During my research, I found a nice mathematical formula for the number of banks in a city. Details will follow later. How well the empirical data points fit the formula can be described with a statistical metric called “R-squared”. Here are some good explainers:

Is GDP underestimated?

How big is the economy? It’s a crucial question in economics. It’s also the title of a chapter in Bankers are people, too (pages 119-122).

Gross domestic product (GDP) is a measure for economic output based on market prices. This means that unpaid (e.g. domestic) work is not included in GDP, although we find it valuable.

As consumers, we also benefit from free digital services (email, messaging apps, maps, search engines…) that didn’t exist 40 years ago.

But how much do people value these digital services?

In How should we measure the digital economy, Erik Brynjolfsson and Avinash Collis try to measure just that. They introduce ‘GDP-B’, a metric which ‘augments’ GDP with the consumer wellbeing from free stuff. The whole article is worth a read.

For example, they argue that “Facebook alone has created more than $225 billion worth of uncounted value for consumers since 2004” and that “including the consumer surplus value of just one digital good—Facebook—in GDP would have added an average of 0.11 percentage points a year to U.S. GDP growth from 2004 through 2017.”

For more on the difficulties of GDP, read Economics is hard.

Productivity data

Productivity isn’t everything, but in the long run it is almost everything.

Paul Krugman

International datasets on productivity:

EU KLEMS (‘measures of economic growth, productivity, employment, capital formation, and technological change at the industry level for all European Union member states, Japan, and the US’)



CompNet (‘micro-based competitiveness dataset for European countries, unprecedented in terms of coverage and cross-country comparability’)

Economics is hard

How are economic statistics collected? Do economic models correspond to observable reality?

In a world where markets and politicians respond strongly to things like gross domestic product (GDP) figures and economic forecasts, these are important questions. Unfortunately, discussion often jumps directly to the interpretation of new data or the output of models. Students are rarely challenged to question what the data and the models represent.

I recently came across two great articles that dig into these issues. Continue reading “Economics is hard”