{"id":11158,"date":"2022-09-16T19:22:50","date_gmt":"2022-09-16T18:22:50","guid":{"rendered":"https:\/\/emilkirkegaard.dk\/en\/?p=11158"},"modified":"2022-09-16T19:23:14","modified_gmt":"2022-09-16T18:23:14","slug":"national-intelligence-really-is-the-best-predictor-of-economic-growth","status":"publish","type":"post","link":"https:\/\/emilkirkegaard.dk\/en\/2022\/09\/national-intelligence-really-is-the-best-predictor-of-economic-growth\/","title":{"rendered":"National intelligence really is the best predictor of economic growth"},"content":{"rendered":"

This is the sister post <\/i>to the one on Anglo Reaction\u2019s Substack<\/i><\/a>. We published a new paper of high significance, and we didn\u2019t agree about how to summarize it for a wider audience. So we decided to just post both on our respective blogs. Why not? There\u2019s no censorship here, and no lack of space. For good measure, here\u2019s a Twitter poll about which one is better.<\/i><\/p>\n

Suppose you wanted to increase economic growth in your country. I know, in the times of \u2018degrowth\u2019 mania, you may be somewhat quaint to wish for more economic growth. BUT suppose you still wanted more economic growth, what should you do? Well, is there a science that deals with these matters that we can learn from? Yes, economics. What have they learned? Well, according to some economists: not much!<\/p>\n

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There is a literature on what is called \u201crobust ambiguity<\/a>\u201d, meaning that different methods give different answers about what really promotes economic growth, and so the right position is to throw the arms up in the air and say we don\u2019t know. In contrast to this view, one might take a more standard approach. Don\u2019t free markets promote economic growth? Many people have heard of the miracle of Chile<\/a>, and that seems hard to explain away (sadly Chile is now turning to socialism again). And what about being poor to begin with? Granted, not all poor countries grow fast, but a lot of countries that grew very fast in the last couple of decades were poor to begin with, called the advantage of backwardness<\/em>. That\u2019s certainly true for the Asian tigers<\/a>, and the Big Tiger<\/a> is also growing very well since it too gave up on socialism (sort of). Going further, people familiar with intelligence research will know of findings going back a few decades that national intelligence, whichever way you measure them, predicts economic growth fairly well<\/a>. What about that? Have mainstream economists not examined this? Or have they examined it and found it wanting?<\/p>\n

Setting that question aside for now, think about the statistics of the issue. There are only 200ish countries in the world. Not all of these have available data for economic growth going back decades, usually because they are too poor or war-torn or corrupt to collect accurate government statistics. So these countries cannot be easily used in studies, as one has to fill in the missing data with guesses. And what about all the things that people have claimed cause or at least predict economic growth? There\u2019s probably several hundred such factors that have been claimed and supporter in one study somewhere. Is there a way to do a big master-study that examines them at all once and gives us a clear answer?<\/p>\n

Well, yes and no. The no answer is that you cannot plug all of the predictors into a single regression model and get reliable answers. It\u2019s simply a matter of statistics: you have more predictors than countries, and so the model cannot fit. OK, you could fit it using penalization (ridge\/lasso\/net), but this will come at a cost of more bias. Though you can’t fit a full model, you could fit some subsets, i.e., pick some of the variables by your judgement call and present those models. There are 1000s of studies of economic growth, each using different measures of GDP, different sets of countries, different sets of controls to achieve their beloved p < .05. A lot of this is just plain cheating. They tried a lot of ways to fit a model until they found one that ‘worked’. There is a smart way to get around both of these problems: Bayesian Model Averaging<\/a> (BMA). The idea is to fit a lot of models without cheating. Millions of models in fact. If you have 200 predictors, there are 2^200 possible models, which is like a lot. You can’t fit all of those, as that would take approximately forever. But fortunately, you can pick at random from this population of models and try out, say, 5 million of them. Then you rank these models by their \u2018model fit\u2019 (R\u00b2, BIC etc.), and then take a weighted mean of the best 1000 models. That is essentially what BMA does for you. Using the results from the 5 million models you fit, you could see the performance of a given predictor. Does it work well regardless of which other predictors are in the model? Does it always have the same sign i.e. direction of effect? Is it always found in the best models? If the answer to these questions is \u201cyes yes yes\u201d, then you have found a robust predictor.<\/p>\n

So, what the robust ambiguity supporters are saying is that they tried this approach and they found that nothing is robust. If one does the modeling one way, one gets one answer; if one does it another, one gets a different answer. Unsatisfactory. However, there is a catch. These people have been ignoring national intelligence in their BMA studies. That is, except for ONE paper. In 2006, Garett Jones and W. J. Schneider published this important paper:<\/p>\n