## wpiea2020160-print-pdf ## Source details **Canonical URL:** [wpiea2020160-print-pdf](https://www.imf.org/-/media/files/publications/wp/2020/english/wpiea2020160-print-pdf.pdf) ## Other formats - [Markdown version](/-/media/files/publications/wp/2020/english/wpiea2020160-print-pdf.pdf.md) - [Structured JSON version](/-/media/files/publications/wp/2020/english/wpiea2020160-print-pdf.pdf.json) --- ### Data and measurement of tangible and intangible investment - Firm-level data: quarterly financial statements from Compustat covering 1991–2016. - Sample after exclusions: 8,938 unique firms and 318,305 firm-quarter observations between 1991 and 2016. - Tangible investment and capital: CAPX and PPENT. - Intangible investment (Peters and Taylor, 2017): R&D expenses + 30% of SG&A expenses. - Intangible capital stock: capitalized using depreciation rates from Li and Hall (2016) plus on-balance sheet intangibles (mostly goodwill); annual series interpolated to quarterly. - Robustness alternative: Ewens et al. (2019) intangible capital stock (acquisition-price based). - Exclusions: financial firms (SIC codes 4900 - 4999), utilities (SIC codes 6000 - 6999), government (SIC codes 9000 and above), firms with missing/negative assets or sales, negative CAPX/R&D/SG&A, and firms with physical capital under $5 million. - All data deflated using the CPI and expressed in real 1990 U.S. Dollars. - Aggregate data: BEA NIPA quarterly series. - Total investment: total non-residential fixed investment, split into tangible (structures and equipment) and intangible (intellectual property products: R&D, software, artistic originals). - Other variables: - Firm controls from Compustat: firm age, Tobin’s Q, leverage, cash holdings, cashflows, firm size, dividend dummy. - Stock returns from CRSP mapped to Compustat. - Macroeconomic controls from FRED: 1-year Treasury rate, CPI, industrial production, employment ratio. - Financial conditions control: excess bond premium from Gilchrist and Zakrajšek (2012). ### Dynamics of tangible vs intangible investment (empirical patterns) - Compustat vs BEA: - Compustat shows higher intangible-to-total asset and investment ratios than BEA (Compustat focuses on large technological firms and uses a broader intangible definition). - Both datasets display a similar upward trend in intangible investment and capital stock. - Volatility and cyclicality: - Intangible investment is less volatile than physical investment in both datasets. - Within Compustat, R&D is somewhat more volatile than SG&A, but SG&A is substantially less cyclical than physical investment. - The pattern suggests intangible investment may respond less to macro shocks such as monetary policy. ### Stock price response to monetary policy shocks (Section 3.1) - Identification: - Monetary policy shocks identified from high-frequency changes in Fed Funds futures in the 30-minute window around FOMC announcements (1991–2016), following Kuttner (2001) and related literature. - Excluded the FOMC meeting on September 17, 2001. - Regression specification (overview): daily stock return ∆RET_it regressed on ∆FF4_t, firm characteristics X_it, interactions ∆FF4_t × X_it, with 4-digit NAICS industry × event-date fixed effects, optional firm fixed effects, and fiscal-quarter fixed effects. Abnormal returns computed via CAPM over 100 days prior. - Main empirical findings: - Average stock price response: −4.36% to a 1% unexpected increase in the Fed Funds rate (Column 1). - Interaction of monetary shock with intangible-to-total capital ratio: coefficient stable between 1.42 and 1.53 across specifications, consistently significant at the 5% level. - Interpretation: firms with higher intangible ratios have stock prices that react less negatively to monetary policy surprises. - Quantification: a one-standard deviation increase in the intangible ratio is associated with a 45bp smaller stock price decline in response to a 1% unexpected increase in the Fed Funds rate. - Using abnormal returns: interactions of ∆FF4 with other firm characteristics become insignificant or marginal, but the interaction with the intangible ratio remains significant — implying the intangible effect is not driven by systematic risk differences. - Robustness: - Jarocinski and Karadi (2020) decomposition into “pure” monetary policy shocks and “central bank information shocks”: - Pure monetary policy shocks affect stock prices negatively; central bank information shocks affect stock prices positively. - Interaction of intangible ratio with pure monetary shocks is positive (point estimates slightly higher than baseline); interaction with information shocks is statistically insignificant. - Alternative intangible capital measure (Ewens et al., 2019): interaction estimates similar to baseline. ### Investment response to monetary policy (Section 3.2) - Identification and strategy: - Monetary stance measured by the 1-year Treasury rate; instrumented with cumulative FF4 monetary policy shocks (cumulative FF4 is a strong instrument; first-stage F-stat > 37). - Dynamics traced with LP-IV (local projections with instrument). Aggregate regressions include macro controls and calendar quarter fixed effects; firm-level regressions include firm fixed effects, fiscal-quarter fixed effects, firm controls, and allow interaction terms with firm characteristics. - Verification: responses of macro variables to a 25bps increase in instrumented 1-year Treasury rate: - CPI drops by 0.2-0.3% (vs −0.15% in Gertler and Karadi (2015)) - Employment drops by 0.2% (similar to −0.25% in Cloyne et al. (2018)) - Excess bond premium increases by 10 bps - Industrial production drops by 0.7% (similar to 0.6% in Cloyne et al. (2018)) - Aggregate business investment drops by 2% (in line with Cloyne et al. (2018)) - Firm-level investment measures (investment rates): - I_tan = CAPX_t / PPE_{t−1} - I_int = (R&D_t + 0.3×SG&A_t) / K_int_{t−1} - I_tot = (CAPX_t + R&D_t + 0.3×SG&A_t) / (K_int_{t−1} + PPE_{t−1}) - Investment rates winsorized at the 1% level. - Firm-level impulse responses to an instrumented 25bp increase in the 1-year Treasury rate: - Total investment rate drops by 2% after 8–12 quarters. - Heterogeneity by firm intangible ratio (median split): - Firms with below-median intangible ratio: total investment reduces by up to 3% after 10–12 quarters. - Firms with above-median intangible ratio: total investment reduces by less than 1%. - Component responses within firms: - Tangible investment declines by about 4% after 12 quarters. - Intangible investment declines by only 0.7%. - Ratio responses: - The tangible-to-intangible investment ratio falls by up to 3% (statistically different from zero at 95% confidence). - CAPX-to-R&D ratio shows a similar response. - Interaction local projections (equation 4): - Interaction between intangible ratio and 1-year Treasury rate positive and statistically significant at horizons h = 8 and h = 12 quarters. - A one-standard deviation increase in the intangible-to-tangible asset ratio reduces a firm’s total investment response to a 25bp increase in the 1-year Treasury rate by about 40bp — about a fifth of the average investment response of 2%. - Aggregate (BEA NIPA) evidence: - In response to an instrumented 25bp increase in the 1-year Treasury rate: - Tangible investment (structures and equipment) declines by about 2.2% after 12 quarters (panel A). - Intangible investment (IPP) declines by 0.75% (panel B). - Total investment declines by less than 2% (panel C). - The tangible-over-intangible ratio drops by 1.5% (panel D), indicating a statistically significantly stronger response of tangible compared to intangible investment. - Aggregate results are consistent with firm-level findings: intangible investment responds less to monetary policy than tangible investment. ### Mechanisms: credit, depreciation, and adjustment cost channels (overview) - Candidate channels for muted intangible response: 1. Credit channel: intangible assets have lower collateral value; monetary tightening reduces collateral values and tightens external finance more for tangible firms. 2. Depreciation channel: intangibles have higher depreciation rates; higher δ makes user cost of capital less sensitive to interest rate changes. 3. Adjustment cost channel: intangible investment may be costlier and more irreversible to scale up/down. - Summary finding: the paper offers evidence consistent with the credit and depreciation channels; existing indirect evidence cannot support the adjustment cost channel. ### 4.1 Credit channel — model, predictions, and evidence - Model and mechanism: - Collateral constraint: D_t ≤ (1−μ) Q_t(r_t) K_t, where Q_t(r_t) is collateral value of capital and Q′_t(r_t) < 0, and μ is the share of capital that is intangible (empirical counterpart: intangible-to-total assets ratio). - For constrained firms: I_t = (1−μ) Q_t(r_t) K_t + A_t, with dI_t/dr_t = (1−μ) Q′_t(r_t) K_t. Investment declines in the interest rate less for firms with higher μ. - Testable predictions: - (1) Firms with a higher ratio of intangible assets adjust their investment less in response to monetary policy. - (2) This muted response operates primarily for financially constrained firms. - Measuring financial constraints: - Two approaches used: firm characteristics correlated with constraints (e.g., low firm age; high cash holdings) and textual analysis (delaycon measure of Hoberg and Maksimovic (2015)). - Correlation between different measures of financial constraints is modest at about 0.12-0.16. - Stock price response (empirical splits): - Age-based split: - Young firms (lowest age tercile): coefficient estimates between 3.08 and 3.64 (over twice the full-sample estimate ~1.5). Implied effect: a one standard deviation increase in the intangible ratio leads to a 0.9-1.1% smaller stock price decline to a 1% unanticipated Fed Funds increase (compared to 0.45% in full sample). - Old firms (top age tercile): coefficient estimates between 0.25 and 0.7; smaller and statistically insignificant. - Cash-based split: - High cash holdings (top cash-to-assets tercile): coefficient estimates between 2.55 and 2.78; higher than full-sample and significant at the 1% level. - Low cash holdings (bottom two terciles): coefficients substantially smaller and statistically insignificant. - Textual measure split (delaycon): - Constrained firms (above-median delaycon): coefficient estimates between 2.25 and 2.66, larger than full-sample estimates. - Difference between constrained and unconstrained firms is strongest for abnormal returns—with up to 9 times different coefficient estimates. - Investment response: - Interaction coefficient of intangible ratio with instrumented 1-year Treasury rate at horizons h = 8 and h = 12 quarters is at least twice as high for constrained firms compared to unconstrained firms. - Aggregate ratios: average ratio of tangible over intangible investment is 0.43 among intangible firms and 4.65 among tangible firms (Table 1). - Aggregate impulse responses by age (Figure 7): - Among old firms: tangible minus intangible growth difference reaches 3% after 10 quarters. - Among young firms: exceeds 6% after 12 quarters and almost 8% after 15 quarters. - Difference-in-difference reaches 3-5% and is statistically significant throughout. - Conclusion: tangible investment reacts more to monetary policy, about twice as strongly among financially constrained firms. - Borrowing response: - Aggregate debt growth response: - Full sample: debt growth declines by 0.35 percentage points after 10 quarters (mean debt growth is 2.9%). - Intangible firms: decline by 0.2 percentage points after 10 quarters. - Tangible firms: decline by 0.5 percentage points after 10 quarters. - Local projections with interaction: coefficient estimates larger among young firms and among firms with above-median delaycon. - Conclusion: firms with more intangible assets reduce debt growth less in response to monetary tightening; differential larger for credit constrained firms. ### Depreciation channel — rates, gaps, and evidence - Depreciation facts: - BEA estimates R&D capital depreciation rates to be 10-40% depending on the industry (Li, 2012). - Ewens et al. (2019) estimate an average R&D capital depreciation rate of 32%. - Average tangible capital depreciation rate in the BEA data is under 10%. - Mechanism: in a neoclassical model F′(K_t) = r_t + δ; a higher δ reduces ∂I_t/∂r_t in magnitude, making user cost less interest-rate sensitive for high-δ assets. - Depreciation gap: - Defined as difference between intangible and tangible capital depreciation rates. - Depreciation gap in sample varies from 6% to 40%, with mean and median around 22% (Table 1). - Empirical splits by depreciation gap: - Stock price response: interaction coefficient between ∆FF4 and intangible ratio up to twice as large for firms with high depreciation gap versus low gap. - Total investment response (at 8 and 12 quarters): interaction coefficients twice as high for firms with high depreciation gap. - Aggregate evidence (Figure 8): difference in response of tangible and intangible aggregate investment to a 25bp monetary policy shock is more pronounced among firms with high depreciation gaps; difference-in-difference statistically significant with 95% confidence after about 11 quarters. - Conclusion: results are consistent with a depreciation channel contributing to weaker stock price and total investment responses for firms with more intangible assets, and to a weaker intangible investment response to monetary policy. ### 4.3 Adjustment costs — features, ambiguity, and indirect evidence - Features raising adjustment costs for intangibles: - Intangible assets are more often built than purchased, firm-specific, not easily redeployable. - Creation takes more time; downsizing is costlier and more irreversible. - Dependence on highly skilled human capital makes hiring/firing costly. - R&D projects involve sequential and irreversible outlays, implying high adjustment costs and long time to build. - Peters and Taylor (2017) document that intangible capital adjusts more slowly than physical capital. - Ambiguous net effect of adjustment costs on monetary transmission: - Mechanically, high adjustment costs can mute investment responses (especially short-run). - Uncertainty channel: high adjustment costs make investment more sensitive to uncertainty; monetary policy shocks raise uncertainty (correlation between interest rate shocks and VIX is 0.12 and significant at the 0.1% level). - Net effect ambiguous because restrictive policy raises uncertainty (dampening investment) while accommodative policy reduces uncertainty (supporting investment). - Data limitations and indirect testing: - Firm- and industry-level measures of adjustment costs available focus on tangible assets (e.g., Kim and Kung (2017); Hall (2004)). - Redeployability is negatively correlated with depreciation gaps; Kim and Kung (2017) redeployability and Hall (2004) adjustment costs have a low negative correlation. - Because direct measures for intangible adjustment costs are unavailable, the paper conducts indirect tests using tangible adjustment-cost measures and extrapolates. - Empirical indirect test: - Figure 9 splits show that more inert tangible investment—defined as below-median asset redeployability (panel A) or above-median investment adjustment costs (panel B)—responds, if anything, more to monetary policy. - Interpretation: consistent with an uncertainty channel; investment with high adjustment costs responds to changes in uncertainty induced by monetary policy. - Extrapolation implies intangible (more inert) investment would be expected to respond more, not less, to monetary policy; thus the adjustment-cost explanation cannot account for the weaker response of intangible investment. - Conclusion: indirect evidence does not support the adjustment cost channel as the driver of the muted intangible response to monetary policy. *Source: wpiea2020160-print-pdf* ### Section 4 presents evidence on the credit, depreciation, and adjustment cost channels, and Section 5 concludes. ### Section 4 presents evidence on the credit, depreciation, and adjustment cost channels, and Section 5 concludes. ### Data and measurement of tangible and intangible investment - Firm-level data: quarterly financial statements from Compustat covering 1991–2016. - Sample after exclusions: 8,938 unique firms and 318,305 firm-quarter observations between 1991 and 2016. - Tangible investment and capital: CAPX and PPENT. - Intangible investment (Peters and Taylor, 2017): R&D expenses + 30% of SG&A expenses. - Intangible capital stock: capitalized using depreciation rates from Li and Hall (2016) plus on-balance sheet intangibles (mostly goodwill); annual series interpolated to quarterly. - Robustness alternative: Ewens et al. (2019) intangible capital stock (acquisition-price based). - Exclusions: financial firms (SIC codes 4900 - 4999), utilities (SIC codes 6000 - 6999), government (SIC codes 9000 and above), firms with missing/negative assets or sales, negative CAPX/R&D/SG&A, and firms with physical capital under $5 million. - All data deflated using the CPI and expressed in real 1990 U.S. Dollars. - Aggregate data: BEA NIPA quarterly series. - Total investment: total non-residential fixed investment, split into tangible (structures and equipment) and intangible (intellectual property products: R&D, software, artistic originals). - Other variables: - Firm controls from Compustat: firm age, Tobin’s Q, leverage, cash holdings, cashflows, firm size, dividend dummy. - Stock returns from CRSP mapped to Compustat. - Macroeconomic controls from FRED: 1-year Treasury rate, CPI, industrial production, employment ratio. - Financial conditions control: excess bond premium from Gilchrist and Zakrajšek (2012). ### Dynamics of tangible vs intangible investment (empirical patterns) - Compustat vs BEA: - Compustat shows higher intangible-to-total asset and investment ratios than BEA (Compustat focuses on large technological firms and uses a broader intangible definition). - Both datasets display a similar upward trend in intangible investment and capital stock. - Volatility and cyclicality: - Intangible investment is less volatile than physical investment in both datasets. - Within Compustat, R&D is somewhat more volatile than SG&A, but SG&A is substantially less cyclical than physical investment. - The pattern suggests intangible investment may respond less to macro shocks such as monetary policy. ### Stock price response to monetary policy shocks (Section 3.1) - Identification: - Monetary policy shocks identified from high-frequency changes in Fed Funds futures in the 30-minute window around FOMC announcements (1991–2016), following Kuttner (2001) and related literature. - Excluded the FOMC meeting on September 17, 2001. - Regression specification (overview): daily stock return ∆RET_it regressed on ∆FF4_t, firm characteristics X_it, interactions ∆FF4_t × X_it, with 4-digit NAICS industry × event-date fixed effects, optional firm fixed effects, and fiscal-quarter fixed effects. Abnormal returns computed via CAPM over 100 days prior. - Main empirical findings: - Average stock price response: −4.36% to a 1% unexpected increase in the Fed Funds rate (Column 1). - Interaction of monetary shock with intangible-to-total capital ratio: coefficient stable between 1.42 and 1.53 across specifications, consistently significant at the 5% level. - Interpretation: firms with higher intangible ratios have stock prices that react less negatively to monetary policy surprises. - Quantification: a one-standard deviation increase in the intangible ratio is associated with a 45bp smaller stock price decline in response to a 1% unexpected increase in the Fed Funds rate. - Using abnormal returns: interactions of ∆FF4 with other firm characteristics become insignificant or marginal, but the interaction with the intangible ratio remains significant — implying the intangible effect is not driven by systematic risk differences. - Robustness: - Jarocinski and Karadi (2020) decomposition into “pure” monetary policy shocks and “central bank information shocks”: - Pure monetary policy shocks affect stock prices negatively; central bank information shocks affect stock prices positively. - Interaction of intangible ratio with pure monetary shocks is positive (point estimates slightly higher than baseline); interaction with information shocks is statistically insignificant. - Alternative intangible capital measure (Ewens et al., 2019): interaction estimates similar to baseline. ### Investment response to monetary policy (Section 3.2) - Identification and strategy: - Monetary stance measured by the 1-year Treasury rate; instrumented with cumulative FF4 monetary policy shocks (cumulative FF4 is a strong instrument; first-stage F-stat > 37). - Dynamics traced with LP-IV (local projections with instrument). Aggregate regressions include macro controls and calendar quarter fixed effects; firm-level regressions include firm fixed effects, fiscal-quarter fixed effects, firm controls, and allow interaction terms with firm characteristics. - Verification: responses of macro variables to a 25bps increase in instrumented 1-year Treasury rate: - CPI drops by 0.2-0.3% (vs −0.15% in Gertler and Karadi (2015)) - Employment drops by 0.2% (similar to −0.25% in Cloyne et al. (2018)) - Excess bond premium increases by 10 bps - Industrial production drops by 0.7% (similar to 0.6% in Cloyne et al. (2018)) - Aggregate business investment drops by 2% (in line with Cloyne et al. (2018)) - Firm-level investment measures (investment rates): - I_tan = CAPX_t / PPE_{t−1} - I_int = (R&D_t + 0.3×SG&A_t) / K_int_{t−1} - I_tot = (CAPX_t + R&D_t + 0.3×SG&A_t) / (K_int_{t−1} + PPE_{t−1}) - Investment rates winsorized at the 1% level. - Firm-level impulse responses to an instrumented 25bp increase in the 1-year Treasury rate: - Total investment rate drops by 2% after 8–12 quarters. - Heterogeneity by firm intangible ratio (median split): - Firms with below-median intangible ratio: total investment reduces by up to 3% after 10–12 quarters. - Firms with above-median intangible ratio: total investment reduces by less than 1%. - Component responses within firms: - Tangible investment declines by about 4% after 12 quarters. - Intangible investment declines by only 0.7%. - Ratio responses: - The tangible-to-intangible investment ratio falls by up to 3% (statistically different from zero at 95% confidence). - CAPX-to-R&D ratio shows a similar response. - Interaction local projections (equation 4): - Interaction between intangible ratio and 1-year Treasury rate positive and statistically significant at horizons h = 8 and h = 12 quarters. - A one-standard deviation increase in the intangible-to-tangible asset ratio reduces a firm’s total investment response to a 25bp increase in the 1-year Treasury rate by about 40bp — about a fifth of the average investment response of 2%. - Aggregate (BEA NIPA) evidence: - In response to an instrumented 25bp increase in the 1-year Treasury rate: - Tangible investment (structures and equipment) declines by about 2.2% after 12 quarters (panel A) — somewhat less than the 4% decline in firm-level data. - Intangible investment (IPP) declines by 0.75% (panel B) — similar to firm-level effect. - Total investment declines by less than 2% (panel C). - The tangible-over-intangible ratio drops by 1.5% (panel D), indicating a statistically significantly stronger response of tangible compared to intangible investment. - Aggregate results are consistent with firm-level findings: intangible investment responds less to monetary policy than tangible investment. ### Mechanisms: credit, depreciation, and adjustment cost channels (Section 4 preview and summary) - Candidates for why intangible investment and intangible firms react less to monetary policy: 1. Credit channel: - Firms with more intangible assets are less reliant on secured debt funding. - Monetary policy amplification via collateral value declines and tighter external finance constraints is therefore muted for intangible firms. 2. Depreciation channel: - Intangible assets have higher depreciation rates than tangible assets. - The same interest rate changes may affect the user cost of capital for intangibles proportionately less. 3. Adjustment cost channel: - Intangible investment may be costlier to scale up and down compared to tangible investment. - Evidence summary: - The paper offers evidence consistent with the credit and the depreciation channels. - Existing indirect evidence cannot support the adjustment cost channel. *Source: wpiea2020160-print-pdf - Section 4 presents evidence on the credit, depreciation, and adjustment cost channels, and Section 5 concludes.* ### 4.1 Credit Channel ### 4.1 Credit Channel ### Model and mechanism - The credit channel operates by affecting the price and volume of credit available to firms; the volume is determined by the collateral value of firm assets. - Intangible assets have lower collateral value compared to tangible assets. - Collateral constraint (equation (5)): D_t ≤ (1−μ) Q_t(r_t) K_t, where: - Q_t(r_t) is collateral value of capital and Q′_t(r_t) < 0. - μ is the share of capital that is intangible (empirical counterpart: intangible-to-total assets ratio). - Firm optimization (equation (6)): max_{I_t,D_t} F(K_{t+1}) − I_t r_t subject to K_{t+1}=K_t+I_t, I_t=A_t+D_t, and the collateral constraint. - Two solution regions: - Unconstrained firm: choose I_t such that F′(K_{t+1}) = r_t; μ does not affect investment when unconstrained. - Constrained firm: I_t = (1−μ) Q_t(r_t) K_t + A_t, with dI_t/dr_t = (1−μ) Q′_t(r_t) K_t. Investment declines in the interest rate less for firms with higher μ. ### Testable prediction from the model - (1) Firms with a higher ratio of intangible assets adjust their investment less in response to monetary policy. - (2) This muted response operates primarily for financially constrained firms. ### Measuring financial constraints - Two approaches used: - Firm characteristics correlated with constraints (e.g., low firm age; high cash holdings). - Textual analysis of financial statements (delaycon measure of Hoberg and Maksimovic (2015)). - Correlation between different measures of financial constraints is modest at about 0.12-0.16. - Results are documented using multiple measures and are consistent across them. ### Stock price response (empirical findings) - Regressions split sample into more and less financially constrained firms; dependent coefficient is on ∆FF4 × Intangible Ratio. - Age-based split (Panel A): - Young firms (lowest age tercile): coefficient estimates between 3.08 and 3.64 (over twice the full-sample estimate ~1.5 from Table 2). - Implied effect: a one standard deviation increase in the intangible ratio leads to a 0.9-1.1% smaller stock price decline in response to a 1% unanticipated increase in the Fed Funds rate (compared to 0.45% in the full sample). - Old firms (top age tercile): coefficient estimates between 0.25 and 0.7; smaller than full-sample and statistically insignificant. - Cash-based split (Panel B): - High cash holdings (top cash-to-assets tercile): coefficient estimates between 2.55 and 2.78; higher than full-sample and significant at the 1% level. - Low cash holdings (bottom two terciles): coefficients substantially smaller and statistically insignificant. - Textual measure split (Panel C): - Constrained firms (above-median delaycon): coefficient estimates between 2.25 and 2.66, larger than full-sample estimates. - The difference between constrained and unconstrained firms is strongest—for abnormal stock returns—with up to 9 times different coefficient estimates. - Overall: weaker stock price response to monetary policy among firms with more intangible assets is more pronounced for financially constrained firms. ### Investment response (empirical findings) - Investment local projection regressions split by financial constraint measures; outcomes evaluated at 8 and 12 quarter horizons. - Coefficient estimates on the interaction of the intangible ratio with the instrumented 1-year Treasury rate are at least twice as high for constrained firms compared to unconstrained firms. - Coefficient estimates for unconstrained firms have lower or no statistical significance. - Aggregate implications: - Firms with more tangible assets make more tangible investment; firms with more intangible assets make more intangible investment. - Average ratio of tangible over intangible investment: 0.43 among intangible firms and 4.65 among tangible firms (Table 1). - Since firms with more intangible assets adjust total investment less, aggregate intangible investment should react less to monetary policy, especially among financially constrained firms. - Aggregate impulse response evidence (Figure 7): - Age split (Panel A): difference in response (tangible minus intangible growth) - Among old firms: reaches 3% after 10 quarters. - Among young firms: exceeds 6% after 12 quarters and almost 8% after 15 quarters. - Difference-in-difference (Panel B): reaches 3-5% and is statistically significant throughout. - Results are consistent when splitting by cash holdings (Panels C and D) and delaycon (Panels E and F). - Tangible investment reacts more to monetary policy, about twice as strongly among financially constrained firms. ### Borrowing response (empirical findings) - Credit channel implies firms with more intangible assets adjust borrowing less in response to monetary policy. - Aggregate debt growth response (Figure 6): - Full sample: debt growth declines by 0.35 percentage points after 10 quarters (mean debt growth is 2.9%). - Intangible firms: decline by 0.2 percentage points after 10 quarters. - Tangible firms: decline by 0.5 percentage points after 10 quarters. - Local projection regressions (Table 6) with interaction of intangible ratio and instrumented 1-year Treasury rate: - Coefficient estimates are larger among young firms (panel A) and among firms with an above-median delaycon measure (panel B). - Conclusion: firms with more intangible assets reduce debt growth less in response to monetary tightening, and this differential is more pronounced among credit constrained firms. ### Depreciation rates and the depreciation channel - Intangible assets depreciate faster than tangible assets: - BEA estimates R&D capital depreciation rates to be 10-40% depending on the industry (Li, 2012). - Ewens et al. (2019) estimate an average R&D capital depreciation rate of 32%. - Average tangible capital depreciation rate in the BEA data is under 10%. - Higher depreciation rate makes user cost of capital less interest-rate sensitive: - In neoclassical model F′(K_t) = r_t + δ; a higher δ reduces ∂I_t/∂r_t in magnitude. - Define firm “depreciation gap” as difference between intangible and tangible capital depreciation rates. - Depreciation gap in sample varies from 6% to 40%, with mean and median around 22% (Table 1). - Empirical splits by depreciation gap (Table 7): - Stock price response (Panel A): interaction coefficient between ∆FF4 and intangible ratio up to twice as large for firms with high depreciation gap versus low gap. - Total investment response (Panel B, at 8 and 12 quarters): interaction coefficients twice as high for firms with high depreciation gap. - Aggregate evidence (Figure 8): - Difference in response of tangible and intangible aggregate investment to a 25bp monetary policy shock is more pronounced among firms with high depreciation gaps. - Difference-in-difference (Panel B) is statistically significant with 95% confidence after about 11 quarters. - Conclusion: results are consistent with a depreciation channel contributing to the weaker stock price and total investment response to monetary policy in firms with more intangible assets, and to a weaker intangible investment response to monetary policy. *Source: 4.1 Credit Channel (excerpt) from the supplied IMF content unit.* ### 4.3 Adjustment Costs ### 4.3 Adjustment Costs ### Features of intangible investment that raise adjustment costs - Intangible assets are more often built rather than purchased because they are firm-specific and not easily redeployable (Bates et al., 2009; Falato et al., 2018). - Creating intangible assets takes more time; downsizing intangible assets is costlier because they must be liquidated rather than sold, making intangible investment more irreversible. - Creation of intangible assets hinges on highly skilled human capital (Döttling et al., 2020); hiring and firing scarce talent is difficult and costly. - R&D projects often require sequential and irreversible outlays, implying both high adjustment costs and long time to build (Roberts and Weitzman, 1981). - Peters and Taylor (2017) document that “compared with physical capital, intangible capital adjusts more slowly to changes in investment opportunities.” ### Ambiguous net effect of adjustment costs on monetary policy transmission - Mechanically: investment with high adjustment costs may respond less to monetary policy, especially over shorter horizons, because investment is forward-looking and not easily reversible. - Uncertainty channel: investment with high adjustment costs responds negatively to uncertainty (Majd and Pindyck, 1985; Bloom, 2009) because such investment is more irreversible and uncertainty raises the risk of non-payoff. - Monetary policy shocks affect uncertainty: uncertainty responds positively to interest rate shocks (Bekaert et al., 2013). - Empirical note from the paper: the correlation between interest rate shocks and VIX is 0.12 and significant at the 0.1% level. - Implication: accommodative monetary policy reduces uncertainty and thus can foster investment with high adjustment costs; restrictive policy increases uncertainty and can dampen such investment. Therefore, due to this uncertainty effect, investment with high adjustment costs may respond more to monetary policy. ### Data limitations and indirect testing strategy - Ideal test would use firm-level measures of tangible and intangible investment adjustment costs to compute an “adjustment costs gap” analogous to the “depreciation gap,” but available firm- and industry-level measures focus on tangible assets only: - Kim and Kung (2017) firm-level estimates of asset redeployability focus on tangible assets. - Hall (2004) industry-level measures of investment adjustment costs focus on tangible assets. - Correlations reported: - Redeployability is negatively correlated with depreciation gaps (due to a positive correlation between redeployability and tangible asset deprecation rates). - Kim and Kung (2017) asset redeployability and Hall (2004) adjustment costs have a low negative correlation, suggesting they capture different dimensions of investment adjustment costs. - Because of these data constraints, the paper analyzes adjustment costs indirectly by: - Establishing the effect of adjustment costs on the response of tangible investment to monetary policy. - Extrapolating that effect to the comparison between tangible and intangible investment. ### Empirical evidence (indirect test) - Figure 9 sample splits show that more inert tangible investment—defined as: - below-median asset redeployability (panel A), or - above-median investment adjustment costs (panel B)— responds, if anything, more to monetary policy. - Interpretation: this pattern is consistent with the monetary policy effect operating through uncertainty (i.e., investment with high adjustment costs responds to changes in uncertainty induced by monetary policy). - Extrapolation: if more inert tangible investment responds more, then extrapolating to intangible (more inert) investment would suggest intangible investment should respond more to monetary policy. - Conclusion from indirect test: this evidence cannot support the notion that higher adjustment costs contribute to the weaker response of intangible investment to monetary policy. ### Connection to paper's broader findings (brief) - The paper’s key result: monetary policy impacts investment less when more of corporate investment is intangible; the stock prices and investment of firms with more intangible assets react less to monetary policy, and intangible investment reacts less to monetary policy compared to tangible investment. - Cross-sectional heterogeneity: attenuating effects of intangible assets are most pronounced among credit constrained firms and firms with a large difference between tangible and intangible asset depreciation rates. *Source: 4.3 Adjustment Costs, wpiea2020160-print-pdf* ### References ### References ### Reference list - Bahaj, S. 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