CAREER: Macroeconomic Policies: from Optimal Government Transfers to Regulating New Technologies

Government policy plays a key role in modern economies. In the short-term, governments help stabilize business cycles. Fiscal transfers, such as stimulus checks, have recently become an important tool in alleviating US recessions.

Two projects funded by this award quantify how large stimulus transfers should be and to what extent they stabilize regional business cycles in a fiscal union. Over longer periods of time, governments are responsible for regulating new technologies and managing their consequences. New challenges brought to the fore by digital and automation technologies motivate the remaining three projects funded by this award.

The projects investigate the misuse of artifical intelligence to support surveillance states, the optimal regulation of digital industries where data can lead to concentration, and how governments should manage episodes of labor reallocation where new technologies displace workers. By informing policy, these projects will benefit disadvantaged populations in the US who are disproportionately impacted by recessions and automation, foster US national security interests and democratic stability, and help ensure the US remains a leader in the digital industries of the future.

The educational component of this award will disseminate the research findings to policymakers and journalists, as well to graduate students through a tutorial ran by the National Bureau of Economic Research.

The projects funded by the award advance our understanding of core issues in macroeconomics, but also connect to broader questions in political economy, industrial organization, and labor economics. The first project recognizes that households' marginal propensity to consume out of a stimulus transfer varies with its size. A key determinant of such size-dependence is the durability of goods.

The project develops a state-of-the art model of durables demand, calibrates it to match key moments in US micro-data, and uses it to quantify the optimal size of stimulus transfers. The second project applies a semi-structural methodology for policy counterfactuals to state-level US data to construct a US economy without fiscal integration. The third project collects global data on facial recognition AI trade.

It documents new facts about US and Chinese exports of this surveillance technology to autocracies and democracies. The fourth project builds a model of the life-cycle of oligopolistic industries, such as digital industries where data is a key input. The equilibrium features an initial firm entry phase, followed by a shakeout and later industry concentration.

The model is calibrated to match US data on digital industries and is used to study optimal industry regulation. The last project begins from the observation that worker displacement is a common feature of many episodes of labor reallocation, such as those induced by automation or the transition to clean technologies. Displaced workers face reallocation and borrowing frictions in such episodes.

The project develops a heterogeneous agents model which incorporates these frictions. It uses it to study second best policies that slow down technological adoption or help worker reallocation.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.