capital as
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Pablo mentioned and typically human capital used to be um two things experience and education we find almost
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no effect for experience um adding a lot of experience to entrepreneurs doesn't
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make the companies fail less but we find quite a big effect uh for Education uh so there is a big gap between failure of
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let's say someone with a bachelor's degree versus someone with a high school
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diploma or someone with a master's degree versus someone with a bachelor's
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degree actually that effect is significant if we look at entrepreneurs with a bachelor's degree versus an
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entrepreneur with a high school diploma the there is a 5% more chances than that
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the bachelor's degree the entrepreneur with the bachelor's degree uh succeeds
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or doesn't fail than the other entrepreneur and although so 5% might seems small this is um like
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entrepreneurship right so the data is very noisy Etc it is a big effect it is a big effect just from the difference
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between entrepreneurs with a high school diploma to entrepreneurs with a bachelor's degree so what are some
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practical implications of your findings how can people use this information to help them so the practical implic
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again that you can look at them from the perspective of the investors and the perspective of the entrepreneur from the
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Investor's perspective is that given that investors are exposed to uh sometimes a large uh range of companies
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they can estimate how how an individual a given company is spending with respect
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to the others and they can assess whether the expenditures are balanced or not uh for R preneurs uh it may work in
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a similar way for entrepreneurs they can go and look at the benchmarks uh the industry benchmarks and see whether they
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are too different too much or too little uh they are spending too much or too little compared to what the industry is
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doing on average and then given these numbers for example investors can say I need to look at a portfolio of 50
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companies I cannot give attention to all of them but I can um kind of signal out
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the ones which are very extrem too little or too much and maybe look into them and see whether they have problems
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and entrepreneurs on the other hand entrepreneurs are typically unsure of how much to spend I
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don't know if I should spend more or less I don't know what the competition
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is doing Etc if you take those numbers this metric is very simple to look at and you can say uh how close am I uh to
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what the market is doing and if I'm Different it doesn't mean it's bad but I
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need to have a good explanation for why it's bad I need to give a good reasoning
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so this is the Practical implication so what said your research apart from other
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work in this area so there are a few aspects uh which make our research unique uh the first one is the data
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source that we're using um this is confidential data that we received from the cun Foundation uh which collected uh
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for seven years between 2004 and 2011 um a long survey about a representative sample of companies that was founded in
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the US it's over 3,000 companies um and they asked them to give information about employees and about
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other things um and we're one of the only researchers that have used this data in this approach yeah another
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another um important departure from previous literature is that even though the burn rate is is is a term that that
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is used widely in the industry uh there is surprisingly little little research at least to our knowledge on that
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important metric and we go to the to the exact exact core of that and and the final thing is that a research looks at
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the Dynamics of the company a lot of research just says this is the companies that started and we see how many failed
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for us we're trying to see what is the chances of failure next year and another
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year and another year which allows us to um filter out a lot of causes for example in our data we have 2008 the
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economic crisis of course more companies were fed during the economic crisis but
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we're able to filter that out and say what is the effect of the B rate above
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that um and this gives us uh statist in statistical language it gives us more power but we can say better what is
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going on with those companies so how will you follow up this research we have two major points um the
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first one is um try to see whether our results are robust so we are currently uh trying to trying to find the same
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effects or looking for the same effects on a data set that's provided by the
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government and that is research in progress and the other thing is what we were talking about or we mentioned
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earlier the the mechanism so what's what's driving this this balanc or unbalanced uh burn rate and and I think
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well if you can you want so so and we have a theory so again as I said the interesting or the surprising result is
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that spending too little is bad and spending too much is too bad like both sides are too bad and you want to stay
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kind of in this optimal Center there and the question is why do you get those extremes um and our current conjecture
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is that um a lot of it comes from the fact entrepreneurs are unable to predict um demand in the future and competition
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in the future as a result they need to plan today for what will happen um and sometimes they over plan like the
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overbuild capacity which just cost them a lot of money so this is spending too much and sometimes they're under plan
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and when they uh built too little and there's a lot of demand in the market they're unable to supply and make the
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money they could have um and as a result other companies are raising the funding
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and for this entrepreneur he has other opportunities and they just decide to close the business um so this is our
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current uh kind of working Theory um and our next steps are basically to validate
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that both using the data and using theory model that we're try to explain that actually Ron mentioned a very
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important point that is fundamental which is the opportunity cost that I we think has a lot to do with
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this uh surprising result that spending too little may lead to failure because the entrepreneur might say okay I'm
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making money I'm not losing money but I'm spending so much time I have a
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probably a lot of Education a lot of connections I might use that in another business and that is something that we
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conjecture is at the core of our of our result great well that's it for now um
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thank you very much for joining us thank Youk you very much for having us