Manuel Menkhoff

Thanks a lot Rudi :)

This paper contributes to work by, among others @aauclert.bsky.social @ludwigstraub.bsky.social @ygorodnichenko.bsky.social @johanneswohlfart.bsky.social @ihaal.bsky.social @bachmannrudi.bsky.social @peterkaradi.bsky.social @joachimjungherr.bsky.social @treinelt.bsky.social

Link to VoxEU: cepr.org/voxeu/column...

Link to paper: www.dropbox.com/scl/fi/w2vdd...

2️⃣ We exploit the survey’s panel dimension: one-time vignette responses to borrowing cost changes predict post-shock investment dynamics.

Two approaches to assess the borrowing cost channel’s macro role after @ecb.europa.eu policy changes.

1️⃣Open-text narratives: we ask firms what typically comes up in investment planning when the ECB changes its key rate—when discussed, borrowing costs are top of mind.

Hurdle rates are sticky and drive the response.

𝐍𝐚𝐫𝐫𝐚𝐭𝐢𝐯𝐞𝐬 𝐟𝐨𝐫 𝐧𝐨𝐧-𝐚𝐝𝐣𝐮𝐬𝐭𝐦𝐞𝐧𝐭: Cash buffers and not being at the margin—investment is driven by capacity. Effects bite most for externally financed and labor-short firms.

A 1pp cut in lending rates lifts corporate investment by ~7% over two years. This direct response is ≈½ of the total monetary policy effect on investment.

Big heterogeneity: many firms don’t adjust; adjusters ramp up sharply.

Beyond magnitudes: the reasons why managers do (not) adjust investment typically remain a black box.

𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧: elicit managers’ narratives with open-text questions.

Identifying the direct effect of borrowing costs is difficult due to scarce exogenous variation and confounding GE forces.

𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧: use hypothetical scenarios with loan rate changes (up to 400 bp) in the great ifo survey, holding the macro environment fixed.

In new paper with @leabest.bsky.social and @bborn.bsky.social we unpack the ‘impact of interest’: how borrowing costs directly shape firms’ investment, from micro to macro.

Thanks, Miriam! :)

Thanks, Davide!

Thank you, Christina! :)

Thank you, John :)

Thanks a lot, Pierre! :)

Thank you, Lennard!

I'm also deeply grateful to everyone who supported me during the job market and to the many inspiring researchers I had the chance to meet along the way. Looking forward to the next chapter!

Huge thanks to @apeichl.bsky.social , Mirko Wiederholt, Martin Schneider, and @bborn.bsky.social for their incredible support over the past months.

I'm happy to share that I've successfully submitted my dissertation this week! I'm also thrilled to announce that I'll join the University of Copenhagen as an Assistant Professor this summer.

@econmunich.bsky.social

Thanks a lot to my advisor, co-authors, and everyone at ifo & LMU for their support along the journey! @apeichl.bsky.social @bborn.bsky.social Mirko Wiederholt, Martin Schneider @bachmannrudi.bsky.social @peterzorn.de @almutballeer.bsky.social @pschuele.bsky.social

Check out the details in my JMP: www.dropbox.com/scl/fi/7cwz8...
And explore my other research on my website: sites.google.com/view/manuelm...
#EconSky #EconJMP #JMP

What’s the role of policy?

➡️In a quantitative model, I show that fiscal policy is particularly effective in stimulating investment.
➡️Automatic stabilizers such as generous loss carrybacks mitigate decline in capital stock by around 25% when macro tail risk increases.

To explain these findings, I propose a new mechanism:

➡️A model of firm dynamics with heterogenous & uncertain exposure to macro tail events.
➡️When exposure is ambiguous, even risk-neutral firms cut back investment beyond first and second moments.

External validation:
Using newspaper articles (1986–2023) + GPT, I create a time series of macro tail risk beliefs.

➡️Macro tail risk beliefs didn’t return to pre-2008 levels after the Great Recession.
➡️Macro tail risk beliefs🔼 foreshadow weaker investment dynamics.

I corroborate the results in a survey experiment:

➡️Firms invest more in scenarios with lower tail risk, even if the mean and variance of macro outcomes are the same.
➡️Again, results are driven by high worst-case exposure firms.

Subjective probability of a macro tail event 🔼 → investment 🔽

50% of the relationship persists even when controlling for subjective expectations and uncertainty (first & second moments of firms’ forecast distribution).

Results are driven by firms with high worst-case exposure.

I also asked firms about their exposure to macro tail events:

➡️Managers are highly uncertain about their exposure.
➡️Past exposure predicts future beliefs about vulnerability.

I created novel data using the ifo survey:
Firms report subjective probabilities of 4 specific macro tail events.

➡️Firms view these events as fairly likely, but beliefs vary widely.
➡️Higher tail risk beliefs → lower expectations & higher uncertainty about their own business.