Monetary Policy, Firm Heterogeneity, and the Distribution of Investment Rates
What this paper finds — and why it matters
Layer 1: Overview
Research question and motivation. Investment is a sizable and the most volatile component of aggregate GDP, so understanding the investment channel of monetary policy matters for policymakers. Prior work has overwhelmingly studied the effect of monetary policy on the average investment rate. But an estimated average effect can reflect either a uniform rightward shift of the entire distribution (all firms invest a bit more) or a change in the shape of the distribution (a few firms invest a lot more). The paper asks: how does monetary policy reshape the cross-sectional distribution of firm investment rates, and what does that reveal about the frictions driving (heterogeneous) transmission?
Data and empirical strategy. Quarterly firm-level data from Compustat, sample 1986Q1–2018Q4, U.S. nonfinancial firms (financial firms, foreign firms, and firms with incomplete/questionable data excluded). Firm age is merged from WorldScope and Jay Ritter’s database. Accounting capital stocks are converted to real economic capital via a Perpetual Inventory Method (building on Bachmann and Bayer 2014). The investment rate is real capital expenditures (CAPX) net of sales of property/plant/equipment (SPPE), deflated and divided by the lagged real capital stock. The firm-level data are aggregated into quarterly investment-rate distributions and moments. Identification uses monetary policy shocks from the Gertler and Karadi (2015) Proxy SVAR (re-extracted with updated VAR data and high-frequency instruments). Estimation is via two-step quantile/bin local projections (eq. 1), with quarter dummies for seasonality and Newey-West standard errors. Shocks are scaled to reduce the 1-year Treasury yield by 25 basis points (100bp in some distribution figures for readability). As a validity check, an expansionary shock produces hump-shaped increases in investment (peak 1.4%) and GDP (peak 0.35%).
Main findings (three facts). Fact 1: An expansionary shock changes the shape of the distribution — fewer zero and small investment rates and more large ones. The 75th percentile responds significantly more than the 25th (the interquartile range rises significantly); the share of firms in bins [0,2) and [2,4) falls significantly while higher positive bins rise, most sizably in bin [28,infinity); negative investment rates are not meaningfully affected. The spike rate (share with investment rate >10%) rises and the inaction rate (|i|<0.5%) falls. Fact 2: These shape changes are more pronounced and statistically significant among young firms (defined as less than 15 years old) than old firms; spike rates rise more and inaction rates fall more for young firms. These effects persist even among firms unlikely to be financially constrained (low leverage, high liquidity, or dividend payers), arguing against a purely financial explanation. Fact 3: A decomposition (eq. 3) into extensive vs. intensive margins shows the extensive margin accounts for around 60% (intensive 40%) of the effect on the average investment rate, and around 60% (intensive 40%) of the heterogeneous average effect across age groups.
Model and mechanism. The authors build a general-equilibrium New Keynesian heterogeneous-firm model with fixed and convex capital adjustment costs, maintenance investment, and firm entry/exit (life cycles), in the spirit of Khan and Thomas (2008) and Winberry (2021). Calibrated to U.S. data (quarterly, beta=0.99), it replicates all three facts. Fixed costs generate lumpy investment and an extensive-margin channel: an interest-rate cut raises the discounted benefit of investing, inducing some firms to switch from inaction to a sizeable investment. Young firms are on average farther from their optimal capital (higher marginal product of capital under decreasing returns), so they are induced to invest more easily — generating heterogeneity without any financial friction. This implies observational equivalence with the financial accelerator, but with opposite cyclicality: fixed costs imply procyclical policy effectiveness, whereas financial acceleration implies countercyclical effectiveness.
Aggregate/policy implications. Monetary policy is most effective when many firms are “close to paying the fixed cost.” The decline in business dynamism / firm aging since the 1980s has made monetary policy about 12% less effective at stimulating investment; policy is also less effective in recessions than booms (about 22% more effective in a large boom than a deep recession).
Layer 2: Deep Dive
What is the identification strategy and what are the main threats to it?
The authors use exogenous monetary policy shocks from the Gertler and Karadi (2015) Proxy SVAR, re-extracted after updating both the VAR time-series data and the high-frequency (high-frequency surprise) instruments. These shocks are fed into two-step local projections: in the first step they construct time series of distributional objects (quantiles, interquartile range, the share of firms in each investment-rate bin, the spike rate, the inaction rate); in the second step (eq. 1) they regress the h-period change in each object on the shock, with calendar-quarter dummies to absorb seasonality and Newey-West standard errors for heteroskedasticity and autocorrelation. The validity check is that the shocks produce plausible hump-shaped aggregate responses (investment peak 1.4%, GDP peak 0.35%). The key threats are the standard ones for high-frequency-identified monetary shocks (the shock series being a valid instrument / external to the outcome) and the aggregation step; the paper does not run firm-level panel regressions with firm fixed effects here but instead works on aggregated distributional time series, so threats relate to the time-series identification of the GK shocks rather than firm-level confounding.
What are the main mechanisms and how are they distinguished empirically?
Two margins: the intensive margin (firms changing the size of investment conditional on adjusting) and the extensive margin (firms changing whether to invest at all). Empirically they are separated via the decomposition in equation (3), which classifies observations into spikes (i>10%) and normal (i<=10%) and writes the average rate as the spike fraction times the conditional spike rate plus the complementary term. The extensive-margin component isolates the change in the average rate coming only from changes in the spike rate; the intensive component isolates changes in conditional investment rates. Two covariance terms are dropped as negligible. The shape change in the distribution (fewer small, more very-large investments, negatives unaffected), plus the rising spike rate and falling inaction rate, are the empirical fingerprints of the extensive margin. The decomposition attributes about 60% of the average effect to the extensive margin.
What heterogeneity is documented?
Heterogeneity by firm age (young = less than 15 years old, old = 15+). Young firms show larger and more statistically significant shape changes (bigger drop in bin [0,2), bigger rise in bin [28,infinity)), larger spike-rate increases, and larger inaction-rate declines. The disproportionate right-tail (upper-quantile) response holds in both groups but is much more pronounced for young firms. The extensive margin explains roughly 60% of the young-vs-old gap in average effects. Appendix C reports similar but quantitatively weaker results when comparing small vs. large firms instead of young vs. old. The heterogeneous age effect survives within groups unlikely to be financially constrained (low leverage, high liquidity, dividend payers) and is also present among likely-constrained firms.
How does the model decompose the heterogeneous extensive-margin effect, and what is the ‘heterogeneous size effect’?
Using eq. (22), the heterogeneous extensive-margin effect splits into (i) a ‘heterogeneous hazard rate increase’ — an interest-rate cut raises young firms’ hazard (adjustment probability) more than old firms’, because young firms have a higher marginal product of capital and are farther from optimal size, so the discounted benefit of investing rises more for them; and (ii) a ‘heterogeneous size effect’ — among new adjusters, young firms choose higher conditional investment rates than old firms, so there would be a heterogeneous average effect even if hazard rates rose identically. Both are quantitatively important.
What role do the different adjustment costs play, and how is the model calibrated?
The model has fixed adjustment costs (random, uniform on [0, xi-bar]), convex adjustment costs (parameter phi), and maintenance investment (parameter chi). In isolation, the fixed cost generates 55% of the heterogeneous average effect and the convex cost only 29%, with the remaining 16% from their interaction (the heterogeneous size effect needs both: hazard changes require fixed costs, differing conditional rates require convex costs). Five parameters (sigma_z=0.07, k0=2.27, xi-bar=0.90, phi=2.20, chi=0.34) are fitted to five moments: standard deviation of investment rates (data 0.20 / model 0.18), average investment rate (0.12/0.13), autocorrelation of investment rates (0.38/0.38), relative size of entrants (0.29/0.29), and relative spike rate of old firms (0.40/0.40). Fixed parameters include beta=0.99, psi=0.58, theta=0.21, nu=0.64, delta=1.93% (giving a 7.7% annual aggregate investment rate), rho_z=0.95, pi_exit=1.625%, phi(Rotemberg)=90, gamma=10, Taylor inflation coefficient phi_pi=1.5, smoothing rho_r=0.75, external capital adjustment cost kappa=11.
What untargeted moments validate the model?
The model reproduces (i) firm life-cycle profiles — average investment rate highest for newborns and falling with age, decomposed into frequency of adjustment (extensive) and conditional investment rate (intensive), both higher for young firms; (ii) plausible aggregate monetary-policy responses; and (iii) the interest-rate elasticity of aggregate investment. All three investment frictions are needed for the life-cycle profiles: fixed costs generate adjustment frequencies below one, convex costs keep young firms’ conditional investment rates plausible (no instant jump to optimal size), and maintenance investment makes hazard rates decline with age.
What robustness checks are run?
Robustness to alternative quantile choices (Figure A.1); alternative spike thresholds of 8% and 12% (Figure A.8); using the spike rate vs. hazard rate to identify extensive-margin adjustments in the model (Figure A.12, very similar results); replication of heterogeneous spike/inaction effects within groups unlikely to be financially constrained (Figure A.6) and within likely-constrained firms (Figure A.7); small-vs-large firm comparison (Appendix C); and comparison of extensive-margin contributions across different shocks (aggregate TFP, wage-markup) in Appendix E.4, showing the extensive-margin contribution can differ substantially when a shock directly affects adjustment costs.
How does this paper relate to and differ from closely related prior work?
It builds on the empirical investment-channel literature (Christiano et al. 2005; Gertler and Gilchrist 1994; Ottonello and Winberry 2020; Jeenas 2023; Cloyne et al. 2023) which focused on aggregate or average investment rates; its novelty is documenting effects on the entire distribution and its moments. Against Cloyne et al. (2023), who interpret stronger young-firm responsiveness through the financial accelerator, this paper shows a non-financial friction (fixed adjustment costs) generates the same age heterogeneity — an observational-equivalence point — though it stresses its findings are ‘consistent with’ and ’not necessarily at odds with’ the financial accelerator (the intensive margin, stronger among young firms, may reflect financial acceleration). On the lumpy-investment theory side it extends Khan and Thomas (2008), Winberry (2021), Koby and Wolf (2020), Reiter et al. (2013, 2020), Fang (2023) by adding firm life cycles. Relative to contemporaneous work by Lee (2023), which examines spike rates of small vs. large firms, this paper studies young vs. old firms and the entire distribution; relative to Gourio and Kashyap (2007), who study unconditional spike-rate cyclicality, this paper studies responses to monetary shocks.
What are the policy implications and their scope conditions?
Monetary policy stimulates aggregate investment mainly because a few firms switch from inaction to sizeable investment (extensive margin), not because many firms invest a little more. Effectiveness is state-dependent: it is higher when many firms are ‘close to paying the fixed cost’ — i.e., in booms and in high-business-dynamism economies with many young, growing firms. Scope conditions/quantification: the post-1980s decline in business dynamism / firm aging has made policy about 12% less effective; the impact effect on aggregate investment is 1.44% in baseline, 1.61% (about 11.5% larger) under a high-dynamism calibration (13% entrant share, as in 1984) and 1.32% (about 8.5% smaller) under low dynamism (3.375% entrant share); policy is about 22% more effective in a large boom than a deep recession. Critically, the cyclicality direction differs from the financial accelerator: fixed costs imply procyclical effectiveness, financial acceleration implies countercyclical — a distinction that matters for policy and aligns with evidence (Tenreyro and Thwaites 2016) that policy is weaker in recessions. A key caveat from general equilibrium: a higher young-firm share does not automatically raise effectiveness, because higher investment demand raises the price of capital and crowds out investment; state dependence only arises when the price elasticity of aggregate investment is sufficiently low (as in their model).
What are the main caveats and open questions?
The extensive-margin channel cannot rationalize the entire young-old responsiveness gap — the intensive margin is also quantitatively relevant and may reflect financial acceleration. The roughly-60% extensive-margin share of the heterogeneous effect cannot be rationalized by the classical Bernanke-Gertler-Gilchrist (1999) financial accelerator, which operates on the intensive margin. The spike rate is used as an empirical proxy for the model’s unobservable hazard rate. The paper leaves open why young firms grow slowly, how the relevant frictions respond to economic policy, and how policy effects are shaped by these frictions, pointing to non-financial constraints like productivity/demand uncertainty (Jovanovic 1982; Chen et al. 2023) as further avenues.