Here’s another article I want to recommend strongly. It’s an op-ed in the New York Times by Raj Chetty, John N. Friedman and Michael Stepner on the stark differences in the present recession between “high-income workers” and “low-income workers”. Here’s a snippet:
Policymakers are debating new stimulus payments. As they do so, we are able to track the effects of the $600 checks paid in the last stimulus bill. Within just three weeks after the checks were deposited, low- and middle-income households immediately spent a significant portion of the money they received. But our early estimates show that households making more than about $75,000 per year have spent less than $45 out of the $600.
The staggered nature of the current stimulus payments — $600 in early January, $1,400 now under consideration — illustrates how this real-time data can aid policymaking. Based on our analysis of the impacts of the $600 checks, we predict that sending higher-income households $1,400 would cost the government $200 billion — but generate only $15 billion in immediate economic activity. Hence, targeting stimulus checks to lower- and middle-income households and using the money that is saved for programs to support those who need the most help is likely to yield greater economic benefits.
That passage and, indeed, the entire piece just filled me with ideas. For example, helping the needy isn’t as simple as voting Democratic rather than voting Republican. It doesn’t just require data but being open to the direction in which the data lead.
In the piece the authors call for more real-time data-based policy-making:
When politicians lack clear answers to such questions, policy choices often rely on intuition and political ideology rather than scientific evidence. The indicators that we currently use to guide policy decisions — statistics like gross domestic product and unemployment rates — are collected by surveying businesses and households. They provide vital information about the economy as a whole but are often insufficient to guide policy in the moment. The surveys take weeks or months to gather and release. Moreover, they are limited in how finely you can zoom in on the experiences of specific cities or subgroups of Americans.
But we live in the age of information, where virtually all economic transactions leave a digital trail — from credit card receipts to paychecks to loans. These data are routinely used by companies and financial analysts to make better business decisions. And when the same data are put in the hands of the public, they can be used to guide our most important policy decisions, too.
without recognizing that the actual conclusion to which that drives one is that what is needed is more pragmatic rules-based policy-making that won’t be skewed by politics.
There’s another problem. The data sources used by the authors capture at most 85% of the economy. It doesn’t include the cash economy at all and that’s a sizeable chunk of total economic activity.
What that tells us is that any policy conclusions one might reach based on their data will be wrong because the data they’re using just aren’t as accurate as they might wish and that they are missing a lot.






