The following comes from David Card (1995) using NLSY for men. We consider the relationship of wages (log wages) to education and other controls. Of course, there is the confounding omitted variable “ability” of which little is known that’ll make our results biased. This is such an important empirical question it is part of the active research agenda till today.
Card makes the following argument, we can assume that some of the exogenous variation that education has on earnings can come from the difference in groups which leave near 4-year institutions and those who live far away from them. I have recreated the following from Card’s paper to make the case for the 4-year college distance treatment. Particularly due to it’s effect on poor and low educated households.
This is what what I want to note ever so slightly. Where colleges exist in cities it makes a difference in the lower quantiles of education fulfilment. Intuitively this makes a lot of sense. Costs to low income households are down if their children attend local universities. Running a regression with respect to the indicator variable for nearby 4-year colleges also shows a positive relation. Thus, we have a (positive) causal relationship between years of education and nearby colleges. This has one important implication in my mind, recall from my past post I mentioned the same technology hubs over and over. They came up in Enrico Moretti’s work. These are Austin, New York, Seattle, Bay Area, etc. All of them have large and highly reputable colleges. More than that they have several large and highly reputable colleges.
Thus, when we discuss the effects of human capital externalities we can probably see from the graph above a positive generational effect. If in San Francisco day labourers are attracted to the hustle and bustle of a burgeoning city their kids will have an easier time affording San Francisco University. This might be the machinery of innovative cities so many urban planners and developmental economists discuss. Don’t take my statement as definitive proof, just food for thought.
PS I remembered this paper only because I had a chance to read it as part of my advanced econometrics class. Just goes to show you it pays to read the suggested readings no matter how overwhelming the number is. Frankly, I sometimes felt like I spent more time reading them and not enough drilling some basic concepts into my head.