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Promoting a subsidy for hiring older unemployed workers
Last registered on December 06, 2016


Trial Information
General Information
Promoting a subsidy for hiring older unemployed workers
Initial registration date
December 06, 2016
Last updated
December 06, 2016 3:43 AM EST
Primary Investigator
CPB Netherlands Bureau for Economic Policy Analysis
Other Primary Investigator(s)
PI Affiliation
Erasmus University Rotterdam
PI Affiliation
Amsterdam Economics
Additional Trial Information
In development
Start date
End date
Secondary IDs
This study analyzes the effects of promoting an existing subsidy for hiring older unemployed workers.
External Link(s)
Registration Citation
Bosch, Nicole, Robert Dur and Lucy Kok. 2016. "Promoting a subsidy for hiring older unemployed workers." AEA RCT Registry. December 06. https://doi.org/10.1257/rct.1321-1.0.
Former Citation
Bosch, Nicole et al. 2016. "Promoting a subsidy for hiring older unemployed workers." AEA RCT Registry. December 06. http://www.socialscienceregistry.org/trials/1321/history/12252.
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Experimental Details
Intervention Start Date
Intervention End Date
Primary Outcomes
Primary Outcomes (end points)
There are two key outcome variables: 1. the number of hired workers aged 56 and above 2. the number of workers aged 56 and above for which a wage cost subsidy is paid. We will also study effects on hiring of other types of employees to see whether substitution takes place and, if so, from what type of employees. Lastly, we will do analysis for the subgroup of employers who, before the experiment, did hire old unemployed workers, but did not sign up for the subsidy. We expect the largest effects on the take up of the wage subsidy for these employers, since they are apparently offer jobs that match well with the subsidiy's target group and they are likely unaware of the subsidy before receiving the letter (for otherwise they would have applied for it). Moreover we will do an analysis for the subgroup of employers who, before the experiment, did not hire old unemployed workers.
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
Analyze the consequences of promoting the wage cost subsidy.
Experimental Design Details
Note that in our data we use fiscal identities as a sampling unit. Large firms with multiple locations receive one letter at the office, otherwise they could receive several different letters. Our data do not allow us to to send different letters to the different locations. Our full sample consists of 40,420 firms, of which 3552 are large firms that have used the subsidy in the past, 6872 are large firms that have not used the subsidy in the past, 3324 are small firms that have used the subsidy in the past, and 26672 are small firms that have not used the subsidy in the past. Within each of these four categories, firms have been randomly assigned to untreated control and three treatment groups. For each type of firm (large, small, made use, did not make use), we have equal numbers of firms in each of these four groups (control and three treatments).
Randomization Method
Randomization done by a computer.
Randomization Unit
Was the treatment clustered?
Experiment Characteristics
Sample size: planned number of clusters
- two firm size classes (large and small)
- within each class two groups: current users and non-users
- three treatment groups and one control group
Sample size: planned number of observations
Sample size (or number of clusters) by treatment arms
40,420 firms of which 30,315 firm receive a letter.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
In our first power calculations (before drawing the actual sample) the minimal detectable size for detecting a difference in hiring rates was as follows: Large firms: sample of 1400 per group, 90% power (effect 10%) and 60% power (effect 5%) Smaller firms: sample of 8000 per group, 42% power (effect 10%) (we do notice that power is too low, but for policy relevance we run this separate experiment for smaller firms) After drawing the actual sample, the numbers changed a bit. Large firms: 2.500 Smaller firms: 7.600 Power 90% (effect 10%) and power 60% (effect 5%) Smaller firms: 40% power (effect 10%) For small firms power might not be enough, only for large effects or combination of letters. Because of their policy relevance, we did not drop smaller firms from the sample, but instead run a separate experiment on them.
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)