The aggregate impact of AI on Norwegian employment

Exposure, the wage it travels with, and their interaction — to 2026Q1

Published

8 June 2026

The question

Exposure scores say what AI could touch. They do not say what happened to jobs. This page asks the aggregate question on Norwegian register-style data: once general-purpose AI arrived, did the most-exposed occupations — and especially the well-paid, exposed ones, where the economics says displacement should bite first — lose employment?

Data & method

Panel. Quarterly STYRK-08 occupational employment and monthly earnings, 2016Q1–2026Q1, from Statistics Norway (SSB tables 12542 and 11418); AI exposure from Eloundou et al. (2024).

The regression. One observation per occupation. The outcome is the Yagan (2019) percent change in employment relative to its pre-ChatGPT average, \[y_o = 100\,\frac{E_{o,\,2026Q1} - \bar E^{\text{pre}}_o}{\bar E^{\text{pre}}_o}, \qquad \bar E^{\text{pre}}_o = \text{mean employment over quarters } \le 2022Q3,\] a level-robust alternative to a log difference (it does not blow up when the base quarter is small or noisy). We build it up in three nested steps, each regressor standardised (so coefficients are per 1 SD, in percentage points of employment), with heteroskedasticity-robust SEs:

$y_o = \alpha + \beta_1\,\mathrm{Exp}_o + \varepsilon_o$ — the naive exposure regression.
add the pre-ChatGPT average log wage $\ln \bar w^{\text{pre}}_o$ (mean monthly wage over 2016Q1–2022Q3) as a control — pre-determined, so it is not contaminated by the post period.
add the interaction $\mathrm{Exp}_o \times \ln \bar w^{\text{pre}}_o$.

Why this order. Exposure and pay are correlated (high-exposure office work is well paid: corr ≈ 0.55). So a raw exposure coefficient is partly just wage. Putting wage in as a control lets OLS net that out; the interaction then asks the sharp comparative-advantage question — does exposure cost more jobs precisely where pay is high? (prediction $\beta_3<0$). Because all three are entered together, $\beta_3$ is the genuine, partialled interaction — there is no collinearity sleight of hand.

From one number to a path. The cross-section is a single long difference, so we also run the event-study (diff-in-diff) version: every coefficient gets a time subscript, estimated jointly with occupation and quarter fixed effects (ref. = 2022Q3, SE clustered by occupation), \[y_{o,t}=\alpha_o+\tau_t+\sum_{k}\big(\beta_{1,k}\,\mathrm{Exp}_o+\beta_{2,k}\ln\bar w_o +\beta_{3,k}\,\mathrm{Exp}_o\times\ln\bar w_o\big)\mathbf 1[t{=}k]+\varepsilon_{o,t}.\] We show it twice, exactly as in the deck: naive ($\beta_{1,k}$ alone — what people usually estimate) and joint (all three entered simultaneously, so $\beta_{1,k}$ is exposure net of wage and $\beta_{3,k}$ is the genuine heterogeneity-by-wage interaction).

Robustness (HonestDiD). A positive post-shock coefficient can be a real effect or the pre-existing trend continuing; Rambachan & Roth (2023) ask how big a post-period violation of parallel trends — as a fraction $\bar M$ of the largest pre-period wobble — is needed before zero re-enters the CI. That threshold, the breakdown $\bar M^\ast$, is the headline: small means fragile.

Results

Employment growth against exposure

Figure 1: Each dot is a STYRK-08 occupation; **area is employment in 2026Q1** (with a size floor so small occupations stay visible). The most-exposed occupations are ringed and labelled. If exposure destroyed jobs the cloud would slope down — it does not.

The cloud is flat-to-slightly-positive. Note two things, because they answer the obvious question “where are the 90–100%-exposure software jobs?”: the most-exposed occupations (programmers, database/data-entry, payroll) sit at exposure 0.86–0.97 (ringed) but employ only a few hundred to a few thousand each, so they barely move aggregate employment; and the single 100%-exposure occupation (“Kodere mv.”) is dropped, because SSB suppresses its wage (≈ 6–14 employed) so it has no wage to control for.

The regression: exposure, then control for wage, then the interaction

Effect on employment (percentage points), per 1 SD; robust SE in parentheses; *** p<.01, ** p<.05, * p<.1.

Employment, % vs pre-ChatGPT avg (Yagan)	(1) exposure only	(2) + log pre-wage	(3) + interaction
AI exposure (β₁)	+4.4 (1.6)***	+0.9 (1.9)	+1.5 (1.8)
log pre-ChatGPT wage (β₂)		+6.4 (1.8)***	+5.9 (1.8)***
exposure × log wage (β₃)			+2.8 (2.0)
R²	0.025	0.063	0.068
N (occupations)	357	357	357

Read it left to right — this is the whole story:

(1) Naive. On its own, AI exposure looks good for jobs: +4.4 (1.6)***% per SD, significant. Exposed occupations grew. (The same lesson as the deck’s event-study exposure and comparative-advantage slides — both ≈ +3 pp.)
(2) Control for pre-ChatGPT wage. The exposure coefficient collapses to +0.9 (1.9) — indistinguishable from zero — while the wage control takes a large, significant +6.4 (1.8)*. The naive “exposure effect” was wage in disguise**: high-exposure occupations are well paid, and well-paid occupations grew. OLS nets it out.
(3) Add the interaction. Exposure stays at +1.5 (1.8) and the genuine interaction is +2.8 (2.0) — not significant. There is no extra job loss in high-wage exposed occupations: the sharp comparative-advantage prediction ($\beta_3<0$) does not show up.

The number that matters: total exposure effect by wage level

Because the effect of exposure now depends on wage, the quantity to report is the total exposure effect for an occupation, $\beta_1+\beta_3\cdot(\text{wage})$:

Total effect of +1 SD exposure	Employment
in a high-wage occupation (β₁ + β₃)	+4.3% [-1.7, +10.3]
in a low-wage occupation (β₁ − β₃)	-1.3% [-6.0, +3.4]

Even at the top of the pay distribution — where displacement was predicted to bite hardest — the total employment effect of AI exposure is +4.3% [-1.7, +10.3], indistinguishable from zero. No displacement, including where the theory said to look for it.

The naive event study: exposure only

Figure 2: Event study of the naive exposure effect (β₁, per 1 SD): employment (% vs the occupation’s pre-ChatGPT average, Yagan 2019) on exposure × quarter, occupation + quarter fixed effects, 95% CIs clustered by occupation. Dashed amber line = ChatGPT (2022Q4); green band = average post-shock effect; dashed pre-trend slope fitted on the pre-period.

This is what people usually estimate — and on its own it looks like good news: +3.11 pp per SD post-ChatGPT. But the path shows a clear upward pre-trend (+0.107 pp/quarter) — exposed occupations were already drifting up before ChatGPT — which is exactly why the naive number cannot be read causally.

The same event study with heterogeneity by wage: the joint decomposition

The deck’s causal centerpiece. Exposure, the log wage, and their (centered) interaction each get a full set of quarter interactions, entered simultaneously — so the exposure path is net of wage, and the interaction path is the genuine does-exposure-bite-where-pay-is-high object ($\beta_3 < 0$ is the displacement prediction).

Figure 3: Joint event study: exposure β₁,ₜ **net of wage and the interaction** (per 1 SD). Read it as a difference-in-differences: how employment in a more-exposed occupation moves around ChatGPT relative to a less-exposed one, net of quarter shocks and fixed occupation differences.

Figure 4: Joint event study: the interaction β₃,ₜ = Exp × ln w̄ (per 1 SD) — the comparative-advantage / heterogeneity-by-wage path. The sharp displacement prediction is β₃ < 0.

Net of the wage it travels with, exposure does nothing: the average post estimate is +0.3% [-1.5, +2.0]. And the interaction — the displacement object — is -1.6% [-4.0, +0.9], flat through the shock. The prediction $\beta_3<0$ does not appear: even the well-paid, exposed occupations show no extra decline. (The comparative-advantage signal in a one-regressor fit is carried by the wage main effect, +4.8% [+1.7, +7.9].)

Causal interpretation: HonestDiD on naive vs joint

Per-SD average post effects with Rambachan–Roth (2023) relative-magnitudes robustness — the same table as the deck:

	avg post (pp/SD)	95% CI	HonestDiD 95% CI (M̄=1)	breakdown M̄*
Naive exposure only (β₁)	+3.11	[+1.22, +4.99]	[-26.4, +32.6]	0.04
Joint fit: exposure (β₁)	+0.27	[-1.51, +2.04]	[-8.4, +9.0]	n.s.
Joint fit: interaction (β₃)	-1.57	[-4.04, +0.90]	[-32.4, +29.3]	n.s.

The only positive-looking estimate is the naive exposure effect (+3.1 pp/SD). Its breakdown is $\bar M^\ast \approx 0.04$: a post-shock deviation from parallel trends just 4% as large as the worst pre-ChatGPT wobble already puts zero back in the interval — not robust.
In the joint fit, exposure (β₁) and the genuine interaction (β₃) are already ≈ 0 — there is no positive effect left for a pre-trend to overturn.
Causal verdict: no robust effect in either direction — no displacement, no dividend.

Bottom line

The naive exposure effect is wage in disguise. +4.4 (1.6)**% per SD on its own → +0.9 (1.9)% once pre-ChatGPT wage is controlled; in the joint event study, exposure net of wage is +0.3% [-1.5, +2.0]. What grew were well-paid* occupations, exposed or not.
No displacement, including where predicted. The heterogeneity-by-wage interaction is -1.6% [-4.0, +0.9] in the joint event study (+2.8 (2.0)% in the cross-section, n.s. both ways); the total exposure effect for a high-wage occupation is +4.3% [-1.7, +10.3] — zero. Aggregate employment in these 357 occupations rose 0.3% from 2022Q3 to 2026Q1 (2,767,939 employed in 2026Q1).
And the naive positive effect is not robust either. A strong pre-trend drives it: HonestDiD breakdown $\bar M^\ast \approx 0.04$. Causal verdict: no robust effect in either direction. Displacement, if it comes, should surface first in hiring and entry-level flows, which a stock panel understates — the next test.

Take this study into the AI Impact Analyst →

The analyst opens with every exhibit of this study loaded in the Exhibit picker, and can edit each one on request — change the specification, the standard errors, the wage definition, or the variables — and the revised figure or table appears directly in the chat.

References

Lindenlaub, I., Oh, R., Rodríguez, M. A. & Veldkamp, L. (2026). Beyond Exposure: Predicting AI Adoption Based on Comparative Advantage.
Eloundou, T., Manning, S., Mishkin, P. & Rock, D. (2024). GPTs are GPTs: Labor Market Impact Potential of LLMs.
Rambachan, A. & Roth, J. (2023). A More Credible Approach to Parallel Trends. Review of Economic Studies.
Statistics Norway (SSB), StatBank via PxWebApi v2 — tables 12542 (employment) and 11418 (monthly earnings).

--- title: "The aggregate impact of AI on Norwegian employment" subtitle: "Exposure, the wage it travels with, and their interaction — to 2026Q1" date: today date-format: "D MMMM YYYY" toc: true engine: jupyter format: html: code-tools: true code-links: false grid: body-width: 1040px --- ```{python} #| label: setup #| include: false import numpy as np import pandas as pd import matplotlib.pyplot as plt import koja_impact as ki KOJA = ki.KOJA plt.rcParams.update({ "figure.facecolor": "none", "axes.facecolor": "none", "savefig.facecolor": "none", "savefig.transparent": True, "text.color": KOJA["mist"], "axes.labelcolor": KOJA["mist"], "axes.edgecolor": KOJA["edge"], "xtick.color": KOJA["muted"], "ytick.color": KOJA["muted"], "axes.titlecolor": KOJA["heading"], "axes.grid": True, "grid.color": KOJA["muted"], "grid.alpha": 0.15, "font.size": 11, }) cs, panel, qs, qidx, ref_i, shock_i = ki.load() R = ki.nested_cross_section() # the headline build-up regression es_exp = ki.event_study(panel, qs, "T_exp") # dynamics for the naive exposure effect he = ki.honest_rm(es_exp["path"], es_exp["theta"], es_exp["se"], es_exp["J"], qs) jt = ki.joint_decomposition(panel, qs) # the deck's joint event-study decomposition he_j = {tr: ki.honest_rm(jt[tr]["path"], jt[tr]["theta"], jt[tr]["se"], jt[tr]["J"], qs) for tr in ("T_exp", "T_xw")} def md(s): from IPython.display import Markdown; return Markdown(s) def stars(p): return "***" if p < .01 else "**" if p < .05 else "*" if p < .1 else "" def cell(sp, k): # "+4.4 (1.6)***" or blank if k not in sp: return "" # coefficients already in pp (Yagan outcome) b, se, p = sp[k] return f"{b:+.1f} ({se:.1f}){stars(p)}" def ci(d): return f"{d['theta']:+.1f}% [{d['theta']-1.96*d['se']:+.1f}, {d['theta']+1.96*d['se']:+.1f}]" def hrow(lab, r, hd): # row of the causal-interpretation table sig = abs(r["theta"]) > 1.96 * r["se"] bd = f"{hd['breakdown']:.2f}" if sig else "n.s." return (f"| {lab} | {r['theta']:+.2f} | [{r['theta']-1.96*r['se']:+.2f}, " f"{r['theta']+1.96*r['se']:+.2f}] | [{hd['lo']:+.1f}, {hd['hi']:+.1f}] | {bd} |") T = R["table"]; C1, C2, C3 = "(1) exposure only", "(2) + log pre-wage", "(3) + interaction" N = R["N"] agg = panel.groupby("quarter").emp.sum() agg_growth = (agg["2026K1"] / agg["2022K3"] - 1) * 100 emp_2026 = agg["2026K1"] ``` ## The question Exposure scores say what AI *could* touch. They do not say what happened to **jobs**. This page asks the aggregate question on Norwegian register-style data: once general-purpose AI arrived, did the most-exposed occupations — and especially the **well-paid, exposed** ones, where the economics says displacement should bite first — *lose* employment? ## Data & method **Panel.** Quarterly STYRK-08 occupational employment and monthly earnings, **2016Q1–2026Q1**, from Statistics Norway (SSB tables [12542](https://www.ssb.no/en/statbank/table/12542) and [11418](https://www.ssb.no/en/statbank/table/11418)); AI exposure from **Eloundou et al. (2024)**. **The regression.** One observation per occupation. The outcome is the **Yagan (2019) percent change** in employment relative to its pre-ChatGPT average, $$y_o = 100\,\frac{E_{o,\,2026Q1} - \bar E^{\text{pre}}_o}{\bar E^{\text{pre}}_o}, \qquad \bar E^{\text{pre}}_o = \text{mean employment over quarters } \le 2022Q3,$$ a level-robust alternative to a log difference (it does not blow up when the base quarter is small or noisy). We build it up in three nested steps, each regressor **standardised** (so coefficients are *per 1 SD*, in percentage points of employment), with heteroskedasticity-robust SEs: 1. $y_o = \alpha + \beta_1\,\mathrm{Exp}_o + \varepsilon_o$ — the naive exposure regression. 2. add the **pre-ChatGPT average log wage** $\ln \bar w^{\text{pre}}_o$ (mean monthly wage over 2016Q1–2022Q3) as a control — *pre-determined*, so it is not contaminated by the post period. 3. add the **interaction** $\mathrm{Exp}_o \times \ln \bar w^{\text{pre}}_o$. **Why this order.** Exposure and pay are correlated (high-exposure office work is well paid: corr ≈ `{python} f"{R['corr_exp_wage']:.2f}"`). So a raw exposure coefficient is partly just *wage*. Putting wage in as a control lets OLS **net that out**; the interaction then asks the sharp comparative-advantage question — *does exposure cost more jobs precisely where pay is high?* (prediction $\beta_3<0$). Because all three are entered together, $\beta_3$ is the genuine, partialled interaction — there is no collinearity sleight of hand. **From one number to a path.** The cross-section is a single long difference, so we also run the **event-study (diff-in-diff) version**: every coefficient gets a time subscript, estimated jointly with occupation and quarter fixed effects (ref. = 2022Q3, SE clustered by occupation), $$y_{o,t}=\alpha_o+\tau_t+\sum_{k}\big(\beta_{1,k}\,\mathrm{Exp}_o+\beta_{2,k}\ln\bar w_o +\beta_{3,k}\,\mathrm{Exp}_o\times\ln\bar w_o\big)\mathbf 1[t{=}k]+\varepsilon_{o,t}.$$ We show it twice, exactly as in the deck: **naive** ($\beta_{1,k}$ alone — what people usually estimate) and **joint** (all three entered simultaneously, so $\beta_{1,k}$ is exposure *net of wage* and $\beta_{3,k}$ is the genuine heterogeneity-by-wage interaction). **Robustness (HonestDiD).** A positive post-shock coefficient can be a real effect *or* the pre-existing trend continuing; Rambachan & Roth (2023) ask how big a post-period violation of parallel trends — as a fraction $\bar M$ of the largest pre-period wobble — is needed before zero re-enters the CI. That threshold, the **breakdown $\bar M^\ast$**, is the headline: small means fragile. ## Results ### Employment growth against exposure ```{python} #| label: fig-growth-exposure #| echo: false #| fig-cap: "Each dot is a STYRK-08 occupation; **area is employment in 2026Q1** (with a size floor so small occupations stay visible). The most-exposed occupations are ringed and labelled. If exposure destroyed jobs the cloud would slope down — it does not." fig, ax = plt.subplots(figsize=(9.0, 5.0)) slope, r2 = ki.draw_scatter(ax, cs) plt.tight_layout(); plt.show() ``` ```{python} #| output: asis #| echo: false md(f"""The cloud is flat-to-slightly-positive. Note two things, because they answer the obvious question *"where are the 90–100%-exposure software jobs?"*: the most-exposed occupations (programmers, database/data-entry, payroll) sit at exposure 0.86–0.97 (ringed) but **employ only a few hundred to a few thousand each**, so they barely move aggregate employment; and the single 100%-exposure occupation ("Kodere mv.") is **dropped**, because SSB suppresses its wage (≈ 6–14 employed) so it has no wage to control for.""") ``` ### The regression: exposure, then control for wage, then the interaction ```{python} #| output: asis #| echo: false md(f"""Effect on employment (percentage points), per 1 SD; robust SE in parentheses; *** p<.01, ** p<.05, * p<.1. | Employment, % vs pre-ChatGPT avg (Yagan) | {C1} | {C2} | {C3} | |---|:--:|:--:|:--:| | **AI exposure** (β₁) | {cell(T[C1],'exposure')} | {cell(T[C2],'exposure')} | {cell(T[C3],'exposure')} | | **log pre-ChatGPT wage** (β₂) | | {cell(T[C2],'logwage')} | {cell(T[C3],'logwage')} | | **exposure × log wage** (β₃) | | | {cell(T[C3],'interaction')} | | R² | {T[C1]['R2']:.3f} | {T[C2]['R2']:.3f} | {T[C3]['R2']:.3f} | | N (occupations) | {N} | {N} | {N} | Read it left to right — this is the whole story: - **(1) Naive.** On its own, AI exposure looks *good* for jobs: **{cell(T[C1],'exposure')}**% per SD, significant. Exposed occupations grew. (The same lesson as the deck's event-study exposure and comparative-advantage slides — both ≈ +3 pp.) - **(2) Control for pre-ChatGPT wage.** The exposure coefficient **collapses to {cell(T[C2],'exposure')}** — indistinguishable from zero — while the wage control takes a large, significant **{cell(T[C2],'logwage')}**. The naive "exposure effect" was **wage in disguise**: high-exposure occupations are well paid, and well-paid occupations grew. OLS nets it out. - **(3) Add the interaction.** Exposure stays at **{cell(T[C3],'exposure')}** and the genuine interaction is **{cell(T[C3],'interaction')}** — not significant. There is **no extra job loss in high-wage exposed occupations**: the sharp comparative-advantage prediction ($\\beta_3<0$) does not show up.""") ``` ### The number that matters: total exposure effect by wage level ```{python} #| output: asis #| echo: false md(f"""Because the effect of exposure now depends on wage, the quantity to report is the **total** exposure effect for an occupation, $\\beta_1+\\beta_3\\cdot(\\text{{wage}})$: | Total effect of +1 SD exposure | Employment | |---|:--:| | in a **high-wage** occupation (β₁ + β₃) | **{ci(R['high_wage'])}** | | in a low-wage occupation (β₁ − β₃) | {ci(R['low_wage'])} | Even at the top of the pay distribution — where displacement was predicted to bite hardest — the total employment effect of AI exposure is **{ci(R['high_wage'])}**, indistinguishable from zero. No displacement, including where the theory said to look for it.""") ``` ### The naive event study: exposure only ```{python} #| label: fig-event-exposure #| echo: false #| fig-cap: "Event study of the naive exposure effect (β₁, per 1 SD): employment (% vs the occupation's pre-ChatGPT average, Yagan 2019) on exposure × quarter, occupation + quarter fixed effects, 95% CIs clustered by occupation. Dashed amber line = ChatGPT (2022Q4); green band = average post-shock effect; dashed pre-trend slope fitted on the pre-period." fig, ax = plt.subplots(figsize=(10.5, 4.6)) ki.draw_eventstudy(ax, es_exp, qs, ref_i, shock_i, f"Naive exposure effect over time (β₁; N={len(cs)})", KOJA["purple"]) plt.tight_layout(); plt.show() ``` ```{python} #| output: asis #| echo: false md(f"""This is *what people usually estimate* — and on its own it looks like good news: **{es_exp['theta']:+.2f} pp per SD** post-ChatGPT. But the path shows a clear upward **pre-trend** ({es_exp['pre']:+.3f} pp/quarter) — exposed occupations were already drifting up before ChatGPT — which is exactly why the naive number cannot be read causally.""") ``` ### The same event study with heterogeneity by wage: the joint decomposition The deck's causal centerpiece. Exposure, the log wage, and their (centered) interaction each get a **full set of quarter interactions, entered simultaneously** — so the exposure path is *net of wage*, and the interaction path is the genuine *does-exposure-bite-where-pay-is-high* object ($\beta_3 < 0$ is the displacement prediction). ```{python} #| label: fig-event-joint-exposure #| echo: false #| fig-cap: "Joint event study: exposure β₁,ₜ **net of wage and the interaction** (per 1 SD). Read it as a difference-in-differences: how employment in a more-exposed occupation moves around ChatGPT relative to a less-exposed one, net of quarter shocks and fixed occupation differences." fig, ax = plt.subplots(figsize=(10.5, 4.6)) ki.draw_eventstudy(ax, jt["T_exp"], qs, ref_i, shock_i, f"Joint: exposure β₁,ₜ net of wage & interaction (N={len(cs)})", KOJA["purple"]) plt.tight_layout(); plt.show() ``` ```{python} #| label: fig-event-joint-interaction #| echo: false #| fig-cap: "Joint event study: the interaction β₃,ₜ = Exp × ln w̄ (per 1 SD) — the comparative-advantage / heterogeneity-by-wage path. The sharp displacement prediction is β₃ < 0." fig, ax = plt.subplots(figsize=(10.5, 4.6)) ki.draw_eventstudy(ax, jt["T_xw"], qs, ref_i, shock_i, f"Joint: interaction β₃,ₜ = Exp × ln w̄, net of main effects (N={len(cs)})", KOJA["fjord"]) plt.tight_layout(); plt.show() ``` ```{python} #| output: asis #| echo: false md(f"""Net of the wage it travels with, exposure does **nothing**: the average post estimate is **{ci(jt['T_exp'])}**. And the interaction — the displacement object — is **{ci(jt['T_xw'])}**, flat through the shock. The prediction $\\beta_3<0$ **does not appear**: even the well-paid, exposed occupations show no extra decline. (The comparative-advantage signal in a one-regressor fit is carried by the **wage main effect**, {ci(jt['T_wage'])}.)""") ``` ### Causal interpretation: HonestDiD on naive vs joint ```{python} #| output: asis #| echo: false md("\n".join([ "Per-SD average post effects with Rambachan–Roth (2023) relative-magnitudes robustness — the same table as the deck:", "", "| | avg post (pp/SD) | 95% CI | HonestDiD 95% CI (M̄=1) | breakdown M̄* |", "|---|:--:|:--:|:--:|:--:|", hrow("*Naive* exposure only (β₁)", es_exp, he), hrow("Joint fit: exposure (β₁)", jt["T_exp"], he_j["T_exp"]), hrow("Joint fit: interaction (β₃)", jt["T_xw"], he_j["T_xw"]), "", f"""- The only positive-looking estimate is the **naive** exposure effect ({es_exp['theta']:+.1f} pp/SD). Its breakdown is **$\\bar M^\\ast \\approx {he['breakdown']:.2f}$**: a post-shock deviation from parallel trends just **{he['breakdown']*100:.0f}% as large as the worst pre-ChatGPT wobble** already puts zero back in the interval — **not robust**. - In the **joint** fit, exposure (β₁) and the genuine interaction (β₃) are **already ≈ 0** — there is no positive effect left for a pre-trend to overturn. - **Causal verdict: no robust effect in either direction** — no displacement, no dividend.""", ])) ``` ## Bottom line ```{python} #| output: asis #| echo: false md(f"""- **The naive exposure effect is wage in disguise.** {cell(T[C1],'exposure')}% per SD on its own → {cell(T[C2],'exposure')}% once pre-ChatGPT wage is controlled; in the joint event study, exposure net of wage is {ci(jt['T_exp'])}. What grew were *well-paid* occupations, exposed or not. - **No displacement, including where predicted.** The heterogeneity-by-wage interaction is {ci(jt['T_xw'])} in the joint event study ({cell(T[C3],'interaction')}% in the cross-section, n.s. both ways); the total exposure effect for a high-wage occupation is {ci(R['high_wage'])} — zero. Aggregate employment in these {N} occupations **{'rose' if agg_growth>0 else 'fell'} {abs(agg_growth):.1f}%** from 2022Q3 to 2026Q1 ({emp_2026:,.0f} employed in 2026Q1). - **And the naive positive effect is not robust either.** A strong pre-trend drives it: HonestDiD breakdown $\\bar M^\\ast \\approx {he['breakdown']:.2f}$. **Causal verdict: no robust effect in either direction.** Displacement, if it comes, should surface first in **hiring and entry-level flows**, which a stock panel understates — the next test.""") ``` ```{=html} <div style="border:1px solid #3a4860;border-radius:10px;background:rgba(111,174,142,.06);padding:1rem 1.1rem;margin:0.2rem 0 1.6rem 0"> <a href="analysis.html?expert=ai-impact&study=aggregate-impact" style="display:inline-block;background:#cf9a5c;color:#070b12;font-weight:600;padding:.55rem 1.1rem;border-radius:8px;text-decoration:none"> Take this study into the AI Impact Analyst →</a> <p style="color:#8292ad;font-size:.86rem;margin:.5rem 0 0 0"> The analyst opens with <b>every exhibit of this study loaded</b> in the Exhibit picker, and can edit each one on request — change the specification, the standard errors, the wage definition, or the variables — and the revised figure or table appears directly in the chat.</p> </div> ``` ## References - Lindenlaub, I., Oh, R., Rodríguez, M. A. & Veldkamp, L. (2026). *Beyond Exposure: Predicting AI Adoption Based on Comparative Advantage.* - Eloundou, T., Manning, S., Mishkin, P. & Rock, D. (2024). *GPTs are GPTs: Labor Market Impact Potential of LLMs.* - Rambachan, A. & Roth, J. (2023). *A More Credible Approach to Parallel Trends.* Review of Economic Studies. - Statistics Norway (SSB), StatBank via PxWebApi v2 — tables 12542 (employment) and 11418 (monthly earnings).