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“The Berlin Stock Exchange in Imperial Germany”

Frankly, I was surprised to see this new paper (ungated working paper version) by Sibylle Lehmann-Hasemeyer from Hohenheim and JochenStreb from Mannheim being published in the American Economic Review. The context seems to be quite specifically focussed on Germany with no immediate policy lessons for the US. And innovation is usually not the most prominent topic in the AER either. But all the better!

It’s a very interesting read (and not too technical). The main message is this:  Germany appears to be the prototype of a bank-based financial system throughout history. Compared to a market-based system—as for example in the US, where many more firms rely on issuing stocks and bonds to finance their business—this is perceived to have disadvantages when it comes to R&D investments. Banks might be too risk-averse to finance innovation at an optimal scale or might want to shield their incumbent customers from new competition. And the entire venture capital industry relies on succesful firms going public as an exit strategy. This paper now presents convincing evidence that although Imperial Germany during 1892 and 1913 was strongly dominated by large banks (Deutsche Bank, Dresdner Bank, etc) the Berlin stock market was nevertheless quite effective in providing fresh capital to startups in the high-tech industries of the time.

I will quote their conclusion here but urge you to read the whole paper:

In the decades before the First World War, Germany changed from a comparatively backward country to a global industrial leader, especially excelling in new and innovative industries such as chemicals, electrical engineering, or machine building. Until now, however, the question of how German firms were able to finance their very risky innovation activities has remained widely unanswered. This paper shows that many innovative companies used the Berlin stock exchange as a source of financing. Even more surprising is the fact that innovators were not penalized by relatively high initial returns or low first trading prices. On the contrary, innovative startups that needed equity capital to run their risky R&D projects realized comparatively high offering prices and, in the longer run, they performed no worse than more seasoned corporations. Our findings suggest that, in the decades before the First World War, the Berlin stock exchange worked as an efficient market for new technology that channeled equity funds from non-innovative firms to innovative ones.

It might therefore be misleading to interpret nineteenth-century Germany’s financial sector as the textbook example of a bank-based financial system. It is true that Germany had a well-developed banking sector with large universal banks, many small savings banks, and credit cooperatives. But the German economy could also rely on the large and efficient Berlin stock market with a market capitalization above the world average (Rajan and Zingales 2003) and, in terms of efficiency, on a par with London (Gelman and Burhop 2008; Burhop, Chambers, and Cheffins 2011). To conclude, Germany’s industrialization and innovation depended much more than previously assumed on the provision of equity capital.

P-values: Can we agree to disagree?

The p-value debate, started by the American Statistical Association (I wrote about it here), gained a lot of attention in the scientific community. Many people have commented on it. And the more I read, the more I got confused about what the correct way of inference from data should be. Continue reading P-values: Can we agree to disagree?

The origin of time preference

Time preference is one of the fundamental primitives in our economic models. It is crucial in investment decision problems were you have to incur a cost today to get a (higher) return at a later point in time. The famous “Marshmallow Test” (here is a funny Youtube video) is one of the most canonical examples of such a problem. Continue reading The origin of time preference

Causality for Policy Assessment and Impact Analysis

Here is a great introductory lecture into causal inference and the power of directed acyclic graphs / bayesian networks. It repeats a point I made earlier on this blog that big data alone, without a causal model (i.e., theory) to support it, is simply not sufficient for making causal claims. Continue reading Causality for Policy Assessment and Impact Analysis

“How Do Venture Capitalists Make Decisions?”by Paul Gompers, William Gornall, Steven N. Kaplan, Ilya A. Strebulaev

There is a new NBER working paper out which could be of interest to those working on venture capital and entrepreneurial finance. Abstract: 

We survey 889 institutional venture capitalists (VCs) at 681 firms to learn how they make decisions across eight areas: deal sourcing; investment selection; valuation; deal structure; post investment value-added; exits; internal firm organization; and relationships with limited partners. In selecting investments, VCs see the management team as more important than business related characteristics such as product or technology. They also attribute more of the likelihood of ultimate investment success or failure to the team than to the business. While deal sourcing, deal selection, and post-investment value-added all contribute to value creation, the VCs rate deal selection as the most important of the three. We also explore (and find) differences in practices across industry, stage, geography and past success. We compare our results to those for CFOs (Graham and Harvey 2001) and private equity investors (Gompers, Kaplan and Mukharlyamov 2016).

Full paper here.

This isn’t a scientific revolution

Two weeks ago I’ve been at the Annual Meeting of the Academy of Management (see this post). And you might have guessed, big data was a huge topic. There is tremendous potential for the use of data science in the business world. Continue reading This isn’t a scientific revolution

“Does Knowledge Accumulation Increase the Returns to Collaboration?,” A. Agrawal, A. Goldfarb & F. Teodoridis (2012)

In my department we organize a literature seminar every second week in which we discuss outstanding papers from the field of innovation, entrepreneurship, and economics of science. Today’s seminar was about Agrawal et al. (2013, NBER working paper version)’s empirical work testing the hypothesis of an increasing knowledge burden. And since Kevin Bryan has already nicely summarized the paper, I will just reblog this here. I have a few minor issues with the paper, namely that they exclude all papers coauthored with Soviet mathematicians and that teasing out the knowledge frontier shock from the labor market movements seems to be very difficult. But after all it’s a great read.

afinetheorem's avatarA Fine Theorem

The size of academic research “teams” has been increasing, inexorably, in essentially every field over the past few decades. This may be because of bad incentives for researchers (as Stan Liebowitz has argued), or because more expensive capital is required for research as in particle physics, or because communication technology has decreased the cost of collaboration. A much more worrying explanation is, simply, that reaching the research frontier is getting harder. This argument is most closely associated with my adviser Ben Jones, who has noticed that while team size has increased, the average age star researchers do their best work has increased, co-inventors on inventions has increased, and the number of researchers doing work across fields has decreased. If the knowledge frontier is becoming more expensive to reach, theory suggests a role for greater subsidization of early-career researchers and of potential development traps due to the complementary nature…

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Theory Horse Races

I just came back from the 76th Annual Meeting of the Academy of Management in Anaheim, California. The conference was great, not just because of the location. Lots of participants and lots of interesting sessions. Every year they have “professional development workshops”, which are a great opportunity to learn, not only for young scholars.

I attended one of those workshops called “Writing Better Theory”. We all know that the standards for how to develop theory are quite different in management science compared to economics. Therefore I was particularly interested in this session. The panel consisted of very distinguished speakers, e.g., Lorraine Eden from Texas A&M University, who is former Editor-in-Chief of JIBS. They gave good advice on how to make your arguments more concise and develop a real contribution to the theory.

But I lost some sleep over one remark by Alvaro Cuervo-Cazurra, reviewing editor of JIBS and AIB fellow (okay, I was also severely jet lagged). He urged people not to run what he called “horse races” between conflicting theories, but rather to stick to one paradigm and develop your arguments from there. I recall his point like this. If theory 1 predicts A and theory 2 predicts B, you might be inclined to conduct an empirical test, and if the data (clearly) show B you will discard theory 1 in favor of theory 2. According to Cuervo-Cazurra this is a bad idea because of two reasons:

  1. Either you encounter reviewer one who always thought that theory 1 was wrong to begin with and who will think your paper is obvious.
  2. Or, there will be reviewer two who strongly believes in theory 1 and who will find every single flaw in your study in order to reject your paper.

Take a moment to let that sink in… This strategy might actually increase your chances to publish, especially if you’re new in the profession. But running empirical tests in order to falsify theories and to discriminate between competing explanations, isn’t that exactly what science is about? What other way is there to “keep your house clean” and to prevent the accumulation of more and more theories with low predictive power?

I was too shocked to raise a question anymore. But I could have simply gotten something wrong here. After all it was early in the morning. Anyways, I would love to hear your thoughts on this!