Attitudes towards Digitalization and Automatization in the Labor Market.

Last registered on February 15, 2019

Pre-Trial

Trial Information

General Information

Title
Attitudes towards Digitalization and Automatization in the Labor Market.
RCT ID
AEARCTR-0003888
Initial registration date
February 14, 2019

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
February 15, 2019, 4:13 PM EST

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

Locations

Region

Primary Investigator

Affiliation
University of Mannheim

Other Primary Investigator(s)

PI Affiliation
ZEW Mannheim
PI Affiliation
ZEW Mannheim

Additional Trial Information

Status
In development
Start date
2019-02-18
End date
2019-02-28
Secondary IDs
Abstract
The implications of digitaization and automatization for the labor market and employment are heavily debated, both in the public and among academics. While some of the press coverage leaves the impression that the demand for human work will substantially decrease as a response to automatization and digitalization, some recent academic work finds evidence that automatization and digitalization increase overall human employment but have distributional consequences. In light of the public and academic debate, we study attitudes towards digitalization and automatization using a large-scall representative survey with experimental components in Germany and the US.

We survey many different attitudes, opinions, information and perceived employment threats in the context of recent developments in the labor market. We also ask for potential policy implications, attitudes towards redistribution and have a real behavioral outcome variable where we offer the actual possibility to donate money for charity. One aim of our study therefore is to gain a detailed picture of the perceptions in the context of the topic among the general public. We also include a randomized component into the survey where we manipulate the level of information about the topic. The information treatments confront participants with the results of a recent academic paper by Graetz and Michaels (2018, ReStat). This experimental component aims at studying whether biased information about the topic change attitudes and perceptions. An additional randomized component varies the order along which our questions are presented: we sometimes first ask general questions whereas sometimes we first ask specific questions about automatization and digitalization. This component helps to study if the general awareness of the topic changes partcioants' perceptions.

External Link(s)

Registration Citation

Citation
Blesse, Sebastian, Philipp Doerrenberg and Arntz Melanie. 2019. "Attitudes towards Digitalization and Automatization in the Labor Market.." AEA RCT Registry. February 15. https://doi.org/10.1257/rct.3888-1.0
Former Citation
Blesse, Sebastian, Philipp Doerrenberg and Arntz Melanie. 2019. "Attitudes towards Digitalization and Automatization in the Labor Market.." AEA RCT Registry. February 15. https://www.socialscienceregistry.org/trials/3888/history/41609
Experimental Details

Interventions

Intervention(s)
We randomly split all survey participants into four groups:

Group 1: Neutral information; normal order of questions (control group)
Group 2: Neutral information; order of questions different than for group 1
Group 3: Information about employment effects of automatization (based on Graetz and Michaels (2018, ReStat)); order of questions as in group 2
Group 4: Information about distributional effects of automatization (based on Graetz and Michaels (2018, ReStat)); order of questions as in group 2
Intervention Start Date
2019-02-18
Intervention End Date
2019-02-28

Primary Outcomes

Primary Outcomes (end points)
We are potentially interested in the efefcts of the randomized treatments on any of the questions that we ask. In partucluar, we are interested in the effect on a set of variables asking about the personal and general consequences of automatization and the policy implications of these trends (including an actual behavioral outcome variable based on a charity possibility that we offer to participants)
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
a) We include a randomized component into the survey where we manipulate the level of information about the topic. The information treatments confront participants with the results of a recent academic paper by Graetz and Michaels (2018, ReStat). This experimental component aims at studying whether biased information about the topic change attitudes and perceptions.

b) An additional randomized component varies the order along which our questions are presented: we sometimes first ask general questions whereas sometimes we first ask specific questions about automatization and digitalization. This component helps to study if the general awareness of the topic changes partcioants' perceptions.

Overall, we randomly split all survey participants into four groups:

Group 1: Neutral information; normal order of questions (control group)
Group 2: Neutral information; order of questions different than for group 1
Group 3: Information about employment effects of automatization (based on Graetz and Michaels (2018, ReStat)); order of questions as in group 2
Group 4: Information about distributional effects of automatization (based on Graetz and Michaels (2018, ReStat)); order of questions as in group 2
Experimental Design Details
Randomization Method
Randomization done by computer
Randomization Unit
Individual survey participants
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
5000
Sample size: planned number of observations
5000
Sample size (or number of clusters) by treatment arms
Evenl split into four groups: about 1250 observations per randomized group.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number

Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials