J. Zachary Mazlish

Economics PhD Candidate at the University of Oxford

john.mazlish@economics.ox.ac.uk
jzmazlish@gmail.com

CV

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My research focuses on macroeconomics. I also write on Substack. If you would like to talk, please reach out via email or twitter.

Working Papers

We study the implications of transformative artificial intelligence for asset prices, and in particular, how financial market prices can be used to forecast the arrival of such technology. We take into account the double-edged nature of transformative AI: while advanced AI could lead to a rapid acceleration in economic growth, some researchers are concerned that building a superintelligence misaligned with human values could create an existential risk for humanity. We show that under standard asset pricing theory, either possibility — aligned AI accelerating growth or unaligned AI risking extinction — would predict a large increase in real interest rates, due to consumption smoothing. The simple logic is that, under expectations of either rapid future growth or future extinction, agents will save less, increasing real interest rates. We contribute a variety of new empirical evidence confirming that, contrary to some recent work, higher growth expectations cause higher long-term real interest rates, as measured using inflation-linked bonds and rich cross-country survey data on inflation expectations. We conclude that monitoring real interest rates is a promising framework for forecasting AI timelines.

Over the last 30 years, the correlation across emerging market countries' sovereign debt spreads is more than double the correlation in their GDP (0.67 vs. 0.33). This discrepancy suggests that movement in sovereign spreads is primarily driven by global factors, not local fundamentals. Using data for 38 emerging market countries, I confirm that global variables are far more significant — have more than an order of magnitude larger R-squared — than local variables in explaining spread movement. Further, as evidence of the importance of price of risk channels for explaining spread movement, the share of a country's debt that is held by foreign investors significantly predicts the sensitivity of the spread to global financial conditions. I then build a three-period multi-country sovereign default model. The model shows that "standard" model features alone only produce spread correlations between 0.3-0.4. Introducing either cyclical investor risk-aversion or cross-country connections in variable costs of default matches the empirical correlation of 0.67. Yet, spreads in the model remain more tightly linked to fundamentals than in the data. 

Across a large cross-country panel of surveyed macroeconomic expectations, we establish four facts about average macroeconomic expectations: 1) Less than one-year ahead expectations under-revise. 2) Two or more year ahead expectations over-revise. 3) All horizon expectations tend to be too extreme. 4) Over-revision and over-extremity increase in the horizon of the forecast. These facts hold across advanced and emerging economies, and across a host of different macroeconomic variables. We then show that despite long-term expectations overreacting more, it is short-term expectations which are most strongly associated with ”booms and busts” in investment, GDP, and the stock market. 

Research Notes

We decompose the post-1973 productivity growth slowdown into three causes: structural change (Baumol’s cost disease), input misallocation, and pure productivity effects. We do this by constructing sector-level productivity from 1947 to 2016, using the recent BEA-BLS Integrated Level Production Accounts (Eldridge et al. 2020) adjusted with our own industry-specific markup estimates. We find that Baumol’s cost disease explains ∼25% of the productivity slowdown. The magnitude of the input misallocation channel is sensitive to methodology, reflecting uncertainty about whether or not aggregate markups have risen: input misallocation can account for between 0-20% of the productivity slowdown. Finally, we also show that linear growth fits the US data better than exponential growth, though under either exponential or linear growth there has been a post-1973 productivity slowdown.