Managerial implicit stereotypes and where to find them: Evidence from Incentivized Resume Rating

Last registered on October 29, 2021


Trial Information

General Information

Managerial implicit stereotypes and where to find them: Evidence from Incentivized Resume Rating
Initial registration date
October 26, 2021
Last updated
October 29, 2021, 5:55 AM EDT



Primary Investigator

Bocconi University

Other Primary Investigator(s)

PI Affiliation
Bocconi University
PI Affiliation
Bocconi University

Additional Trial Information

In development
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Do implicit gender bias affect managers' discriminating behaviors? Does becoming aware of own implicit bias attenuate discrimination?

The project consist of a two-step lab-in-the-field experiment. As a general summary: we will evaluate gender bias of managers through the IAT test, reveal to the respondents of the treatment group their own score in the IAT, deliver a second survey in which we will be asking managers from treatment and control group to evaluate explicitly hypothetical students profiles according to the methods of the Incentivized Resume Rating.

External Link(s)

Registration Citation

Spadavecchia, Lorenzo, Paola Antonia Profeta and Maddalena Ronchi. 2021. "Managerial implicit stereotypes and where to find them: Evidence from Incentivized Resume Rating." AEA RCT Registry. October 29.
Sponsors & Partners

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Experimental Details


Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
The primary outcomes will be the score provided by respondents to the Incentivized Resume Rating. Through the Incentivized Resume Rating we will ask managers to evaluate 10 explicitly fake CVs, that contain a bunch of information such as gender, GPA, previous education, work experience, experience abroad, languages.
Please find attached the second survey containing the IRR. You will see only one CV, as characteristics inside of it are randomized for 10 iterations, while keeping the shape and presentation of the CV fixed. At the end of each CV, a slider will be used to give an evaluation from 1 to 10.

Managers will be asked to provide an evaluation of each profile on a Likert Scale from 1 to 10. We are interested in understanding how the gender of the profile and manager’s own implicit bias affect the score provided to the profile. Other variables included in the profile and manager’s characteristics will be used as control variables. Our preferred specification will be as follows:

〖Profile Score〗_im= α+βX_i+ρ〖Gender〗_i+ γ〖Treatment〗_m×〖Gender〗_i+δ_m+ε_im

Where X_i are profile characteristics, δ_m are manager’s m fixed effects and γ is our coefficient of interest, identifying the effect of IAT score disclosure on on outcome variable 〖Profile Score〗_im.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
The other outcomes will be given by the IAT score itself, which will be regressed on manager’s individual characteristics.
We will also use firm’s performance variables and the share of women in the firm and in managerial positions as outcome variables, and study whether manager’s bias affect these figures.
Evetually, we also use explicit attitudes and belilefs of managers on the IAT score, showing whether correlation arises between explicit and implicit attitudes. This is ex ante ambiguous and the prior is that the explanatory power of the IAT score in explaining explicit bias is low. This might be due both to Social Desirability Bias, for which managers provide answers that are more acceptable, but also to the fact that managers are trained in answering these type of questions in a non discriminatory way (thanks for example to diversity training they have undertaken during their career).
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The treated grous receive the treatment before the Incentivized Resume Rating (IRR). Managers in the control group will receive the control straight after the IRR.

Please refer to the "hidden" experimental design for more information: excessive details in these section might alter results.
Experimental Design Details
Not available
Randomization Method
The randomization will be done in office by a computer. We will stratify managers by their demographic characteristics and firm’s characteristics. In particular, the characteristics we will take into consideration when stratifying the managers into treatment and control group will be:
- Sector: it is in fact well known in the literature that more discriminatory behaviour might affect some sectors more than others
- Gender of the manager
- Age of the manager
- IAT score

We will divide the sample of respondents in 6 groups, 4 treatment and 2 control groups. This will be done to study whether the revelation of the IAT score (the treatment) has persistent effects over time. The first treatments and control group will hence receive the Incentivized Resume Rating after one week from the revelation (of the IAT or of the general information about the gender gap). The second groups will receive the IRR after three months.
Randomization Unit
The randomization unit will be at the manager's level. As the Association of managers we are collaborating with ensured that respondents never belong to the same firm. Anyway, we will know whether two or more respondents belong to the same company.

If such an event happened, our unit of randomization will be the firm instead of the single manager.

Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
The sample size has a planned number of cluster equal to 3000.
We will deliver the survey to about 30’000 managers, but previous survey run by the Company we are collaborating with (we do not display the name here publicly for experimental reasons) show that usual take up rate if of about 10%.
Each of the six treatment and control goups is estimated to contain about 500 managers.
Sample size: planned number of observations
The planned number of observations is 3000
Sample size (or number of clusters) by treatment arms
500 managers: first treatment (revealing IAT and receive IRR after one week: IAT & Short Run effect)
500 managers: second treatment (informative treatment on gender gap and receive IRR after one week: Information & Short Run effect)
500 manager: first control (no treatment and receive IRR together with first and second treatment groups: Control & Short Run effect)

500 managers: third treatment (revealing IAT and receive IRR after three months: IAT & Long Run effect)
500 managers: fourth treatment (informative treatment on gender gap and receive IRR after three months: Information & Long Run effect)
500 manager: second control (no treatment and receive IRR together with third and fourth treatment groups: Control & Long Run effect)
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
The IRR will contain abut 20 profiles that the respondents will have to evaluate. If all the respondents in the first survey will respond to the second survey containing the IRR, we will receive about 12'000 evaluations of CV. Groups of 500 for treatment and control outnumber the ones usually used in the literature.
Supporting Documents and Materials

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Institutional Review Boards (IRBs)

IRB Name
Bocconi Research Ethics Committee
IRB Approval Date
IRB Approval Number