Stimulating controlled or autonomous motivation of job seekers. What works best?

There are two treatment groups conditions (controlled or autonomous motivation) and one control group. The controlled treatment group condition receives a series of six electronic messages (of about 100-120 words) during the first half year of unemployment (so conditional on still being unemployed). The content of these messages aims at reinforcing external pressure. The autonomous treatment group condition also receives these six messages according to the same timing, but their content aims rather at triggering autonomy, competence and relatedness, the three inborn needs of people according to the Self-Determination Theory. The control group receives no messages.

2020-02-26

2021-06-30

(i) Self-determined motivation based on surveys sent to the participants and the control group about 3.5 months after entry in unemployment;

(ii) Job search intensity (quantity): number of hours typically spent on job search activities in the past weeks (survey sent 3.5 months after entry in unemployment) and the number of reported job applications and other job search activities (from register data);

(iii) Job search quality measured by the extent of engagement in metacognitive activities (surveys about 3.5 months and 9 months after entry in unemployment);

(iv) Outcomes of job search: job finding and job quality as measured by objective indicators (such as the wage, unemployment duration, employment status and the contractual and effective job duration) and by subjective indicators of job satisfaction (based on exit surveys sent one month after exit from unemployment).

The measure of self-determined motivation is based on the psychological scale in Da Motta Veiga and Gabriel (2016), while the measure of job search quality measured by the extent of engagement in metacognitive activities is based on a measure developed by Turban, Stevens and Lee (2009). For both measures some items are slightly modified to take into account the specific local context and the requirements of the PES.

References:
Da Motta Veiga, Serge P. & Grabriel, Allison S. (2016). The Role of Self-Determined Motivation in Job Search: A Dynamic Approach. Journal of Applied Psychology, 101(3), 350-361.

Turban, D. B., Stevens, C. K., & Lee, F. K. (2009). Effects of conscientiousness and extraversion on new labor market entrants’ job search: The mediating role of metacognitive activities and positive emotions. Personnel Psychology, 62, 553–573.

An interaction between, on the one hand, the aforementioned measure of self-determined motivation as measured in an initial survey within the first month of registration in unemployment, and, on the other hand, outcomes in a) the administrative data, b) the intermediate survey (about 3.5 months after entry in unemployment) and the outcome survey (one month after exit from unemployment). This aims, amongst other, at measuring the moderating effect of initial motivation on the other outcomes.

The population involved in this RCT will consist of a 2/3 random sample of all Swedes who start a spell of insured unemployment between January 20, and December 19, 2020.

There are two treatment group conditions (controlled or autonomous motivation) and one control group, all individuals who entered unemployment and registered at the Public Employment Service (PES) at some point between January 20, and December 19, 2020. The size of the control group is double as large as that of the treatment groups. The controlled treatment group condition receives a series of six electronic messages (of about 100-120 words) during the first half year of unemployment (so conditional on still being unemployed). The content of these messages aims at reinforcing external pressure. The autonomous treatment group condition also receives these six messages according to the same timing, but their content aims rather at triggering autonomy, competence and relatedness, the three inborn needs of people according to the Self-Determination Theory. The control group receives no messages.

Not available

The randomization is based on birthday (date-of-the-year). All born on 1/6 of the days of the year is assigned to the controlled condition, 1/6 of the days to the autonomous condition, 1/3 to the control group and 1/3 to a treatment condition unrelated to our research. The days of the year are allocated to these groups based on numbers drawn by a computer.

The randomization is done at the individual level.

No

2/3 of all individuals who entered unemployment in Sweden and registered at the Public Employment Service between January 20, and December 19, 2020 will be sampled. This is expected to comprise between 82,500 and 105,000 individuals.

2/3 of all individuals who entered unemployment in Sweden and registered at the Public Employment Service between January 20, and December 19, 2020 will be sampled. This is expected to comprise between 82,500 and 105,000 individuals.

The treatment groups are each of size ¼ of the total sample size, i.e. in expectation between 20,625 to 26,250 individuals. The control group is expected to be ½ of the total sample size, i.e. 41,250 to 52,500 individuals.

For the power calculations we consider two sets of outcome variables. The first set of outcome variables uses the administrative files and can, hence, be based on the full sample. The second set of outcome variables are based on 3 electronic surveys sent by e-mail: (1) an initial survey at the start of the unemployment spell, (2) an intermediate survey after about 3.5 months of unemployment, and (3) an exit survey one month after exit from unemployment. We plan to send initial surveys to about 50% of the retained treated sample and to about 25% of the control sample, i.e. to between 10,300 and 13,125 individuals in each treatment arm (between 30,900 and 39,375 individuals in total). By contrast, intermediate surveys are planned to be sent to the full treatment population and 50% of the control population, i.e. to between 20,600 and 26,250 individuals in each treatment arm (between 61,800 and 78,750 individuals in total). Nevertheless, these numbers should be regarded as targets. Due to budgetary and administrative restrictions we might have to reduce these targets. Based on a pilot survey we set the first survey response rate equal to 30%. Conditional on response in a prior survey, the subsequent response rate is assumed to be 70%. These response rates are taken into account when conducting the power analysis for the outcomes that are based on the survey outcomes. For outcomes based on the administrative data only, these response rates are set to 100% Since we have no clear a priori about the means and standard deviations of the outcome variables, we conduct the power analyses for two cases: (1) for a standardized outcome variable, i.e. a variable of which the mean is normalized to zero and the standard deviation to one, and for (2) a discrete dichotomous variable with two possible outcomes and with mean equal to 0.5. We set in all power analyses the significance level to 5% and the power to 80%. We consider for each outcome three different effects: treatment 1 (T1) versus control (C), T2 versus C and T1 versus T2. Because the control group is double as large as each of the treated groups, the power analysis for the outcome variables based on administrative files differs according to the considered treatment effect: the minimum detectable effect size (MDE) will be larger for the contrast between T1 and T2 than for the other two contrasts. By contrast, since in the surveys we set the sample size to be equal for each treatment arm, the MDE is the same for all three treatment effects. 1. Set of outcome variables based on admin files (i) For the standardized outcome (mean equal to zero and standard deviation equal to one) the MDE ranges, depending on aforementioned sample size ranges, between 0.0212 and 0.0239 of a standard deviation when we consider the contrasts between T1 (or T2) and C. For the contrast between T1 and T2 the MDE ranges between 0.0245 and 0.0276. (ii) For the binary discrete outcome with mean equal to 0.5, the MDE for the contrasts between T1 (or T2) and C ranges between 0.0106 and 0.0119. For the contrast between T1 and T2 it ranges between 0.0130 and 0.0146. 2. Set of outcome variables based on the intermediate and the outcome surveys. For outcomes based on these surveys, we consider that the response rate of 30% reduces the effective sample size by treatment arm to between 6,180 and 7,950. Hence the MDE of a standardized outcome ranges between 0.0444 and 0.0504, while for a binary discrete outcome it ranges between 0.0222 and 0.0252. 3. Set of outcome variables based on an interaction between variables in the initial survey on the one hand, and, on the other hand, outcomes in a) the administrative data, b) the intermediate survey and the outcome survey. This aims, amongst other, at measuring the moderating effect of initial motivation on the other outcomes. (i) the administrative data, the effective survey size must only be reduced by the non-response rate (70%) in the initial survey. The effective sample size by treatment arm ranges therefore between 3,090 and 3,938. The MDE of a standardized interacted outcome therefore ranges between 0.0631 and 0.0713. (ii) the intermediate survey and the outcome survey, the effective survey size must be reduced by the non-response rate in the initial survey (70%) as well as by the non-response response rate in the intermediate survey, conditional on response in the initial survey (30%). The effective sample size by treatment arm therefore ranges between 2,163 and 2,757. The MDE of a standardized interacted outcome therefore ranges between 0.0755 and 0.0852.