Supporting young adults neither in education, emplyment or training

2023-02-20

2023-12-31

Sub project 1:
(1) Register data measures on labor market participation or education enrollment: employed, enrollment in education, participation in labor market programs initiated by NAV, and social security benefits.

(2) Survey measures on current life situation: 1) Perceived stress (PSS-10; Cohen and Williamson, 1988); 2) Warwick-Edinburgh mental well-being scale (Stewart-Brown et al., 2009; Tennant et al., 2007); 3) Symptoms checklist (SCL-10; Strand et al., 2003).

In follow up studies we will also look at educational attainment and income in registry data.

Sub project 2:
(1) Indicator for consent, indicator for program completion, time spent on program and surveys
(2) Survey measures on personality: 1) Time preferences (Falk et al., 2022); 2) Conscientiousness (BFI-2 XS, Soto and John, 2017); 3) Self efficacy (Gaumer Erickson et al., 2016)
(3) Survey measures on current life situation: 1) Perceived stress (PSS-10; Cohen and Williamson, 1988); 2) Warwick-Edinburgh mental well-being scale (Stewart-Brown et al., 2009; Tennant et al., 2007); 3) Psychological distress symptoms checklist (SCL-10; Strand et al., 2003).

Sub project 1:
Mechanisms
(1) Self efficacy (Gaumer Erickson et al., 2016)
(2) Stress mindset (Yeager et al., 2021)
(3) Procrastination (IPS; Steel, 2002 & 2010; translation to Norwegian by Svartdal, 2015)

Treatment Fidelity
(1) Fixed mindset scale (Yeager, 2016)
(2) Stress manipulation test (Yeager et al., 2021)

Other outcomes
(1) Locus of control (Pearlin and Schooler, 1978)
(2) Number of job applications (Bjorvatn et al., 2021)
(3) Knows future career (Resnjanskij et al., 2021).

All youths registered at the Norwegian public employment office (NAV) will be invited to participate. After consenting to participation, logging on to the web application and completing the baseline survey, subjects will be automatically randomized to different treatment arms, involving different material in the web application. The participants will receive an SMS each week for five consecutive weeks, with a note that a new session in the app is available. There will also be reminders to complete the content in each session. Subjects are blind to treatment.

Not available

Randomization of the invitation with and without incentives will be performed by a computer at NAV Statistical section at the individual level, with a witness present.
Randomization happens individually and is automated in the app at the individual level.

In both subprojects we randomize at the individual level with equal probability of each treatment.

No

1300 Individuals

First field implementation: We invite all youths registered as unemployed during the last two months at NAV Rogaland aged 18-29 years, for whom NAV has not registered any special difficulties making regular employment highly unlikely. This will give us a sample size of about 1300. Based on previous studies, we expect 383 participants to sign up. Second field implementation: We will invite all youths aged 18-29 years registered at NAV in Norway except from Rogaland, for whom NAV has not registered any special difficulties. We will have a first-serve policy up until at least 1000 youths have consented to participate.

First field implementation:
The 1300 youths are randomized to receive an invitation to experiment A or B with equal probability, leading to 650 participants in each group. We expect a 35 % uptake in A and a uptake of 24 % in B, giving a sample of 227 in A and 156 in B .

Second field implementation:
We will have 500 in treatment and 500 in control.

First field implementation: We calculate the MDES using Optimal Design Software (Spybrooke et al., 2001). For investigating effects on participation, we have 1300 observations. With this sample size, we are able to detect an effect of 0.16 standard deviations assuming 80 percent power and a two-sided 95-percent confidence interval. Assuming as above a 35% response rate, this translates into a difference of 7 percentage points. For investigating effects on selection, our sample is reduced to those who agreed to participate in the project. We expect our sample to be 383 individuals with 227 in experiment A and 156 in experiment B. This reduces the MDES to 0.30 standard deviations when comparing differences between participants in A and B. Second field implementation: We calculate the MDES using Optimal Design Software (Spybrooke et al., 2001). We have individual-level randomization and a sample of 1000 youths who have signed-up to participate. This sample is equally split between treatment and control. Assuming 80 percent power and a two-sided 95-percent confidence interval, we have a MDES of 0.18 standard deviations, not including any control variables. If we assume that control variables explain 20% of the variation in the outcome variables (R^2=0.2) the MDES reduces to 0.16 standard deviations. If we assume an R^2 of as high as 0.8 (as we observe baseline values of most of our outcome variables), it would even go down to 0.08 standard deviations. Adding data from the first field implementation may give us even more precision.