Innovation Across Borders: Experimental Evidence on Artificial Intelligence

Last registered on January 28, 2026
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Pre-Trial

Trial Information

General Information

Title
Innovation Across Borders: Experimental Evidence on Artificial Intelligence
RCT ID
AEARCTR-0017121
Initial registration date
October 29, 2025

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
October 31, 2025, 8:38 AM EDT

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Last updated
January 28, 2026, 8:27 AM EST

Last updated is the most recent time when changes to the trial's registration were published.

Locations

Country
Germany
Region
Europe

Primary Investigator

Name
Ester Faia
Affiliation
Goethe University
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Other Primary Investigator(s)

PI Name
Annalisa Ferrando
PI Affiliation
European Central Bank
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PI Name
Judit Rariga
PI Affiliation
European Central Bank
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PI Name
Zoe Cullen
PI Affiliation
HBS
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PI Name
Ursel Baumann
PI Affiliation
European Central Bank
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Additional Trial Information

Status
Completed
Start date
2025-11-19
End date
2025-12-15
Keywords
Finance & Microfinance, Firms & Productivity
Additional Keywords
Cross-country diffusion, artificial intelligence, innovation, strategic complementarities, higher order beliefs, firms
JEL code(s)
O3, C83
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
We designed an information-provision experiment on the adoption of Artificial Intelligence (AI) technologies. Our survey module will be embedded in the Survey on the Access to Finance of Enterprises (SAFE), conducted by the European Central Bank (ECB). We expect to collect responses from roughly 5,000 firms across 12 European Union (EU) countries. The survey elicits firms’ beliefs about the adoption rates of AI among competitors within their own country and, separately, among competitors in the largest EU economies. We then randomize the provision of information about these two adoption rates. Finally, we measure how this information affects firms’ beliefs about future AI adoption by competitors, domestically and abroad, as well as their own intentions to adopt AI in the future. Additionally, we plan to explore heterogeneity by access to credit. Intuitively, a firm that learns it is lagging behind competitors in technology adoption may be able to respond by investing in adoption if it has sufficient liquidity or credit access, but may be unable to react otherwise.
External Link(s)

Registration Citation

Citation
Baumann, Ursel et al. 2026. "Innovation Across Borders: Experimental Evidence on Artificial Intelligence." AEA RCT Registry. January 28. https://doi.org/10.1257/rct.17121-2.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
The survey elicits firms’ beliefs about the adoption rates of AI among competitors within their own country and, separately, among competitors in the largest EU economies. We then randomize the provision of information about these two adoption rates. Finally, we measure how this information affects firms’ beliefs about future AI adoption by competitors, domestically and abroad, as well as their own intentions to adopt AI in the future.
Intervention (Hidden)
Intervention Start Date
2025-11-19
Intervention End Date
2025-12-15

Primary Outcomes

Primary Outcomes (end points)
(i) Expectations about competitors' investment in AI (domestically an abroad).
(ii) The firm's own intention to invest in AI
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
In the future, we may be able to explore additional data. First, we may be able to conduct a follow-up survey to assess the persistence of the effects, if any. Second, we might be able to merge the survey data with various administrative sources (e.g., balance sheet data from Orbis). At this stage, it is unclear whether either option will be feasible, and we need to seek for approval in advance. If they are, we intend to update this RCT registration before analyzing any additional datasets.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The subjects will be randomized between treatment and control groups. The information treatment provides firms with data on the share of firms that have invested in AI among: (1) domestic competitors (defined as firms in the same sector and size class within the same country); and (2) international competitors (defined as firms in the same sector and size class but located in the largest EU economies: Germany, France, and Italy).

Following the standard design of information-provision experiments, we seek to exploit heterogeneity in prior beliefs. Firms that underestimated the share of adopters are expected to revise their beliefs upward, those with accurate priors should show little or no updating, and those that overestimated the share are expected to revise their beliefs downward. The design leverages heterogeneity in both prior beliefs (one for within-country adoption and another for cross-country adoption) to identify the relative strength of within-country versus cross-country diffusion of innovation.

We also plan to explore heterogeneity by access to credit. Intuitively, a firm that learns it is lagging behind competitors in technology adoption may be able to respond by investing in adoption if it has sufficient liquidity or credit access, but may be unable to react otherwise. To measure access to credit, we can rely on survey questions from other modules of the SAFE survey and, where possible, complement these with administrative records, such as (i) balance sheet data from Orbis and (ii) credit data from the European credit registry (AnaCredit).
Experimental Design Details
Randomization Method
Randomization done in office by computer
Randomization Unit
The unit of randomization is the firm
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
Around 5,000 firms
Sample size: planned number of observations
Around 5,000 firms
Sample size (or number of clusters) by treatment arms
50% control and 50% treatment
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number
Analysis Plan

Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials