Information and the Formation of Inflation Expectations by Firms: Evidence from a Survey of Israeli Firms
What this paper finds — and why it matters
Layer 1: Overview
Research question and motivation. How do firms form and update inflation expectations during a monetary-policy regime change and a transition from high/volatile inflation to a low, stable, inflation-targeting environment? This matters because tracking and managing expectations is central to modern monetary policy (especially under forward guidance), yet high-quality firm-level expectations data—particularly across regime changes—are scarce (Bernanke 2007). A central tension in the literature is that firms and households in long-stable advanced economies are largely inattentive to inflation and monetary policy, plausibly because successful stabilization removes the incentive to monitor them. Israel offers a natural experiment: its recent history of high inflation and dollarization, followed by disinflation, de-dollarization, and the anchoring of expectations at the ~2% target midpoint around 2003.
Data and design. The authors use the Bank of Israel Firms’ Survey, a quarterly survey (quantitative inflation-expectation questions added in 1997), covering six industries (post-2009 shares: manufacturing 36%, services 36%, commerce 14%, transportation/communications 5%, hotels 5%, construction 4%). The main analysis sample is 2001Q3–2018Q3. The survey is voluntary, unbalanced, not nationally representative; late-sample participation fell to ~250–300 firms with a response rate around 30%. Identification exploits within-quarter variation in response timing: because Israel’s CPI is published monthly on the 15th and policy-rate decisions are scheduled, firms responding after a release (“treatment”) had information that firms responding earlier (“control”) did not. Surprises are defined relative to professional forecasters’ mean expectations: an inflation (CPI) surprise and a monetary (policy-rate) surprise. Identification assumes response timing is random; the authors show firm characteristics generally do not predict either response period (Table 4) or the cross-section of expectations (Table 3). Estimation uses two-way (firm and quarter) fixed-effects panel regressions interacting treatment dummies with surprise size, plus a lagged dependent variable; local projections (Jordà 2005) first show output/employment respond to the shocks, motivating that beliefs should too.
Main quantitative findings (Table 9, full sample 2001Q3–2018Q3). A positive inflation surprise of one percentage point raises 1-year inflation expectations by about 0.5 pp from the second-monthly-CPI surprise (coefficient 0.467) and about 0.7 pp from the third-monthly-CPI surprise (0.700). The effect on 1-quarter expectations is weaker (≈0.12 and ≈0.29). Because the annual response exceeds the quarterly response, firms on average treat CPI surprises as persistent, not transitory. A surprise one-percentage-point hike in the policy rate lowers 1-year inflation expectations by about 0.3 pp (coefficient 0.343, negative sign) and 1-quarter expectations by roughly 0.15 pp. The mean second-month-CPI treatment dummy itself is small (-0.07 pp), so the interaction terms carry the economic content.
Mechanisms and scope conditions. The inflation-surprise result is robust across sub-periods, before/after 2010, firm sizes, and industries. The monetary-surprise result is NOT robust: dropping the large 2001–2002 policy shocks (sample 2002Q3–2018Q3) renders it insignificant and sign-flipped, consistent with policy shocks having little effect on beliefs in stable environments (Coibion et al. 2020; Ilek 2021 for Israeli forecasters). Implication: even after de-dollarization and prolonged low/stable inflation, Israeli firms keep monitoring macro news; (re)anchoring expectations—making them insensitive to news—may take a long time, an insight relevant for countries now facing high inflation.
Layer 2: Deep Dive
What is the identification strategy and what are the main threats to it?
The strategy exploits variation in survey response timing within each quarter. Because Israel publishes CPI on the 15th of each month and policy-rate decisions are on scheduled dates, firms that respond after a release (treatment) have seen information that firms responding earlier (control) have not. Responses are grouped into Periods 1, 2, 3 (and Period 0 for missing/late dates), generating two CPI surprises (second- and third-monthly index) and one interest-rate surprise per quarter. The key identifying assumption is that response timing is as-good-as random. The main threat is selection—if attentive or expectation-distinctive firms systematically respond later, treatment status would be endogenous. The authors address this by regressing exposure-period indicators on observable firm characteristics (Table 4) and finding characteristics generally do not predict response period; they also confirm firm characteristics do not explain cross-sectional expectation levels (Table 3). A placebo test replacing the dependent variable with the prior quarter’s expectation (t-1) finds no effect (Appendix Table B5), supporting the timing identification. A residual threat is unobservable correlates of timing not captured by observables.
What are the main mechanisms and how are they distinguished empirically?
Two mechanisms: (1) firms update inflation expectations to new CPI information, and (2) firms update to monetary-policy information. They are distinguished by using separate, independently timed surprises (CPI releases vs. policy-rate decisions) and separate interaction terms. Persistence vs. transitory perception is inferred from the horizon pattern: because the 1-year response to a CPI surprise (~0.5–0.7 pp) exceeds the 1-quarter response (~0.12–0.29 pp), firms must expect the price increase to continue over subsequent quarters, i.e., they perceive CPI shocks as persistent. For monetary policy, the smaller 1-quarter than 1-year effect is read as consistent with monetary policy operating with a lag. The output/employment local projections (Table 8) show a non-monotonic response to rate surprises (rises in quarters 0–1, declines in quarters 2–3), which the authors note could mix conventional contractionary effects with an information effect (a higher rate signaling a stronger economy).
What heterogeneity is documented?
By firm size (Table 11): all three size groups (small, medium, large) respond to CPI surprises on 1-year expectations and the differences across groups are generally not statistically significant; the interest-rate-surprise effect resembles the pooled estimate for medium and large firms but is not statistically significant for small firms. By industry (Table 12): the CPI-surprise effect on 1-year expectations is positive and statistically significant in nearly every industry, whereas the interest-rate-surprise effect on 1-year expectations (full sample) is negative and significant only in manufacturing. Over time (Table 10): the 1-year CPI-surprise effect is almost identical before and after 2010 (the year the monetary committee was established), and the 1-quarter effect is similar or if anything stronger in the later period. Cross-sectionally, firm size, industry, and region are mostly statistically and economically insignificant predictors of expectation levels (Table 3).
What robustness checks are run?
(1) Shorter sample 2002Q3–2018Q3 excluding the large 2001–2002 policy shocks—CPI-surprise results essentially unchanged, monetary-surprise results become insignificant and change sign. (2) Split before/after 2010 allowing time-varying effects (Table 10). (3) Heterogeneity by size (Table 11) and industry (Table 12) as consistency checks. (4) A placebo test regressing the previous quarter’s (t-1) expectation on current-quarter news, finding no effect (Appendix Table B5). (5) Checks that firm characteristics predict neither response timing (Table 4) nor expectation levels (Table 3), supporting the random-timing assumption. (6) Local projections on output and employment (Table 8) establishing that firms’ real-side behavior responds to the shocks, motivating belief responses. Standard errors are White and clustered at the firm level throughout.
How does this paper relate to and differ from closely related prior work?
It builds on the firm-expectations literature (Coibion, Gorodnichenko, Kumar 2018; Candia, Coibion, Gorodnichenko 2023) showing firms’ expectations lie between professional forecasters’ and households’—confirmed here by intermediate disagreement among firms. It connects to expectation-formation work (D’Acunto et al. 2021 on shopping experience; Coibion-Gorodnichenko 2015 on exchange-rate sensitivity in Ukraine; Kumar et al. 2015 on New Zealand managers) and to studies of news effects on expectations (Beechey, Johannsen, Levin 2011). It is closest in spirit to Lamla and Vinogradov (2019), who compare household expectations before/after monetary announcements; the contribution is to study firms in an economy with a recent history of high inflation and dollarization undergoing disinflation. It also relates to regime-change classics (Sargent 1982 on ending hyperinflations; Mankiw, Reis, Wolfers 2003 on Volcker disinflation), filling the gap that little is known about firms’ expectations across a policy-regime change. Its Israeli monetary-surprise null in the stable period echoes Coibion et al. (2020) and Ilek (2021).
What are the policy implications and their scope conditions?
Central implication: even after successful de-dollarization and a prolonged low-and-stable inflation environment, Israeli firms continued to monitor and react to inflation news—so de-dollarization (firms’ renewed trust in local currency) does not necessarily translate into inattention, and (re)anchoring expectations in the sense of making them insensitive to news may take a long time. For countries currently experiencing high inflation, the Israeli experience suggests firm expectations can remain news-sensitive for an extended period. Scope conditions: the firm sample is not nationally representative; results are specific to Israel’s institutional setting (monthly CPI on the 15th, scheduled rate decisions); the monetary-policy result is fragile—it is driven mainly by the unusually large 2001–2002 shocks and disappears in calmer periods, so the conclusion that monetary surprises move firm expectations holds chiefly when shocks are large.
Are there other significant findings or caveats?
Descriptive facts: firms’ average annual inflation expectations (2001Q3–2018Q3) averaged 2.34% (vs. 1.81% for professional forecasters, 1.57% for the capital market); in the 2011Q1–2018Q3 panel households averaged 3.02% while firms averaged 1.83%, banks 1.07%. Firms’ expectations are about one percentage point below households’ but 0.5–1 pp above other (forecaster/market) sources, and disagreement among firms lies between that of households and professional forecasters—consistent with prior literature. Expectations co-move strongly across sources and across industries. Raw cross-period descriptive evidence (Table 5) shows average and median expectations decline as more information becomes available (Period 1 mean 2.52 → Period 3 mean 2.26), and disagreement weakly declines. The largest interest-rate surprises (1.5–2 pp) occurred at the sample start: in December 2001 the Bank cut the rate by 2 pp to 3.8%, triggering capital outflow, depreciation, and price increases, then reversed to 9.1%. A caveat is that the survey was discontinued at end-2020 (replaced by a CBS survey), and the unbalanced, voluntary panel limits representativeness.