Hiring Decisions and Wage Inequality

Last registered on July 19, 2023


Trial Information

General Information

Hiring Decisions and Wage Inequality
Initial registration date
July 14, 2023

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
July 19, 2023, 2:31 PM EDT

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



Primary Investigator

ESMT Berlin

Other Primary Investigator(s)

PI Affiliation
Federal Institute for Vocational Education and Training, Maastricht University
PI Affiliation
University of Potsdam
PI Affiliation
Federal Institute for Vocational Education and Training, Maastricht 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.
This project sets out to add to the understanding on how managers make their hiring and wage setting decisions in German establishments. We start from the question to what extent wages and hiring decisions depend on believes about the return to individual characteristics, in particular the returns to four different types of human capital (general human capital as well as firm-, occupation-, and task-specific experience).
To address the lack of exogenous variation in human capital measures in observational data, we design a conjoint survey experiment that creates random variation in the characteristics of potential hires. We then let actual decision makers in German establishments evaluate these randomly generated candidate profiles. The conjoint survey experiment is anchored in the BIBB Cost-Benefit Survey 2022/23. We randomly vary the gender and expected performance of the potential hires as well as their general human capital, occupational experience, firm-specific experience, and task-specific experience. Based on random variation in these individual characteristics, we assess the perceived return to these different types of individual-level human capital.
Furthermore, we assess a range of personality traits of the respondents and plan to analyze the interaction between the decision makers’ personality and their choices. Finally, we aim to link the data from our questionnaire to administrative linked employer-employee data from the German Institute for Employment Research (IAB). This link will allow us to quantify the contribution of (heterogeneity in) believes about the importance of different types of HC to overall wage inequality.
External Link(s)

Registration Citation

Caliendo, Marco et al. 2023. "Hiring Decisions and Wage Inequality." AEA RCT Registry. July 19. https://doi.org/10.1257/rct.11782-1.0
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Experimental Details


A conjoint survey experiment (choice experiment) as detailed below.
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
Our first primary outcome of interest is the binary choice regarding which of two hypothetical applicants is preferred for a hiring/promotion decision in the eyes of a human resource manager. Our second primary outcome of interest is the choice of a wage (in relation to an average wage for a leadership position at the decision maker’s company) for each of the two applicants based on their presented profiles. The two hypothetical applicants in each choice are characterized by six different features that determine their endowment with human capital. The six different attributes are the applicants’ gender, general human capital, occupational experience, experience in leadership positions, expected performance after an assessment center, and tenure at the firm (for internal candidates). The distribution of values for the three experience measures is calculated from the Linked-Employer-Employee-Data (LIAB, longitudinal model 1975-2019) of the German Institute for Labor Market Research (IAB).
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Effect-heterogeneity concerning decision makers’ characteristics (role in their own company, tenure (in their firm and position), apprenticeship in the firm, age, gender, highest occupational degree), preferences (risk- and time preferences, positive reciprocity, trust), and industry/occupation.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Within the BIBB Cost Benefit Survey 2022/23, we implement a module with survey questions and an experimental design. The BIBB Cost Benefit Survey establishment panel has ample information on the firm, on training and on industry and work characteristics. To this established survey, we add questions on the decision maker’s characteristics and preferences as well as a conjoint survey experiment. The respondents are decision makers in a representative sample of German establishments.
Respondents are first asked for their consent to link their answers to social security data from the German Institute for Labor Market Research (IAB). Second, they are asked some background questions regarding their own position and tenure within the organization, whether they have conducted an apprenticeship at the same company, their age, gender, education, and decision-making power. Then, they see an introductory text that briefly introduces the questions on preferences as well as shortly explains the relevance of preferences in this field and states that it is has become a customary practice to collect data on preferences in such settings. Fourth, they answer questions regarding preferences, in particular their risk aversion, time preferences, reciprocity, and trust in others.
Fifth, the respondents receive the instructions regarding the survey experiment where we ask them to make hypothetical hiring decision on two hypothetical applicants with different characteristics.
Finally, they see five screens with a pair of applicants on each. All applicants are characterized by six features (where the values are drawn randomly from the indicated set):
- In the firm since {0, 2, 7, 18} years
- Experience in leadership positions {0, 1, 3, 6} years
- Expected performance after the assessment center {average, good, excellent}
- Gender {male, female}
- General knowledge and skills {average, good, excellent}
- Experience in the occupation {0, 4, 10, 19} years
The possible (positive) values for the experience attributes are computed as the 25th, 50th, and 75th percentile of the respective distributions in a representative sample of German establishments, the LIAB (longitudinal model for the years 2012-2019). All values are drawn uniformly at random.
For each pair, the respondents are asked to make two decisions: Decide for one of the two applicants and enter a proposed salary for each of the applicants. Respondents can tick the box for their preferred candidate and move a slider to indicate the wage that they deem appropriate for each candidate (in percentage relation to an average wage for a leadership position of the respondent’s own organization). We randomize the order of the items as well as the order of the two tasks (wage setting and forced choice) at the respondent level.
We aim to analyze the data from the trial by estimating average marginal component effects (AMCE, see Bansak et al., 2021 and Hainmueller et al., 2014). Using our fully randomized conjoint design, we estimate linear regressions of our two primary outcomes on dummies for the different item values.
We further aim to estimate conditional AMCEs for different subgroups of respondents defined based on respondent characteristics (see our secondary outcomes). In particular, we plan to distinguish AMCEs conditional on respondents’ gender, industry, tenure at the firm, age, and educational degree. In addition, we estimate conditional AMCEs based on the respondents’ answers to the preference / personality trait questions. While parts of these heterogeneity analyses are explorative, we also aim to test a range of specific hypotheses:
- Respondents with higher levels of reciprocity are more likely to prefer internal candidates.
- Respondents with higher levels of trust are more likely to prefer external candidates.
- Respondents with lower risk tolerance are more likely to prefer internal candidates.
- Respondents in industries with higher average education levels are more likely to prefer internal candidates.
- Respondents with longer firm tenure are more likely to prefer internal candidates.
- Respondents who have conducted an apprenticeship in the same firm are more likely to prefer internal candidates.

We aim to operationalize “higher levels of …” by median splits of the sample. We measure the preference for internal vs. external candidates by the coefficients on the dummies for different values of establishment tenure.

Bansak, K., Hainmueller, J., Hopkins, D., Yamamoto, T. (2021). Conjoint Survey Experiments. In J. Druckman & D. Green (Eds.), Advances in Experimental Political Science (pp. 19-41). Cambridge: Cambridge University Press. doi:10.1017/9781108777919.004
Hainmueller, J., Hopkins, D.J., Yamamoto, T. (2014). Causal Inference in Conjoint Analysis: Understanding Multidimensional Choices via Stated Preference Experiments. Political Analysis 22(1):1-30. doi:10.1093/pan/mpt024
Experimental Design Details
Not available
Randomization Method
Randomization is carried out by a computer-based randomization.
Randomization Unit
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
4,000 firms (2300 personal interviews and about 1700 web-based interviews are planned)
Sample size: planned number of observations
4,000 firms (2300 personal interviews and about 1700 web-based interviews are planned)
Sample size (or number of clusters) by treatment arms
fully randomized conjoint analysis where all items are drawn with equal probability
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number