Earlier this year, my Ancestry and Long-Run Growth Reading Club walked through Putterman and Weil’s “Post-1500 Population Flows and the Long-Run Determinants of Economic Growth and Inequality.” (Quarterly Journal of Economics, 2010)  Quick review: Putterman and Weil measure the ancestral origins of the world’s current inhabitants, then show that past civilization of countries’ inhabitants is a much stronger predictor of current economic prosperity than past civilization of countries’ territory.

Instead of looking at the long-run effects of places’ traits, they look at the long-run effect of tribes’
traits.  They focus on two measures: state history and years of
agriculture.  State history measures how long a country “had a
supratribal government, the geographic scope of that government, and
whether that government was indigenous or by an outside power.” 
Following previous work, they massage this measure: “The version used by
us, as in Chanda and Putterman (2005, 2007), considers state history
for the fifteen centuries to 1500, and discounts the past, reducing the
weight on each half century before 1451-1500 by an additional 5%.” 
Years of agriculture, in contrast, is not massaged.  It’s simply the
“the number of millennia since a country transitioned from hunting and
gathering to agriculture.”


Now for the punchline: Migration-adjusted measures are much more
predictive of modern GDP than raw measures.  “Not surprisingly, given
previous work, the tests suggest significant predictive power for the
unadjusted variables. However, for both measures of early development,
adjusting for migration produces a very large increase in explanatory
power. In the case of statehist, R2 goes from .06 to .22, whereas in the
case of agyears it goes from .08 to .24. The coefficients on the
measures of early development are also much larger using the adjusted
than the unadjusted values.” 

The more I explored this paper, however, the more I realize that the world’s three most populous countries – China, India, and the United States – are big outliers.  The people of China and India have great scores for state history and agriculture, but remain poor.  The people of the United States have mediocre scores for state history and agriculture, but remain rich.  If these three outliers were Grenada, Slovakia, and Botswana, I wouldn’t demur; outliers have ye always.  But the fact that the three countries with the most people manifestly deviate from Putterman-Weil is troubling to say the least.

The obvious remedy, as my last post explained, is to run a weighted regression, to place heavier weight on more populous countries, and lighter weight on less populous countries.  GMU econ prodigy Nathaniel Bechhofer volunteered to do all the legwork, and David Weil confirmed the accuracy of his output.  Specifically, Bechhofer did the following for Putterman-Weil’s Table 4, columns (2) and (6).

1. Replicate the original results.
2. Re-do the original specifications with year-2000 population weights.*

You can access Bechhofer’s unabridged results here, here, and here.  For now, though, let’s walk through the basic findings. 

Putterman-Weil’s original output for the effect of ancestral state history and absolute latitude on modern per-capita GDP:


The same output, with countries weighted by population:


Putterman-Weil’s original output for the effect of ancestral agriculture and absolute latitude on modern per-capita GDP:


The same output, with countries weighted by population:


In both cases, the original coefficients imply huge positive effects of ancestral development on modern living standards.  And in both cases, these huge coefficients actually turn negative after weighting for population.  For agricultural history, a large statistically significant positive effect transmutes into a large statistically significant negative effect.  Reviewing all of Bechhofer’s results, the signs on ancestral development are not uniformly negative, but Putterman-Weil’s dramatic positive results never re-emerge.

In stark contrast, the measured effects of sheer geography withstand population-weighting with ease.  Absolute latitude continues to have a huge positive effect.  So, to a slightly lesser extent, does being land-locked.  (See the unabridged results).

What does this all mean?  At minimum, the apparent effects of ancestral development on modern living standards are not robust.  They don’t proverbially “leap out of the data”; they hinge on the debatable assumption that every country on Earth, no matter how small, is equally informative about the causes of economic development. 

Personally, I’d go further: Despite its ubiquity in growth regressions, the equal-weighting assumption is silly.  China, India, and the United States obviously teach us more about human societies than Grenada, Slovakia, or Botswana.  And what they teach us is that ancestral greatness is not vital for modern prosperity.

* Bechhofer also tried year-1500 population weights, with very similar results.