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The impact of peers’ behaviour on firms’ training decisions

Last registered on September 12, 2025

Pre-Trial

Trial Information

General Information

Title
The impact of peers’ behaviour on firms’ training decisions
RCT ID
AEARCTR-0016766
Initial registration date
September 12, 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
September 12, 2025, 10:49 AM EDT

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

Locations

Primary Investigator

Affiliation
FBK-IRVAPP

Other Primary Investigator(s)

PI Affiliation
FBK-IRVAPP
PI Affiliation
FBK-IRVAPP
PI Affiliation
Trento Employment Agency
PI Affiliation
Trento Employment Agency

Additional Trial Information

Status
In development
Start date
2025-09-15
End date
2026-06-30
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Continuous training for workers plays a fundamental role in enabling firms to keep up with technological advancements and the resulting changes in production and organisational processes. This is particularly relevant for craft firms, where training supports adaptation to shifts in the demand for products and services while preserving the quality of products and services. Yet, compared to larger firms, craft firms tend to invest less in training (Kotey & Folker, 2007; Storey, 2004), often underestimating its benefits and overestimating its cost (Barrett & Mayson, 2007; Storey, 2004; Storey & Westhead, 1997). A key policy question is which levers are most effective in encouraging these firms to engage in training activities.
Since micro-enterprises and craft businesses typically operate in narrow and competitive local markets, where the innovation capacity is generally limited, the literature shows how they heavily rely on their peers when making important business decisions (McPherson, 1996; Nichter & Goldmark, 2009; Maté-Sánchez Val et al., 2018; Tomelleri & Billé, 2025), and this may also apply to investments in training.
In this vein, to test the extent to which craft firms' decisions to participate in training depend on the training activities undertaken by their peers, we run an information treatment experiment. In collaboration with the local Employment Agency (EA) of Trento, located in a highly productive province in Northern Italy, we randomly provide craft firms with accurate information about the training activities undertaken by their peers to evaluate the effect on training decisions. The study contributes to the literature on training behavioural nudges by examining low-cost interventions in the training markets.
External Link(s)

Registration Citation

Citation
Martini, Elisa et al. 2025. "The impact of peers’ behaviour on firms’ training decisions." AEA RCT Registry. September 12. https://doi.org/10.1257/rct.16766-1.0
Sponsors & Partners

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Experimental Details

Interventions

Intervention(s)
Intervention (Hidden)
The local employment agency in the province of Trento provides important training subsidies to craft firms, financed by the Autonomous Province of Trento. Despite a high coverage rate of the cost of training up to 100% of the total expenses, take-up is relatively low, with only 5% of craft firms having benefited from this financing channel in the past 3 years. This is consistent with the overall evidence from the literature that craft firms underinvest in training despite low costs and potentially high benefits. We run an information treatment experiment providing craft firms with information about the training activities done by their peers through the local Employment Agency. The aim is to evaluate the peer effects on individual firms’ training decisions and test whether providing information on peers can be used as a lever to induce more training. Through administrative databases, we obtain the universe of training financed by the local employment agency to craft firms. We construct the average share of craft firms in the province that have benefited from this financing channel in the past 3 years in each sector, the average amount of funding, the average number of hours, as well as the generic type of training (technical vs. Soft skills) and the most common titles of courses taken, namely the top 5 courses. We construct a dashboard that easily allows users to browse this information by selecting the sector of interest and potentially filtering by firms with and without employees (many craft firms are sole proprietorships with no employees).
For the primary analysis in this study, we mainly leverage two sources of data: i) firm-level demographic information (e.g., sector, class size, number of employees) on the universe of craft firms in the province derived from administrative archives; ii) information about all the training activities financed by the local employment agency to craft firms. In November 2024, a baseline survey (CAWI) was conducted on the population of craft firms in the Trento province to obtain information about the attitudes and propensity towards training in general (i.e., not specifically financed by the local employment agency). This baseline survey was administered to all craft firms in the province, achieving a 21% response rate. Aside from socio-demographic characteristics, the survey also collected detailed information on all training activities carried out (including those not funded by the Employment Agency), as well as on the type of training, funding sources, decision-making processes, and future investment plans in training. Similarly, a follow-up survey is planned to be conducted after the implementation of the intervention and before the end of June 2026, which marks the end of the training "season” for the current year (firms can sign up for training financed by the local employment agency roughly from September to June of a given year). The survey is meant to complement the main analysis on administrative data and allow for exploratory analysis of secondary outcomes, as detailed below. For this reason, sample stratification also includes a stratum for baseline survey respondents.
Intervention Start Date
2025-09-15
Intervention End Date
2026-06-30

Primary Outcomes

Primary Outcomes (end points)
The primary outcomes are:
1. Request for additional information via email (binary, 0/1 – intermediate outcome ).
2. a. Extensive margin: Firm participation in training funded by the local employment agency (binary, 0/1) in the 2025-26 cycle (and potentially onwards); 2b) Intensive margin: number of hours of training funded by the local employment agency (continuous)
3. Qualitative margin: The type of training the firm participates in (technical vs. Soft skills, as well as other potential categorisations to be decided afterwards); whether a firm is more likely to participate in a course that falls within the category of the top 5 courses other firms have benefited from, indicated in the dashboard; 0 otherwise.

Primary Outcomes (explanation)
All primary outcomes are obtained through administrative data: the intermediate outcome (contact via e-mail to the employment agency to obtain information about the financing channel) will be provided by the local employment agency; similarly, information about firm extensive and intensive participation in the training financed by local employment agency, as well as the type of training, will be obtained from administrative datasets operated by the local employment agency.

Secondary Outcomes

Secondary Outcomes (end points)
The secondary outcomes are:
1. General attitudes toward training: This outcome is captured through a composite index based on seven survey items measuring firms’ perceptions and attitudes toward training. The items cover perceived benefits, costs, and barriers to training (continuous).
2. Total hours of training conducted in the year 2025-2026, besides the one financed through the local employment agency (continuous).
3. Percentage of firms that are aware of at least one incentive and/or subsidy provided by the Employment Agency or directly by the province. In the baseline survey, more than 50% of responding firms reported not being aware of the existence of such funding opportunities.
4. Average training expenditure within the firm, excluding contributions received from the Province or the Employment Agency.
5. Planned increase in training effort (hours and budget): Plans to increase training hours over the next 12–24 months (binary 1/0).
Secondary Outcomes (explanation)
All secondary outcomes are collected by the baseline and follow-up survey, which include measures of general attitudes toward training, perceived benefits from training in terms of productivity and experience with training in general. On top of the main analysis of administrative data for the universe of craft firms in the province, we will conduct a separate analysis on the sample of firms that respond to both the baseline (21%) and follow-up survey:

Experimental Design

Experimental Design
Our experimental design follows a stratified four-arm randomised controlled trial (three treatment groups + one control).
Experimental details will be available as soon as the trial is over.
Experimental Design Details
Our experimental design follows a stratified four-arm randomised controlled trial (three treatment groups + one control).
All firms (including control firms) receive a generic email containing information about training opportunities through the financing channel of the local employment agency dedicated to craft firms in the province. Within the text of the email, an option is mentioned to request further details via a dedicated email address. Treated firms receive the same email, which, at the end, also includes a link to the dashboard mentioned above. The dashboard contains various information on training funded by ADL over the 2022–2024 period: in addition to the number of firms that participated in training initiatives, it reports the average training hours, the average funding provided, and the corresponding totals by sector. When the sector is locked (see below), the values refer to all sectors. Linked to these data are details on the breakdown by type of course (i.e., technical and transversal skills), as well as the list of the most frequently attended course cycles for these two categories.
The treatment group is divided into three arms, which differ depending on the type of training information accessible through the dashboard:
• Treatment 1 - receives information limited to the sector in which the firm operates; we obtain the sector from the administrative database containing the universe of craft firms in the province.
• Treatment 2 - receives the "full” dashboard, allowing navigation across all sectors, but pre-set on the sector in which the firms operate; this implies that when opening the dashboard, firms in this treatment arm see the same page as treatment 1, but are allowed to further browse the dashboard across sectors. They can also see the total for all sectors combined.
• Treatment 3 - receives only information for all sectors combined, without the possibility to browse across sectors or to access information about its own specific sectors. This information is also available for firms in treatment 2 by selecting “All sectors”.
The receipt of the email can be monitored to ensure that generic information about the financing channel is delivered in equal proportion to both treatment and control groups. The email subject line will be identical across groups. Treatment take-up will be measured by the number of clicks on the section of the email linking to the dashboard. A pilot analysis on a small sample of firms predicted a take-up rate of approximately 20%.
Randomization Method
Computer random number generator.
Randomization Unit
The randomisation unit is the individual craft firm operating in the province of Trento.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
No clustering.
Sample size: planned number of observations
Indicatively 12,000 craft firms (the full universe of craft firms in the province of Trento) obtained from administrative data.
Sample size (or number of clusters) by treatment arms
3 treatment groups (20% of firms each) and 1 control group (40% of firms).
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Primary analysis on Administrative Data From the administrative data, we know that the baseline participation rate in financed training is equal to 5 % and the average number of hours is equal to 78.8. Considering an alpha level of 0.05 (two-sided), 80% power, allocation 60/40 (T/C), full compliance, full response rate (N = 12,000), the ITT minimum detectable effects (MDEs) would be: • Participation (binary): 1.21 pp • Hours of training (continuous, SD = 26.78 h): 1.40 h If analyses are restricted to 20% responders (20% response rate, N ≈ 2,400), the ITT MDEs worsen: 2.89 pp (participation) and 3.13 h (hours). But because only a fraction of treated firms may comply with the intervention (e.g. clicking the dashboard, engaging with the information), the ITT effect is further diluted according to the compliance rate. Below, we report the implied LATE MDEs under alternative compliance rates: Compliance rate Outcome N=12,000 (MDE) N≈2,400 (MDE) 10% Participation (pp) 12.1 28.9 10% Hours (h) 14 31.3 20% Participation (pp) 6.05 14.45 20% Hours (h) 7 7.1 15.65 30% Participation (pp) 4.03 9.63 30% Hours (h) 4.67 10.43 50% Participation (pp) 2.42 5.78 50% Hours (h) 2.8 6.26
IRB

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number

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