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.

Different stages of empirical research

Eventually, the job market stress comes to an end. So I thought I could start into the blogging year with a bit of humor. During the last couple of weeks I flew out to both economics and more management-oriented departments. That’s were the inspiration for this little comic came from.

state_of_empirical_cropped

Follow-up on “IV regressions without instruments” (technical)

Some time ago I wrote about a paper by Arthur Lewbel in the Journal of Business & Economic Statistics in which he develops a method to do two-stage least squares regressions without actually having an exclusion restrictions in the model. The approach relies on higher moment restrictions in the error matrix and works well for linear or partly linear models. Back then, I expressed concerns that the estimator does not seem to work when an endogenous regressor is binary though; at least not in the simulations I have carried out.

After a bit of email back-and-forth we were able to settle the debate now. Continue reading Follow-up on “IV regressions without instruments” (technical)

Innovation,unemployment and subjective well-being

These days, everybody is talking about the losers of globalization and how they made Trump and Brexit happen. People in industrialized countries lose their jobs due to offshoring and international competition, which leads them to vote for right-wing populists, so the common narrative goes. That might not be the full story though. Continue reading Innovation,unemployment and subjective well-being

Initial vs. Final Precision

Recently I have written about the fundamental divide between Frequentist and Bayesian statistics that lies at the heart of many interpretations of the p-value debate. Perhaps the biggest weapon of the Bayesian camp in the intellectual dispute is the surprising fact of how often you can actually be wrong even if you have a p-value smaller than 0.05. A quite extreme example is put forward in this article. Continue reading Initial vs. Final Precision

IV regressions without instruments (technical)

Arthur Lewbel published a very interesting paper back in 2012 in the Journal of Business & Economic Statistics (ungated version here). The paper attracted quite some attention because it lays out a method to do two-stage least squares regressions (in order to identify causal effects) without the need for an outisde instrumental variable. Continue reading IV regressions without instruments (technical)