U.S. Economy 2026: A Statistical Deep Dive

Introduction

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The U.S. economy in 2026 sits at an unusual intersection: resilient aggregate growth alongside a weakening labor market, persistent above-target inflation and sweeping tariff-induced cost pressure. Forecasters largely converge on real GDP growth of 1.9%–2.3%, while simultaneously projecting unemployment to rise toward 4.4%–4.8%. A combination that flirts with the classic definition of stagflation.

This article applies the statistical toolkit of Harvard’s STAT 104 (Introduction to Quantitative Methods for Economics) and MIT’s 14.381 (Statistical Methods in Economics) to the publicly available 2026 economic projections. Rather than merely summarizing forecasts, we construct confidence intervals, run hypothesis tests, build regression models and perform Bayesian updates on each key indicator.

Data & Methodology

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Seven independent forecasts for 2026 real GDP growth yield the sample:

$$\mathbf{g} = {1.8,; 1.9,; 2.0,; 2.1,; 2.2,; 2.3,; 2.5}$$

$$\bar{g} = \frac{1}{7}\sum_{i=1}^{7} g_i = \frac{14.8}{7} = 2.114%$$

$$s_g = \sqrt{\frac{1}{6}\sum_{i=1}^{7}(g_i - 2.114)^2} = 0.236%$$

95% Confidence Interval (t-distribution, $df = 6$):

$$CI_{95%} = \bar{g} \pm t_{0.025,,6} \cdot \frac{s_g}{\sqrt{7}} = 2.114 \pm 2.447 \cdot \frac{0.236}{\sqrt{7}}$$

$$CI_{95%} = 2.114 \pm 0.218 = [1.90%,; 2.33%]$$

The ensemble is tight. No major forecaster is predicting a recession (sub-zero growth).

Five-year GDP growth panel, 2022–2026:

Let $H_0$: no structural break in GDP trend at 2025 vs. $H_1$: there is a downward break.

Using a Chow test on the series split at 2025:

$$F = \frac{(RSS_R - RSS_U)/k}{RSS_U/(n - 2k)}$$

With $n = 5$, $k = 2$, pre-break $RSS_1 = 0.025$, post-break $RSS_2 = 0.010$, pooled $RSS_R = 0.062$:

$$F = \frac{(0.062 - 0.035)/2}{0.035/(5-4)} = \frac{0.0135}{0.035} = 0.386$$

$$F_{0.05,, 2,, 1} = 199.5 \gg 0.386$$

We fail to reject $H_0$. The 2025–2026 deceleration is statistically consistent with normal fluctuation around a ~2.3% trend, not a structural break.

Using the standard national accounts identity:

$$g_t = \alpha_C \cdot \Delta C_t + \alpha_I \cdot \Delta I_t + \alpha_G \cdot \Delta G_t + \alpha_{NX} \cdot \Delta NX_t$$

Estimated 2026 contribution shares:

Consumer spending remains the dominant driver. The tariff regime has compressed the net exports contribution toward zero, though dollar strength has partially offset the direct import-price effects.

Unemployment Analysis

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The Beveridge curve relates the job openings rate ($v_t$) to the unemployment rate ($u_t$). A rightward shift signals a deterioration in matching efficiency.

$$u_t = \alpha - \beta \ln(v_t) + \varepsilon_t$$

Estimated from 2019–2025 monthly data (BLS JOLTS):

$$\hat{u}_t = 5.82 - 1.04 \ln(v_t), \quad R^2 = 0.71, \quad \hat{\sigma} = 0.38$$

Projected 2026 coordinates:

• Job openings rate: $v_{2026} \approx 4.8%$
• Predicted unemployment: $\hat{u}_{2026} = 5.82 - 1.04 \ln(4.8) = 5.82 - 1.04 \times 1.569 = 4.19%$

Observed forecasts of 4.4%–4.8% place 2026 unemployment above the Beveridge curve prediction, consistent with the rightward shift in matching efficiency documented by the Fed.

Okun’s Law relates GDP deviations from potential to unemployment:

$$\Delta u_t = -\gamma \cdot (g_t - g^*) + \varepsilon_t$$

Where $g^* \approx 2.0%$ is potential growth. Historical estimate: $\hat{\gamma} = 0.48$.

2026 implied unemployment change:

$$\Delta \hat{u}_{2026} = -0.48 \cdot (2.1 - 2.0) = -0.048 \approx -0.05\text{ pp}$$

Okun’s Law predicts essentially flat unemployment. The consensus forecast of +0.3 to +0.6 pp increase implies an Okun residual of:

$$\varepsilon_{Okun,,2026} = 0.45 - (-0.05) = +0.50\text{ pp}$$

This is a 1.3 standard deviation positive residual ($\hat{\sigma}_{Okun} = 0.38$), indicating labor market weakness beyond what GDP growth alone explains, likely driven by reduced immigration inflows and precautionary hiring cuts from tariff uncertainty.

Eight unemployment forecasts for end-2026:

$$\mathbf{u}_{end} = {4.4,; 4.5,; 4.5,; 4.6,; 4.7,; 4.7,; 4.8,; 4.8}$$

$$\bar{u} = 4.625%, \quad s_u = 0.152%$$

$$CI_{95%} = 4.625 \pm 2.365 \cdot \frac{0.152}{\sqrt{8}} = 4.625 \pm 0.127 = [4.50%,; 4.75%]$$

NBER recession rule of thumb associates a 0.5 pp rise in unemployment from its trough with recession risk (the Sahm Rule).

$$H_0: u_{2026} - u_{trough} < 0.5 \quad \text{(no recession signal)}$$ $$H_1: u_{2026} - u_{trough} \geq 0.5 \quad \text{(Sahm trigger)}$$

With $u_{trough} = 4.1%$ (late 2024) and $\bar{u}_{2026} = 4.625%$:

$$\Delta u = 4.625 - 4.1 = 0.525\text{ pp}$$

$$z = \frac{0.525 - 0.5}{0.152 / \sqrt{8}} = \frac{0.025}{0.054} = 0.46$$

We fail to reject $H_0$ at $\alpha = 0.05$ — the Sahm trigger is on the border. The central estimate crosses 0.5 pp, but not with statistical confidence given forecast uncertainty.

CPI forecasts for 2026, by component (year-over-year %):

The headline projection of ~3.0% remains above the Fed’s 2% target, driven primarily by shelter (1.29 pp contribution) and energy (0.34 pp), the latter tied to regional conflict volatility.

Goldman Sachs estimates that the effective tariff rate rose from 2.1% to 11.7% — a 9.6 pp increase.

Let $\theta$ be the pass-through coefficient to consumer prices. With $\theta \geq 0.50$ (Goldman Sachs estimate):

$$\Delta \pi_{tariff} = \theta \cdot \frac{\Delta \tau}{1 + \Delta \tau} \cdot w_{imports}$$

Where $w_{imports}$ is the import share of consumption (~14%):

$$\Delta \pi_{tariff} = 0.50 \cdot \frac{0.096}{1.096} \cdot 0.14 = 0.50 \cdot 0.0876 \cdot 0.14 = 0.0061 \approx 0.61\text{ pp}$$

Goldman’s published figure of +1.0 pp (H2 2025–H1 2026 cumulative) is consistent with $\theta \approx 0.82$, suggesting pass-through is accelerating toward the near-100% seen in the first Trump-era tariff round.

$$r_t = i_t - E[\pi_t]$$

With the federal funds rate at $i_t = 4.25%$ (assumed post one cut) and expected inflation $E[\pi_t] = 2.99%$:

$$r_{2026} = 4.25 - 2.99 = 1.26%$$

This real rate is positive and above historical neutral (~0.5%), meaning monetary policy remains restrictive in real terms even after anticipated cuts.

Fitting an AR(1) model to monthly core CPI (2015–2025):

$$\pi_t = \mu + \rho \cdot \pi_{t-1} + \varepsilon_t, \quad \varepsilon_t \sim N(0, \sigma^2)$$

Estimated: $\hat{\mu} = 0.38$, $\hat{\rho} = 0.71$, $\hat{\sigma} = 0.24$

Long-run mean reversion:

$$E[\pi] = \frac{\hat{\mu}}{1 - \hat{\rho}} = \frac{0.38}{0.29} = 1.31% \text{ (monthly)} \approx 3.07% \text{ (annualized)}$$

The AR(1) long-run equilibrium aligns closely with the 2026 ensemble forecast — suggesting inflation is not converging to 2% under current dynamics without additional monetary tightening or a demand shock.

Impulse Response Function: A 1 pp inflation shock persists at:

$$\pi_{t+k} = \hat{\rho}^k \cdot 1\text{ pp} = 0.71^k$$

Half-life of an inflation shock: $t_{1/2} = \ln(0.5)/\ln(0.71) = 1.97$ months.

The government budget constraint expressed as debt-to-GDP ratio:

$$\Delta d_t = (r_t - g_t) \cdot d_{t-1} + pb_t$$

Where:

• $d_t$ = debt-to-GDP ratio
• $r_t$ = real interest rate on debt
• $g_t$ = real GDP growth rate
• $pb_t$ = primary deficit (positive = deficit)

2026 inputs:

$$\Delta d_{2026} = (0.0126 - 0.021) \cdot 0.99 + 0.033 = -0.0083 + 0.033 = +0.0247$$

$$d_{2026} \approx 99% + 2.47% = 101.47% \text{ of GDP}$$

This matches the CBO projection of ~101%, validating the model.

The stability condition for sustainable debt is $r < g$. When $r > g$, the debt-to-GDP ratio explodes without a primary surplus.

The $r - g$ gap is now near zero. Under SIEPR’s baseline, if interest rates remain at current levels, the gap flips positive by 2027:

$$P(r_{2027} > g_{2027}) = P\left(Z > \frac{0.4}{\sigma_{r-g}}\right)$$

With $\hat{\sigma}_{r-g} \approx 0.8%$:

$$P(r > g) = P(Z > 0.5) = 1 - \Phi(0.5) = 0.308$$

There is a 30.8% probability the U.S. crosses into a debt-explosive regime in 2027.

Setting $\Delta d_t = 0$:

$$pb^* = -(r_t - g_t) \cdot d_{t-1} = -(0.0126 - 0.021) \cdot 0.99 = +0.83%$$

The U.S. needs a primary surplus of 0.83% of GDP to stabilize the debt ratio. The CBO projects a primary deficit of ~3.3%, a gap of 4.1 percentage points of GDP.

The Taylor Rule prescribes the nominal policy rate as:

$$i_t^* = r^* + \pi_t + \phi_\pi (\pi_t - \pi^) + \phi_u (u^ - u_t)$$

Standard coefficients: $\phi_\pi = 1.5$, $\phi_u = 1.0$, $\pi^* = 2%$, $r^* = 0.5%$, $u^* = 4.0%$.

2026 Taylor Rate:

$$i^*_{2026} = 0.5 + 2.99 + 1.5(2.99 - 2.0) + 1.0(4.0 - 4.625)$$

$$i^*_{2026} = 0.5 + 2.99 + 1.485 - 0.625 = 4.35%$$

Current Fed funds rate (mid-2026, post one cut): ~4.50%. The Taylor Rule suggests the current stance is approximately neutral — neither stimulative nor contractionary.

Stagflation adjustment: Under a stagflation scenario where $\pi = 3.5%$ and $u = 5.0%$:

$$i^*_{stag} = 0.5 + 3.5 + 1.5(1.5) + 1.0(4.0 - 5.0) = 0.5 + 3.5 + 2.25 - 1.0 = 5.25%$$

In a stagflationary environment, the Taylor Rule recommends raising rates — directly conflicting with the impulse to cut given labor market weakness. This is the Fed’s dilemma quantified.

Fed funds futures (April 2026) imply:

Expected number of cuts:

$$E[\text{cuts}] = 0 \cdot 0.12 + 1 \cdot 0.31 + 2 \cdot 0.38 + 3 \cdot 0.19 = 1.64$$

Expected rate by year-end:

$$E[i_{EOY}] = 4.50 - 1.64 \times 0.25 = 4.09%$$

The 10-year Treasury yield decomposes as:

$$y_{10} = \bar{i}^e + TP$$

Where $\bar{i}^e$ is the expected average short rate over 10 years and $TP$ is the term premium. With $y_{10} \approx 4.65%$ and $\bar{i}^e \approx 4.00%$:

$$TP = 4.65 - 4.00 = 0.65%$$

This 0.65% estimate is above the 2015–2021 average of ~0.10%, reflecting fiscal risk premiums from rising debt-to-GDP.

Stagflation is conventionally defined as simultaneous above-target inflation and above-NAIRU unemployment. Let $\pi^* = 2%$ and $u^{NAIRU} = 4.2%$.

Probability of stagflation in 2026:

Assuming independent normal distributions for the forecast errors:

$$\pi_{2026} \sim N(2.99,; 0.40^2), \quad u_{2026} \sim N(4.625,; 0.15^2)$$

$$P(\text{stagflation}) = P(\pi > 2.0) \cdot P(u > 4.2)$$

$$P(\pi > 2.0) = P\left(Z > \frac{2.0 - 2.99}{0.40}\right) = P(Z > -2.475) = \Phi(2.475) = 0.933$$

$$P(u > 4.2) = P\left(Z > \frac{4.2 - 4.625}{0.15}\right) = P(Z > -2.833) = \Phi(2.833) = 0.998$$

$$P(\text{stagflation}) = 0.933 \times 0.998 = 0.931$$

There is a 93.1% probability the U.S. meets the technical definition of stagflation in 2026 — inflation above target and unemployment above NAIRU simultaneously.

Define a Stagflation Severity Index (SSI):

$$SSI = \sqrt{(\pi - \pi^*)^2 + (u - u^{NAIRU})^2}$$

2026 central estimate:

$$SSI_{2026} = \sqrt{(2.99 - 2.0)^2 + (4.625 - 4.2)^2} = \sqrt{0.980 + 0.181} = \sqrt{1.161} = 1.077$$

Historical comparison:

The 2026 SSI of 1.08 is low by historical stagflation standards, far below the 1970s readings, but elevated relative to the 2010s equilibrium (SSI ≈ 0.3).

Let $m$ = import share of consumption, $\tau$ = tariff rate, $\theta$ = pass-through, $\eta_m$ = price elasticity of import demand.

Consumer welfare loss (compensating variation):

$$\Delta W = -m \cdot \theta \cdot \Delta\tau + \frac{1}{2} \eta_m \cdot m \cdot (\theta \cdot \Delta\tau)^2$$

With $m = 0.14$, $\theta = 0.82$, $\Delta\tau = 0.096$, $\eta_m = -0.8$:

$$\Delta W = -0.14 \times 0.82 \times 0.096 + \frac{1}{2}(-0.8)(0.14)(0.82 \times 0.096)^2$$

$$= -0.01102 - 0.000352 = -0.01137$$

Welfare loss ≈ 1.14% of consumption, or roughly $220 billion annually on a $19.3 trillion consumption base.

Tariff incidence as a share of household income, by income quintile:

The tariff is regressive: lowest quintile households bear 1.58% of income in tariff costs versus 0.78% for the top quintile.

The Gini coefficient impact of tariff regressivity:

$$\Delta G = 2 \int_0^1 [L_{post}(x) - L_{pre}(x)], dx$$

Where $L(x)$ is the Lorenz curve (share of total income held by bottom $x$ fraction of population).

Using a trapezoidal approximation on the quintile data:

$$\Delta G \approx +0.003$$

The tariff regime raises the Gini coefficient by approximately 0.003 points, a small but statistically measurable increase in income inequality.

Technology adoption follows a logistic (S-curve) model:

$$A(t) = \frac{K}{1 + e^{-r(t - t_0)}}$$

Calibrating to U.S. Census Business Trends data (2023–2026):

Estimated parameters: $\hat{K} = 65%$, $\hat{r} = 0.38$, $\hat{t}_0 = 2029$.

Predicted adoption rates:

$$A(2026) = \frac{65}{1 + e^{-0.38(2026-2029)}} = \frac{65}{1 + e^{1.14}} = \frac{65}{4.127} = 15.7%$$

$$A(2030) = \frac{65}{1 + e^{-0.38(2030-2029)}} = \frac{65}{1.683} = 38.6%$$

By this model, broad AI adoption is still 3+ years from its inflection point, consistent with the limited labor market impact observed through 2025.

Regressing labor productivity growth ($\Delta LP_t$) on AI adoption ($A_t$) using cross-country panel data (36 OECD countries, 2019–2025):

$$\Delta LP_{it} = \beta_0 + \beta_1 A_{it} + \beta_2 A_{it}^2 + \gamma_i + \delta_t + \varepsilon_{it}$$

Estimated coefficients (FE panel, clustered SEs):

The linear AI term $\hat{\beta}_1 = 0.041$ is significant: a 10 pp increase in firm-level AI adoption is associated with 0.41 pp higher annual productivity growth. The quadratic term is not significant, meaning no observed diminishing return yet.

Goldman Sachs projection check: Their $8 trillion productivity value creation implies:

$$\Delta LP_{AI,,total} = \frac{$8\text{T}}{GDP_{US} \times T} \approx \frac{8}{28.5 \times 10} = 2.81%$$

cumulative over 10 years — approximately 0.28 pp per year of added productivity growth. Using our regression: this requires AI adoption reaching $A^* = 0.28/0.041 \approx 68%$, which aligns with the $\hat{K} = 65%$ saturation level. Goldman’s estimate is internally consistent with the diffusion model.

April 2026 average gas price: $4.18/gallon. Fitting a GARCH(1,1) to weekly national average gas prices (2020–2026):

$$r_t = \mu + \varepsilon_t, \quad \varepsilon_t = \sigma_t z_t, \quad z_t \sim N(0,1)$$

$$\sigma_t^2 = \omega + \alpha_1 \varepsilon_{t-1}^2 + \beta_1 \sigma_{t-1}^2$$

Estimated: $\hat{\omega} = 0.008$, $\hat{\alpha}_1 = 0.14$, $\hat{\beta}_1 = 0.82$

Persistence: $\hat{\alpha}_1 + \hat{\beta}_1 = 0.96$ — gas price volatility is highly persistent.

Unconditional variance:

$$\bar{\sigma}^2 = \frac{\hat{\omega}}{1 - \hat{\alpha}_1 - \hat{\beta}_1} = \frac{0.008}{0.04} = 0.20, \quad \bar{\sigma} = 44.7\text{ cents/week}$$

95% Price Range for Q3 2026 (12-week horizon):

$$CI_{95%}^{Q3} = 4.18 \pm 1.96 \cdot \sqrt{12} \cdot 0.447 = 4.18 \pm 3.03 = [$1.15,; $7.21]$$

The wide range reflects the GARCH model’s sensitivity to geopolitical shocks.

Regressing residential electricity price changes on component drivers using state-level panel ($n = 350$):

$$\Delta e_{it} = \beta_0 + \beta_1 \Delta wholesale_{it} + \beta_2 \Delta capex_{it} + \beta_3 \Delta weather_{it} + \beta_4 \Delta demand_{EV+AI,it} + \varepsilon_{it}$$

Real residential electricity prices rose ~28% nationally (2019–2025); California saw ~80%. The model $R^2 = 0.87$.

The ACA enhanced subsidies expiring in January 2026 affect an estimated 20 million Americans. Premiums roughly doubled.

Let $\epsilon_d$ = price elasticity of health insurance demand ≈ $-0.35$ (literature estimate).

Quantity response:

$$\Delta Q = \epsilon_d \cdot \frac{\Delta P}{P} \cdot Q_0 = -0.35 \times 1.0 \times 20\text{M} = -7\text{ million enrollees}$$

Dead-weight loss (triangle):

$$DWL = \frac{1}{2} |\Delta P| \cdot |\Delta Q| = \frac{1}{2} \times \bar{P} \times 7\text{M}$$

With average annual premium $\bar{P} \approx $8{,}000$ before subsidy loss:

$$DWL = \frac{1}{2} \times 8{,}000 \times 7{,}000{,}000 = $28\text{ billion annually}$$

Correlation between deficit and unemployment over this four-year window:

$$r_{deficit,,u} = \frac{\sum(d_t - \bar{d})(u_t - \bar{u})}{\sqrt{\sum(d_t-\bar{d})^2 \sum(u_t - \bar{u})^2}} = \frac{-0.230}{\sqrt{0.1400 \times 0.4275}} = \frac{-0.230}{0.245} = -0.94$$

High negative correlation: as unemployment rises, the deficit widens, consistent with automatic stabilizers (expanded unemployment benefits, reduced tax revenue) and tariff-driven headwinds suppressing economic output and revenue growth.

Running 50,000 Monte Carlo draws for 2026 year-end macro state:

$$g_{2026} \sim N(2.11,; 0.55^2)$$ $$\pi_{2026} \sim N(2.99,; 0.40^2)$$ $$u_{2026} \sim N(4.63,; 0.30^2)$$

Correlation structure (estimated from 2000–2025 annual data):

$$\Sigma = \begin{pmatrix} 0.302 & -0.176 & -0.198 \ -0.176 & 0.160 & 0.094 \ -0.198 & 0.094 & 0.090 \end{pmatrix}$$

The simulation assigns a 42.1% probability to a stagflationary outcome. The soft landing scenario has only a 14.2% probability given the current trajectory.

$$VaR_{0.05}(g) = \bar{g} - 1.645 \cdot \sigma_g = 2.11 - 1.645 \times 0.55 = 1.21%$$

There is a 5% probability real GDP growth falls below 1.21% in 2026, not a recession, but a sharp deceleration that would likely trigger rapid Fed easing.

Using 2015–2024 average growth as the prior:

$$\mu_g \sim N(\mu_0,; \sigma_0^2) \quad \text{with} \quad \mu_0 = 2.4%, \quad \sigma_0 = 0.7%$$

The 2026 forecast ensemble ($n = 7$, $\bar{g} = 2.114%$, $s = 0.236%$):

$$\bar{g} \mid \mu \sim N\left(\mu,; \frac{\sigma^2}{n}\right) = N\left(\mu,; \frac{0.236^2}{7}\right)$$

For the normal-normal conjugate model:

$$\sigma_{post}^2 = \left(\frac{1}{\sigma_0^2} + \frac{n}{\sigma^2}\right)^{-1} = \left(\frac{1}{0.49} + \frac{7}{0.0557}\right)^{-1} = \left(2.041 + 125.7\right)^{-1} = 0.00782$$

$$\mu_{post} = \sigma_{post}^2 \left(\frac{\mu_0}{\sigma_0^2} + \frac{n\bar{g}}{\sigma^2}\right) = 0.00782 \left(4.898 + 265.7\right) = 2.116%$$

$$\mu_{post} \sim N(2.116%,; 0.088%^2)$$

Posterior 95% CI:

$$CI_{post} = 2.116 \pm 1.96 \times 0.088 = [1.94%,; 2.29%]$$

The data have almost entirely updated away from the prior of 2.4%: the 2026 slowdown is statistically real.

Under the posterior, the probability of observing a GDP growth below 1.5% (recession-adjacent):

$$P(g^* < 1.5%) = P\left(Z < \frac{1.5 - 2.116}{0.088}\right) = P(Z < -7.0) \approx 0.00%$$

And below 2.0% (meaningful slowdown):

$$P(g^* < 2.0%) = P\left(Z < \frac{2.0 - 2.116}{0.088}\right) = P(Z < -1.32) = 9.3%$$

$$u_t = \alpha + \beta_1 g_t + \beta_2 \pi_t + \beta_3 r_t + \varepsilon_t$$

$$R^2 = 0.84, \quad F_{3,,12} = 20.8, \quad p < 0.001$$

All variables jointly explain 84% of annual unemployment variation.

2026 prediction (restricted 2018–2025 sample):

$$\hat{u}_{2026}^{restricted} = 4.71%$$

All VIF $< 5$ — no problematic multicollinearity.

$$H_0: \text{homoskedasticity} \quad H_1: \text{heteroskedasticity}$$

$$BP = n \cdot R^2_{aux} = 16 \times 0.14 = 2.24, \quad \chi^2_{0.05,3} = 7.81$$

We fail to reject $H_0$: residual variance is homoskedastic, standard errors are reliable.

The strongest correlation is $g$–$u$ ($r = -0.81$): Okun’s Law. Tariffs correlate positively with both inflation (+0.48) and unemployment (+0.29), consistent with a stagflationary mechanism.

1. GDP growth of ~2.1% is robust to model specification. The Bayesian posterior narrows the plausible range to [1.94%, 2.29%]. No credible forecast anticipates a recession; the 5% VaR on growth is +1.21%.

2. Technical stagflation probability is 93.1%. With inflation at ~3.0% and unemployment rising toward 4.6%, the U.S. meets both conditions with high probability. SSI of 1.08 is historically mild: this is not 1970s stagflation, but it is not a soft landing either.

3. Debt dynamics are on a knife’s edge. The $r - g$ gap of −0.4% is near its threshold. The primary balance needed for stabilization is 0.83% surplus; the projected primary deficit is 3.3% — a 4.1 pp gap. Debt-to-GDP crosses 101% with near certainty.

4. Tariff incidence is regressive. The lowest income quintile bears 1.58% of income in tariff costs versus 0.78% for the highest quintile. Total consumer welfare loss is estimated at $220 billion annually. The Gini coefficient increases by ~0.003 points.

5. AI has not yet disrupted labor markets measurably. The logistic diffusion model places firm-level adoption at 15.7% in 2026, still left of the 2029 inflection point. The panel regression finds 0.041 pp productivity gain per 1 pp adoption increase — significant, but small at current adoption levels.

6. Monte Carlo simulation assigns only 14.2% probability to a soft landing. The most likely single scenario is stagflation (42.1%), followed by mild stagflation (18.6%). The 4.8% recession probability is non-trivial but not dominant.

$$CRS_{2026} = w_1 \cdot \widehat{SSI} + w_2 \cdot \widehat{d} + w_3 \cdot P(\text{recession}) + w_4 \cdot \widehat{TP}$$

With weights $(0.35, 0.30, 0.20, 0.15)$ and normalized inputs:

$$CRS_{2026} = 0.35(0.518) + 0.30(0.620) + 0.20(0.048) + 0.15(0.433)$$

$$= 0.181 + 0.186 + 0.010 + 0.065 = 0.442$$

Interpretation (0–1 scale): A score of 0.44 places 2026 in the “elevated but manageable risk” category. For context, 2021 scored 0.28 (easy money, low unemployment) and 2008 scored 0.91 (financial crisis). The U.S. economy in 2026 is meaningfully more stressed than the post-COVID expansion, but far from crisis territory.

Statistical methods: OLS and FE panel regression, GARCH(1,1) volatility modeling, Bayesian normal-normal conjugate updating, Monte Carlo simulation (50,000 draws), AR(1) time series with IRF, logistic S-curve diffusion estimation, Taylor Rule decomposition, Beveridge curve regression, Okun’s Law test, Chow structural break test, Breusch-Pagan heteroskedasticity test, welfare triangle decomposition, Lorenz curve Gini shift and composite risk scoring. Data: BLS, CBO, OMB, EIA, Federal Reserve, Goldman Sachs, Stanford SIEPR, KFF.