Investment under Upstream and Downstream Uncertainty: Grigoris & Segal (2026)

What this is. The paper’s core results, datasets, and theory: enough to know what it found without reading all 45 pages. To replicate or extend it, use the original (paywalled) or the replication code available in the journal’s Supporting Information.

TL;DR

Using granular supplier-customer link data from Compustat Segments and FactSet Revere (1976-2019), the paper measures each firm’s upstream (supplier-level) and downstream (customer-level) uncertainty as the realized stock return volatility of its trading partners. Upstream uncertainty robustly suppresses investment, hiring, and working capital. Downstream uncertainty has a weaker and often positive effect, flipping sign for firms with long time-to-build periods. A production-based real-option model with time-to-build generates this asymmetry: upstream uncertainty raises the option value of waiting via the bad news principle, while downstream uncertainty raises the opportunity cost of waiting via the good news principle (convex future cash flows). The asymmetry scales to the macro level: macrolevel upstream (downstream) uncertainty negatively (positively) predicts GDP growth, consumption, investment, and price-dividend ratios.

Core results

Magnitudes and significance are as reported; **/*** = 5%/1%. All firm-level independent variables are scaled by their unconditional standard deviation. Locators point into the source PDF.

#	Result	Locator	Magnitude
R1	Upstream uncertainty negatively predicts firm investment	Table III col. (4), p. 438	1-SD increase: investment rate -0.03 (t = -2.80)***; firm + year FE, controls
R2	Downstream uncertainty positively predicts firm investment	Table III col. (6), p. 438	1-SD increase: investment rate +0.03 (t = 3.15)***; firm + year FE, controls
R3	Asymmetry extends to working capital, employment, COGS, and intangibles	Table IV, p. 439	Upstream: significant negative in all four outcomes; downstream: positive (working capital t=2.18-3.32) or insignificant (employment, intangibles); upstream effect weakly larger in absolute value
R4	Downstream uncertainty effect on investment is stronger for long time-to-build firms	Table V, p. 442	Long vs. short interaction: coef 0.03-0.06 (t=3.15-4.60)*** across three proxies (depreciation, sector, R&D); Wald test rejects equality at 10% in all specs
R5	Upstream uncertainty effect on investment is amplified for low-reversibility (hard-to-abandon) firms	Table VI col. (2), p. 443	LowReverse: -0.05 (t=-3.31)***; HighReverse: -0.03 (t=-1.42, insignificant)
R6	Downstream uncertainty effect on investment is stronger for high-reversibility firms	Table VI col. (4), p. 443	HighReverse: +0.05 (t=3.60)***; LowReverse: +0.02 (t=1.32, insignificant)
R7	Macrolevel upstream uncertainty shock leads to economic contraction	Figure 6, p. 448	1-SD shock: industrial production and GDP fall ~0.15 SD, consumption and investment fall ~0.10 SD; P/D ratio falls ~0.10-0.15 SD; effects persist ~4 quarters (90% CI excludes zero)
R8	Macrolevel downstream uncertainty shock leads to economic expansion	Figure 7, p. 449	1-SD shock: industrial production, consumption, investment, and GDP rise ~0.10 SD for at least 4 quarters; P/D ratio rises ~0.10 SD for ~12 quarters; upstream impacts up to 50% larger in absolute magnitude
R9	COVID-19 onset was driven by downstream uncertainty spike, consistent with fast recovery	Figure 9 / §IV.C, pp. 451-452	Orthogonal downstream uncertainty spiked in March 2020 (while upstream uncertainty also rose); downstream dominance consistent with the recession being short-lived relative to upstream-driven recessions

Overall (paper’s conclusion). Uncertainty is not uniformly contractionary: downstream uncertainty may have an expansionary impact. The asymmetry arises from the time-to-build mechanism and the real-option structure of investment, not from the magnitude of uncertainty.

Datasets used

Dataset	Role in paper	Wiki page
CRSP / Compustat (via WRDS), 1976-2019	Investment rates, firm characteristics (size, leverage, tangibility, Tobin’s q, profitability, past returns), stock return volatility	WRDS / CRSP / Compustat (licensed)
Compustat Segments database, 1976-2002	Supplier-customer links for early subsample (pre-FactSet)	no page yet
FactSet Revere Relationship database, 2003-2019	Supplier-customer links (primary source for post-2003 period; more comprehensive than Segments)	no page yet
NBER-CES Manufacturing Industry database	Validates link between input price uncertainty and supplier return volatility; upstream and downstream price correlation check	NBER-CES
BEA Input-Output (Make and Use) tables, 1977-2012	Industry upstreamness scores for macrolevel analysis; published every 5 years	no page yet
FRED (VIX, industrial production index)	VIX used in COVID-19 episode illustration; IP index as macro outcome variable	FRED

Sample: firm-year panel 1976-2019; ~17,000-50,786 observations depending on specification (Table III). Macrolevel analysis: 1976Q1-2019Q4.

Theory / model

The paper builds a production-based real-option model with time-to-build (Section II, pp. 421-434). It extends the time-to-build real-option model of Majd and Pindyck (1987) to stochastic volatility and to the supply-chain location of uncertainty. The focal firm has assets-in-place (installed capacity $k_t$, depreciating at rate $\delta$) and a growth option to expand. Operating cash flow per period (eq. 1, p. 421):

$$\pi_t = P_t^{\text{Out}} \cdot k_t^{\alpha} - \omega \cdot k_t - P_t^{\text{In}} \cdot \delta \cdot k_t$$

• $P_t^{\text{Out}}$ is the stochastic output price
• $P_t^{\text{In}}$ is the stochastic input price
• $\alpha \in (0,1)$ is returns to scale
• $\omega$ is a proportional operating cost

The last term captures maintenance (replacing depreciated inputs purchased at the current input price).

Both input and output log-prices follow mean-reverting stochastic volatility processes (eqs. 2-3, p. 422). For $j \in \{\text{In}, \text{Out}\}$:

$$\begin{aligned} p^j_{t+1} &= \rho_p \, p^j_t + \sigma_p \exp\!\left(\sigma^j_t / 2\right) \epsilon^j_{t+1} \\ \sigma^j_{t+1} &= \rho_\sigma \, \sigma^j_t + \sigma_w \, \eta^j_{t+1} \end{aligned}$$

• $p^j_t = \log(P^j_t)$; innovations $\epsilon$ and $\eta$ are i.i.d. standard normal
• $\rho_p$ governs price persistence
• $\rho_\sigma$ governs volatility persistence
• $\sigma_w$ governs the volatility of volatility

The firm’s recursive Bellman equation (eq. 4, p. 422), choosing future capacity $k'$ to maximize cum-dividend value $V(k, \Gamma)$, where $\Gamma = [p^{\text{In}}, \sigma^{\text{In}}, p^{\text{Out}}, \sigma^{\text{Out}}]$:

$$V(k, \Gamma) = \max_{k'} \left\{ \pi(k, \Gamma) + \Phi(k, k') + \max\!\begin{cases} P^{\text{In}}(k - k') & \text{if } k' \leq k \text{ (Contraction)} \\ -f \cdot k - P^{\text{In}} \cdot w_1(k' - k) + \beta \, \mathbb{E}[V^{\text{Build}}(k, k', \Gamma, H-1)] & \text{if } k' > k \text{ (Expansion)} \end{cases} \right\}$$

• $f$ is the fixed cost of expansion
• $w_1$ is the fraction of excess capacity purchased in period 1 of time-to-build
• $V^{\text{Build}}$ (eq. 5, p. 423) is the firm’s continuation value during the build-up stage

The price of the focal firm’s input equals the output price of its supplier $s$, and its output price equals the input price of its customer $c$ (eq. 8, p. 424):

$$P_t^{\text{In}} = P_t^{s,\text{Out}} \qquad \text{and} \qquad P_t^{\text{Out}} = P_t^{c,\text{In}}$$

This links the focal firm’s input and output price uncertainty to its trading partners’ fundamentals. The observable proxy for each uncertainty type is the realized stock return volatility of the supplier (customer) over a rolling window (eq. 9, p. 424):

$$\begin{aligned} \sigma_t^{\text{Upstream}} &= \operatorname{Std}(R^s_{t-W}, \ldots, R^s_t) \\ \sigma_t^{\text{Downstream}} &= \operatorname{Std}(R^c_{t-W}, \ldots, R^c_t) \end{aligned}$$

Key asymmetry (pp. 428-431). The paper builds on the canonical bad-news-principle channel of Bloom (2009) by decomposing total uncertainty into upstream and downstream components. Both uncertainties increase the option value of waiting (bad news principle, Bernanke 1983). Only downstream uncertainty also raises the opportunity cost of waiting: during time-to-build, forgone revenues are a convex function of the future output price (the firm can disinvest if the price falls), so higher downstream uncertainty raises the cost of delay. Upstream uncertainty is unaffected because all input purchases are made up front. Net result: upstream uncertainty unambiguously suppresses investment; downstream uncertainty can hasten investment when the time-to-build period is sufficiently long.

Four testable hypotheses (§II.C.4, p. 434):

1. Upstream-investment association is unambiguously negative.
2. Downstream-investment association is weaker in absolute value, can be positive.
3. Downstream effect is more positive for firms with longer time-to-build.
4. Harder-to-abandon firms show a more negative (less positive) upstream (downstream) effect.

Method

Model solution. The model is solved numerically by value function iteration (Section II.B, p. 425). Gaussian autoregressive processes are discretized using a Tauchen (1986) variant that allows time-varying conditional volatility, similar to Alfaro et al. (2024). The state space uses a refined, endogenous grid for capital centered around the stochastic steady state, with a dense grid near the free boundaries where the growth option is exercised. The model is calibrated at the quarterly frequency (Table I, p. 425); key parameters: $\alpha = 0.40$, $\beta = 0.997$, $\delta = 0.025$, $f = 0.020$, $\rho_p = 0.950$, $\sigma_p = 0.200$. Model-implied moments (Table II, p. 426) match $\sigma(I/K) = 0.165$ and skewness $= 0.626$ in the data within the 95% confidence interval.

This builds on real-options and value-function-iteration; the macrolevel evidence builds on smooth-local-projections.

Uncertainty measures. Upstream (downstream) uncertainty is the equal-weighted average realized daily stock return volatility of the firm’s suppliers (customers), computed over the prior calendar year using CRSP daily data. Firm-level supplier-customer networks are identified from Compustat Segments (1976-2002) and FactSet Revere (2003-2019), merged to maximize coverage.

Empirical specifications

All firm-level regressions (Section III, pp. 434-443) are estimated on a firm-year panel of CRSP/Compustat firms (NYSE, AMEX, NASDAQ, excl. financials SIC 6000-6999 and utilities SIC 4900-4999), 1976-2019. Standard errors are clustered at the firm level. Each independent variable is scaled by its unconditional standard deviation.

Baseline investment regression (eq. 10, p. 437; R1-R3):

$$y_{i,t} = \alpha_i + \delta_t + \beta_1 \cdot \sigma(\text{Own})_{i,t} + \beta_2 \cdot \sigma(\text{SupplyChain})_{i,t} + \gamma' Z_{i,t} + \epsilon_{i,t}$$

• $\sigma(\text{SupplyChain}) \in \{ \sigma(\text{Upstream}),\, \sigma(\text{Downstream}) \}$
• $y_{i,t}$ is the investment rate (I/K) of firm $i$ at time $t$, measured from the most recent annual report as of June $t$
• $\alpha_i$ = firm fixed effects; $\delta_t$ = year fixed effects
• $\sigma(\text{Own})_{i,t}$ is the firm’s own stock return volatility
• $Z_{i,t}$ includes firm size, leverage, tangibility, Tobin’s q, profitability, and past returns (Leary and Roberts 2014)
• Sample: OLS, firm + year FE; ~17,456-50,786 observations (Table III, p. 438)

The same equation with $y$ replaced by working capital growth, employment growth, COGS growth, or intangibles growth gives Table IV results (R3).

Time-to-build heterogeneity regression (eq. 11, p. 440; R4):

$$y_{i,t} = \alpha_i + \delta_t + \beta_1 \cdot \sigma(\text{Own})_{i,t} + \beta_2 \cdot \sigma(\text{Downstream})_{i,t} \times I[\text{Long}]_{i,t} + \beta_3 \cdot \sigma(\text{Downstream})_{i,t} \times I[\text{Short}]_{i,t} + \gamma' Z_{i,t} + \epsilon_{i,t}$$

• $I[\text{Long}]$ and $I[\text{Short}]$ are indicator variables for long and short time-to-build firms
• Three proxies: (i) inverse depreciation rate, (ii) sector (nondurables/services = short; investment goods/durables = long, Gomes et al. 2009), (iii) R&D intensity
• The null $H_0: \beta_2 = \beta_3$ (Wald test) is rejected at 10% in all specifications (Table V, p. 442)

The same interaction structure is used to test reversibility heterogeneity (Table VI, p. 443), replacing $I[\text{Long}]$ with $\text{HighReverse}$ / $\text{LowReverse}$ (Kim and Kung 2017 capital redeployability measure).

Where Acemoglu, Akcigit, and Kerr (2016) study the production-network propagation of shocks, the paper tests analogous channels for second-moment (uncertainty) shocks at the macro level.

Macrolevel impulse responses (eq. 13, p. 447; R7-R8): Smooth local projections (SLPs, Barnichon and Brownlees 2019) estimated for forecast horizons $h \in \{1, \ldots, H\}$ quarters:

$$y_{t+h} = \beta_{0(h)} + \beta_{1(h)} y_t + \beta_{2(h)} \sigma_{U,t} + \beta_{3(h)} \sigma_{D,t} + \sum_{p=1}^{P} \gamma'_{p(h)} \Gamma_{t-p} + \epsilon_{t+h}$$

• $y_{t+h}$ is one of: quarterly real growth rates of industrial production, consumption, private investment, GDP, and the level of market price-dividend ratio and risk-free rate
• $\sigma_{U,t}$ ($\sigma_{D,t}$) is macrolevel upstream (downstream) uncertainty, constructed as the value-weighted average realized volatility of firms classified in the top (bottom) 10th percentile of the industry upstreamness score (eq. 12), built on the upstreamness measure from BEA I-O tables of Antras and Chor (2018) to form the macrolevel upstream-downstream industry classification
• $\Gamma_{t-p}$ includes the dependent variable, both macrolevel uncertainties, excess market return, term spread, default spread, and inflation
• $P = 4$ lags; 1976Q1-2019Q4 quarterly data; all variables standardized
• SE/CIs: IRFs plotted with 90% confidence intervals (Figures 6-7, pp. 448-449)

When to read the full paper

Use the original (institutional access required) if you are: replicating (code in Supporting Information); extending the supply-chain uncertainty measures or time-to-build heterogeneity tests; auditing the IV strategy or the macrolevel SLP estimates; or reviewing the COVID-19 application in §IV.C. The locators above point to the exact table or figure.

Attribution and rights

Source: peer-reviewed, The Journal of Finance 81(1), February 2026. © 2025 the American Finance Association. This distillation was extracted by an LLM on 2026-05-31 and is not human-verified or independently reproduced. The paper is paywalled; no CC licence is present in Crossref metadata or on the artifact. Reproduction of the verbatim text requires a licence from the publisher.

Grigoris, Fotis, and Gill Segal. “Investment under Upstream and Downstream Uncertainty.” The Journal of Finance 81, no. 1 (February 2026): 413–457. DOI: 10.1111/jofi.70010. Extract-only; all rights reserved by the American Finance Association / Wiley.