{"id":339904,"date":"2017-05-03T07:30:48","date_gmt":"2017-05-02T23:30:48","guid":{"rendered":"http:\/\/techcrunch.com\/?p=1484477"},"modified":"2017-05-03T07:30:48","modified_gmt":"2017-05-02T23:30:48","slug":"quantifying-startup-funding-inequality-for-fun-and-profit","status":"publish","type":"post","link":"https:\/\/people.utm.my\/asmawisham\/quantifying-startup-funding-inequality-for-fun-and-profit\/","title":{"rendered":"Quantifying startup funding inequality for fun and profit"},"content":{"rendered":"

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Jason Rowley<\/span> Contributor<\/span><\/p>\n<\/p><\/div>\n

Jason Rowley is a venture capital and technology reporter for Crunchbase News<\/a>.<\/p>\n

More posts by this contributor:\n<\/div>\n<\/div>\n

Income inequality and disparity in resource distribution have drawn a lot of attention recently in the United States. Today, we\u2019ll investigate a variation on the theme, exploring the distribution of a specific resource, venture capital funding, across a particular population, in this case, all U.S.-based technology companies founded in or after 2003 with at least one recorded funding round.<\/p>\n

It\u2019s intuitive that the majority of startup funding, in dollar terms, goes to a small number of companies. You can verify that by observing the difference<\/a> between the large number of early-stage rounds closed, and their\u00a0comparatively modest share of aggregate investment during any period.<\/p>\n

But we are not content to merely declare<\/a> that the majority of funding goes to a minority of companies. Today we will quantify it using a few\u00a0standard analytical tools that will help us better understand the\u00a0extent to which private-investment resources are distributed unequally among our cohort of startups.<\/p>\n

Lines and curves and coefficients, oh my!<\/h2>\n

Despite being over a century old at this point, the tools we will use here\u00a0are still the gold standard for measuring inequality. And what are the tools? Lorenz curves and Gini coefficients. Before we get to startup funding and its inequality \u2014 which, of course, is more feature than bug \u2014 let\u2019s understand our curves and coefficients.<\/p>\n

(Note to readers: If you have a good working knowledge of basic economic metrics, you can probably skip this section.)<\/p>\n

Lorenz curves, which display the distribution of income or wealth in an economic system, are a way to \u201ctake account simultaneously of changes in wealth and changes in population\u201d according to a paper Max O. Lorenz published in 1905<\/a>.<\/p>\n

A Lorenz curve is displayed as coordinates on a two-dimensional plane, where the X axis is the cumulative share of the population (from 0 to 100 percent) and the Y axis is the cumulative share of the income. The values plotted on that plane for the population one wants to analyze results in a Lorenz curve.<\/p>\n

Below, we\u2019ve rendered an example<\/em> of several Lorenz curves.<\/p>\n

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Typically, Lorenz curves also include a \u201cLine of Equality,\u201d a straight 45-degree line between the origin and the top-right corner of the plane. As the name suggests, the line shows what a totally equal distribution of wealth or income would look like, which serves as a point of comparison against the actual distribution of wealth displayed by the Lorenz curve.<\/p>\n

Lorenz curves can be generated for populations of very different sizes and compositions. But because the terms on which the populations are compared are the same, you can overlay Lorenz curves to make some qualitative comparisons between different populations.<\/p>\n

The more convex or \u201cbowed out\u201d the Lorenz curve, the more unequal the distribution of income, wealth or whatever else one is measuring. In the example above, the population represented by Lorenz Curve A has a more equal distribution of income than the population represented by Lorenz Curve B.<\/p>\n

Lorenz curves can be used to make qualitative comparisons just based on appearance, but what about making quantitative comparisons? In 1912, Italian statistician Corrado Gini published a paper explaining how to measure the income or wealth distribution of a nation\u2019s residents. This method takes the ratio of the area under a Lorenz curve and the area under the line of equality.<\/p>\n

Let\u2019s go back to our example diagram.<\/p>\n

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To calculate the Gini coefficient, we divide the Area A by the sum of Area A and Area B. So basically, Gini = A \/ (A+B). A Gini coefficient of 1 represents maximal inequality, whereas a Gini coefficient of 0 represents maximal equality.<\/p>\n

Now, anyone who has taken calculus knows that the ideal way to find the area under a curve is to find the integral of the function, and, in this case, the function in question is the Lorenz curve. However, real-world data does not always fit so nicely into standard regression models, so finding the function that best fits the Lorenz curve can be difficult. Accordingly, finding the integral would be difficult, as well.<\/p>\n

So, for our purposes here, we\u2019ll just take the sum of the areas under the Lorenz curve by multiplying the mean height (on the Y axis) of each data point by the width taken up on the X axis. We\u2019ll find that width by dividing 1 by the total number of data points in our sample set. (Example: For a sample set of 10 entities, the \u201cwidth\u201d of each data point would be 0.1.) Given that we\u2019ll be using relatively large data sets here, our calculations will produce a reasonably accurate approximation of the integral of each Lorenz curve.<\/p>\n

Now, back to startups.<\/p>\n

Measuring inequality in U.S. startup funding<\/h2>\n

As we mentioned earlier, Lorenz curves and Gini coefficients are typically used to measure inequality in the distribution of income or wealth within a population. We thought it would be fun to calculate these statistics for startups in the United States.<\/p>\n

We started with a data set of nearly 53,000 pre-IPO non-PE venture capital and venture debt deals struck with startups based in the United States that were founded in or after 2003, the time most consider the \u201cEra of the Unicorn<\/a>.\u201d Importantly, this data set did not include companies from sectors like life sciences, oil and gas, energy and others with very high upfront startup costs or non-standard early-stage funding practices.<\/p>\n

We then aggregated the deal data around companies\u2019 unique IDs listed in the data set. This produced a list of nearly 24,700 companies and the total amount of funding recorded for each of them in Crunchbase\u2019s database. We ranked the companies in descending order by the amount of total funding. We then calculated the percentage share of the population that company represents and the share of total funding each company has received.<\/p>\n

We then plotted the data points on a chart to produce a Lorenz curve and a line of equality:<\/p>\n

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For investment data from ~24,700 companies, we found that the Gini coefficient is a whopping 0.836, according to our calculations.<\/p>\n

If that\u2019s the result for the entire country, what if we broke it out by metropolitan region, specifically the two largest (in terms of startup funding) and the rest of the country?<\/p>\n

Out of the nearly 24,700 companies in our analysis, here\u2019s how it broke down:<\/p>\n