Are earnings differences between males and females due to discrimination? A typical approach is to compare the earnings of women to that of men and try to control for typical understandable differences such as education level and location and other factors. Perhaps education levels and location interact and an interaction term is introduced into our model. However, the largest assumption is that once we define our variables, homogeneity rules, that education is homogeneous, e.g., HS graduation means the same for all groups, over all times and locations). I see Autor’s lecture pointing out this heterogeneity and disputing the assumption that all persons are products of the same data generating process. He takes this on and at least for me smashes my initial biases. To be fair, this is my reading of his efforts, he does not utter the word heterogeneity at all, but I don’t think he needed to, not everyone in the audience are econometricians and the implicit heterogeneity problem is taken on directly. I will be sharing this lecture with my data analytic students as a great example of exploratory data analysis that allows a masterfully told story through complex preparation and easy to understand visuals.
The Richard T. Ely lecture at the 2019 American Economic Association meetings was presented by David H. Autor (MIT, NBER) comparing Work of the Past with the Work of the Future. Motivated in part by the “remarkable rise of wage inequality by education,” “vast increase in supply of educated workers,” and a “key observation (of the) polarization of work” that while “most occupational reallocation is upward,” “the occupational mobility is almost exclusively downward” for non-college workers, Autor proceeds to give rise to answers to the questions surrounding
- Diverging earnings and diverging job tasks
- The changing geography of work and wages
- The changing geography of workers, and
- What and where is the work of the future.
The visual presentation makes his data exploration very understandable and are masterfully done. He truly paints a picture that emerges from a vast amount of data that is entertaining and informative. This is well worth the 47 minutes and may actually challenge your preconceived thinking as to the nature of inequality in earnings. It is not as simple as one may think and he perfectly illustrates without ever uttering the word that data heterogeneity when ignored leads to false and inescapable conclusions.
Click on the above image and you will be well rewarded if you want to see a story told with strong graphics, proving to me anyway, that deep diving into data and presenting simple graphics (although complex in their creation) is a most effective way to communicate. A couple of examples of the graphics:
What if we do an econometric analysis of earnings between men and women using current data and a similar analysis from the 1980s. Can you see how this graph as one of many in Autor’s presentation might create havoc in comparing the result in the 1980s to one current? Watch the presentation, plenty of more visuals like this one.
