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The Hidden Cost of Violent Conflict: Sorting into Local Labor Markets A Field Experiment in Colombia
Last registered on June 24, 2020


Trial Information
General Information
The Hidden Cost of Violent Conflict: Sorting into Local Labor Markets A Field Experiment in Colombia
Initial registration date
June 24, 2020
Last updated
June 24, 2020 10:47 AM EDT
Primary Investigator
Heinrich-Heine-University Düsseldorf
Other Primary Investigator(s)
PI Affiliation
Georg-August Universität Göttingen
PI Affiliation
IHS Vienna
Additional Trial Information
Start date
End date
Secondary IDs
Violent conflicts have negative effects on prosperity and development. Reconstruction efforts require that qualified labor force is willing to work in highly violent areas. In this project we use a field experiment to investigate the impact of conflict on labor markets. Our focus is on the qualifications and gender of the pool of applicants. We built a pool of job-seekers and offer comparable jobs in either a low or high conflict area in Colombia. We investigate whether an increase in salary helps to attract more qualified workers and restore gender balancedness in applications.
External Link(s)
Registration Citation
Grosch, Kerstin , Marcela Ibanez and Gerhard Riener. 2020. "The Hidden Cost of Violent Conflict: Sorting into Local Labor Markets A Field Experiment in Colombia ." AEA RCT Registry. June 24. https://doi.org/10.1257/rct.6048-1.0.
Experimental Details
We investigate how pecuniary incentives and the riskiness of the job interact to attract educated labor force. We set up a labor market field experiment where we varied pecuniary benefits and the riskiness of the job, constructing a pool of job-seekers. Over those job-seekers we randomized individual job offers in low violence (safe) districts or high violence (dangerous) districts.
Intervention Start Date
Intervention End Date
Primary Outcomes
Primary Outcomes (end points)
The main outcome is whether the individual apply to the offered job by filling out an extensive online job application from.

We measure gender differences in application rates by gender and qualification of the candidates.
Primary Outcomes (explanation)

Primary Outcomes (Explanation): We measure application rate as the fraction of applicants per invited job-seekers and build this measure for each gender and treatment.

Qualification of candidates is measured as the expected wage that job-seeker will receive in the market. We use secondary data to estimate the likelihood and expected wage for candidates of different degrees of study and socioeconomic background and use the estimated model to predict expected earnings for participants in our sample.
Secondary Outcomes
Secondary Outcomes (end points)
Application strategy of job seekers.
Secondary Outcomes (explanation)
We compare number of days and hours that it take participants to complete the application and the performance in cognitive questions across treatments.
Experimental Design
Experimental Design
Stage 1: Announcement of Job
– Employer
– Location of job
– Position
– Responsibilities
Stage 4: Invitation to Apply
Randomization over the job announcement at individual level
– Job type: Low Risk/low salary
High Risk
––Salary (for high risk)
Experimental Design Details
The experiment involved five stages that are presented in Table~\ref{tab:Recruitment-Process} and described in more detail below. \paragraph{Stage 1: Announcement.} In the first stage we announced the positions through newspapers, university employment boards and social media In this announcement we provided general information about the position. Table~\ref{tab:Information} presents a summary of the information that participants received in this and subsequent stages. In this stage we tried to get a large pool of subjects interested in the positions over which we randomized the treatments. We invited recently graduated applicants from all areas of study. Note that at this stage the exact nature and dangers of the job have \textit{not} been communicated yet. \paragraph{Stage 2: Statement of Interest.} Job-seekers were allowed to state their interest in the position by filling out a low hurdle \emph{statement of interest} form. %\autoref{app:Statement_Interest} presents the complete list of socioeconomic information collected in this stage. The announcement evoked substantial initial interest and around 2,200 people expressed interest in the positions. We randomly assigned 1,105~job-seekers to the three treatments that we explain below. In this stage we elicited basic information of the job-seekers by asking them to fill out an ``Expression of interest'' form. We obtained information about gender, age, level of studies (undergraduate, master), area of studies and year of graduation.\footnote{Asking for gender is common in Colombia and there are no legal implications in doing so. } The measures that we obtained in this stage can be considered predetermined as we observe them before participants are exposed to the treatment. These measures are used to evaluate the qualification of job-seekers as explain in more detail below. Using self-reported measures, truthful reporting is of some concern as people have a tendency to misreport in order to increase their chances to be hired. We therefore provided incentives for truthful reporting by announcing that supporting documents will be required in order to be eligible for employment if selected. This policy was communicated whenever they had to enter verifiable information during the application process. \paragraph{Stage 3: Invitation to apply.} In this stage, about half of the job-seekers who submitted a statement of interest form were randomly assigned to be invited to continue the application process. This group to which we refer to as invited job-seekers, received an email explaining the particular conditions of employment regarding job responsibilities, salary and duration of the employment and an invitation to apply for the job (see~Table \ref{tab:Information}). At this stage we introduced the treatments and varied: the location of the job (low or high conflict area) and the wage compensation paid in the risky area. %A job offer to work in low risk regions at a specified salary of 1.5~million Colombian Pesos (COP) per month, a job offer to work in the high risk region at a specified salary of 1.5~million COP or to work in the high risk regions at a salary of 1.8~million COP. % say how we determined the risk premium (was sth liek the additional premium one has to pay for insurances in this region) Finally, we asked invited job-seekers to complete a lengthy application questionnaire in order to apply for the position. Filling out the application questionnaire took between 40 and 60~minutes and required to search supporting information on several questions which was announced to job-seekers. By using a demanding and time-consuming application questionnaire that would increase the cost of the application (time required), we wanted to capture the effect of opportunity cost of applying to the position. As recruitment processes are usually very comprehensive, we expected that invited job-seekers would not be surprised about the demanding application process.\footnote{We stratified job-seekers into treatment and control group based over gender and the main residence in Bogotá.} \paragraph{Stage 4: Application.} Invited job-seekers had access to a personalized page and could complete the application form over different sessions saving the information and continuing the application over several days. Yet, a strict deadline date was set after which no application was accepted. The measures of qualifications obtained in this stage are endogenous as performance in the tests and responses to the questionnaire could have been affected by the treatment. A summary of the information job-seekers obtained is presented in Table~\ref{tab:Information}. Invited job-seekers who were not interested in the job also had the chance to actively drop out of the application by clicking a button. In the questionnaire we asked the reasons why they left the application process. However, the turnout was low and only 6.4~percent of subjects who did not start the application process actively dropped out. Field experiments that last for a longer period and communication between subjects or potential subjects could cause information spillovers. We tried to minimize this by opening the position at the same time and by recording the starting time of the applications, in order to control for potential timing effects. \paragraph{Stage 5: Hiring.} Invited job-seekers who completed the application processes, to which we will refer to as applicants, were ranked upon qualifications. The top 10~applicants were invited for an interview. The best applicants received a job offer. We hired 3~applicants. We do not consider measures of on-the-job performance, as the limited number of positions offered does not allow us to conduct a statistical analysis of the impact on job performance. We use a between subject design that varied the the location of the job (low or high conflict area) and the wage compensation paid in the risky area.\footnote{As "no risk" jobs do not exist, we are not interested in looking at a certainty effect, a discontinuity in risk perception from no-risk to very small risks. Although theoretically interesting and documented in laboratory studies, "no-risk" environments do not exist and even in laboratory studies a "no risk" condition entails some not specified background risk (i.e. the experimenter has run out of cash or acts deceptively).}
Randomization Method
First stage collect a sample of job seekers. We use Stratified Randomization. In the first stage we stratified job-seekers over gender, resident of Bogota, and holding a master's degree. With stratum, we use computerized randomization.
Randomization Unit
Individual job seeker
Was the treatment clustered?
Experiment Characteristics
Sample size: planned number of clusters
no cluster sampling
Sample size: planned number of observations
1105 job seekers
Sample size (or number of clusters) by treatment arms
Low risk: 367,
High Risk - Low salary: 369,
High-Risk- High Salary: 369.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB Name
IRB Approval Date
IRB Approval Number
Post Trial Information
Study Withdrawal
Is the intervention completed?
Is data collection complete?
Data Publication
Data Publication
Is public data available?
Program Files
Program Files
Reports, Papers & Other Materials
Relevant Paper(s)