Innovation on (government) demand?

Next week we will organize the 7th ZEW/MaCCI Conference on the Economics of Innovation and Patenting in Mannheim and the program will be great. We will have Bronwyn Hall from Berkeley and Pierre Azoulay from MIT as keynote speakers. I’m definitely looking forward to hear them speak.

Myself, I will present a new project on the relationship between public procurement and innovation. In brief the research question is the following. Continue reading Innovation on (government) demand?

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Cardwell’s Law

While reading Joel Mokyr’s newest book I came across an older paper of him, which I found very interesting. It is about what Mokyr calls Cardwell’s law*— the empirical regularity that “most societies that have been technologically creative have been so for relatively short periods”. Throughout economic history successful countries in terms of innovation and economic growth have usually lost their competitive edge pretty soon again and were overtaken by others. Continue reading Cardwell’s Law

Do Most Companies Even Try to Innovate Anymore?

This post first appeared on hbr.org (Harvard Business Review, 14 April 2017).

We are living in the age of the superstar firm. Companies like Samsung, Google, or BMW—the top players in their respective industries—are prospering. Yet economic growth remains sluggish in many parts of the world. The reason for that paradox, as the OECD has warned, is that the productivity gap between firms at the global frontier and those lagging behind has widened. Continue reading Do Most Companies Even Try to Innovate Anymore?

How effective are patents really?

Today, an interesting NBER working paper by Deepak Hegde from NYU Stern and coauthors got published:

We provide evidence on the value of patents to startups by leveraging the random assignment of applications to examiners with different propensities to grant patents. Using unique data on all first-time applications filed at the U.S. Patent Office since 2001, we find that startups that win the patent “lottery” by drawing lenient examiners have, on average, 55% higher employment growth and 80% higher sales growth five years later. Patent winners also pursue more, and higher quality, follow-on innovation. Winning a first patent boosts a startup’s subsequent growth and innovation by facilitating access to funding from VCs, banks, and public investors.

Continue reading How effective are patents really?

Innovation activity in Germany is becoming more concentrated

This is an English translation of a column I published together with my colleague Christian Rammer from ZEW on oekonomenstimme.org. A pdf version can be downloaded here.

For years investments in research and development (R&D) have shown a rising trend in Germany. In 2015 they have reached a record high of 157.4 billion euro. At the same time, however, R&D expenditures are becoming concentrated within a smaller number of actors. The share of companies that invest in innovation falls steadily. As a result, innovation activities in the economy are more unevenly distributed. This column discusses possible causes for this development. Continue reading Innovation activity in Germany is becoming more concentrated

How WWII changed family life in Soviet Russia (and probably elsewhere too)

This is an interesting paper (here is an ungated version) by Elizabeth Brainerd in the new issue of the Review of Economics and Statistics. Abstract:

How does a shock to sex ratios affect marriage markets and fertility? I use the drastic change in sex ratios caused by World War II to identify the effects of unbalanced sex ratios on Russian women. Using unique archival data, the results indicate that male scarcity led to lower rates of marriage and fertility, higher nonmarital births, and reduced bargaining power within marriage for women most affected by war deaths. The impact of sex ratio imbalance on marriage and family persisted for years after the war’s end and was likely magnified by policies that promoted nonmarital births and discouraged divorce.

Other countries had similar wartime experiences to those of Russia. But what is unique about the Soviet  context is the scale of casualties during WWII. There was an estimated number of “26 to 27 million, or roughly 13.5% of the prewar population”, victims. “The sex ratio fell dramatically for individuals born in the 1920s and reached a low of 0.60 for women born in 1924″. And this “relative scarcity of men continued to profoundly affect women’s lives: women were less  likely to marry, more likely to give birth out of wedlock, and more likely to be divorced in  the birth cohorts and regions facing the greatest shortages of men” (all quotes from the paper).

As the abstract states, the effect of male shortage was likely enhanced by subsequent Soviet family policies (p. 3):

Alarmed at the devastating population losses suffered by the country and the  declining birth rate, the Soviet government implemented the strongly pronatalist  Family Code in 1944. This legislation imposed a tax on single people and married couples with fewer than three children and expanded the child benefit program to  provide a monthly payment for all children born out of wedlock (Heer, 1977). Far from discouraging nonmarital births, the 1944 law absolved fathers of any financial or legal responsibility for children fathered outside marriage; unmarried mothers were prohibited from naming the father on the birth certificate or claiming financial support for their children. The 1944 Family Code also made the procedure for divorce  so expensive and complicated that it has been described as effectively a ‘‘prohibition on divorce’’ (Avdeev & Monnier, 2000). The high cost of divorce combined with nearly costless nonmarital sexual relations significantly increased the cost of registered  marriage relative to bachelorhood for men.

The author concludes that, although the Soviet experience was certainly unique, the results on the effect of an extremely unbalanced sex ratio on marriage and fertility are informative also for other contexts. Similar things (albeit to a lesser extent) could have been going on in other post-war societies in Europe. And there is some empirical evidence to support this hypothesis (see here and here).

Judea Pearl on Angrist and Pischke

Today, Judea Pearl commented on a new NBER working paper by Josh Angrist and Jörn-Steffen Pischke in a mail for subscribers to the UCLA Causality Blog. I think the text is too good to hide it in a mailing list though. That’s why I will quote it here:

Overturning Econometrics Education
(or, do we need a “causal interpretation”?)

My attention was called to a recent paper by Josh Angrist and Jorn-Steffen Pischke titled; “Undergraduate econometrics instruction” (A NBER working paper)
http://www.nber.org/papers/w23144?utm_campaign=ntw&utm_medium=email&utm_source=ntw

This paper advocates a pedagogical paradigm shift that has methodological  ramifications beyond econometrics instruction;  As I understand it, the shift stands contrary to the traditional teachings of causal inference, as defined by Sewal Wright (1920), Haavelmo (1943), Marschak (1950), Wold (1960), and other founding fathers of econometrics methodology.

In a nut shell, Angrist and Pischke  start with a set of favorite statistical routines such as IV, regression, differences-in-differences among others, and then search for “a set of control variables needed  to insure that the regression-estimated effect of the variable of interest has a causal interpretation” Traditional causal inference (including economics)  teaches us that asking whether the output of a statistical routine “has a causal interpretation” is the wrong question to ask, for it misses the direction of the analysis. Instead, one should start with the target causal parameter itself, and asks whether it is ESTIMABLE (and if so how),  be it by IV, regression, differences-in-differences, or perhaps by some new routine that is yet to be discovered and ordained by name. Clearly, no “causal interpretation” is needed for parameters that are intrinsically causal; for example, “causal effect” “path coefficient”, “direct effect” or “effect of treatment on the treated” or “probability of causation”

In practical terms, the difference  between the two paradigms is that estimability requires a substantive model while interpretability appears to be model-free.
A model exposes its assumptions explicitly, while statistical routines give the deceptive impression that they run assumptions-free ( hence their popular appeal). The former lends itself to judgmental and statistical tests, the latter escapes such scrutiny.

In conclusion, if an educator needs to choose between the “interpretability” and “estimability” paradigms, I would go for the latter. If traditional econometrics education is tailored to support the estimability track, I do not believe a paradigm shift is warranted towards an “interpretation seeking” paradigm as the one proposed by Angrist and Pischke,

I would gladly open this blog for additional discussion on this topic.

I tried to post a comment on NBER (National Bureau of Economic Research), but was rejected for not being an approved “NBER family member”. If any of our readers is a “”NBER family member” feel free to post the above.

Note: “NBER working papers are circulated for discussion and comment purposes.” (page 1).

Judea

Update: By now, the text has been published on the causality blog.