Active Labour Market Policies in Addis Ababa

Last registered on October 13, 2015

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Pre-Trial

Trial Information

General Information

Title

Active Labour Market Policies in Addis Ababa

RCT ID

AEARCTR-0000911

Initial registration date

October 13, 2015

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

October 13, 2015, 10:58 AM EDT

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

Locations

Country

Ethiopia

Region

Primary Investigator

Name

Simon Franklin

Affiliation

London School of Economics

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

Paolo Falco

PI Affiliation

OECD

Contact Investigator

PI Name

Girum Abebe

PI Affiliation

Ethiopian Development Research Institute

Contact Investigator

PI Name

Marcel Fafchamps

PI Affiliation

Stanford University

Contact Investigator

PI Name

Stefano Caria

PI Affiliation

University of Oxford

Contact Investigator

PI Name

Simon Quinn

PI Affiliation

University of Oxford

Contact Investigator

Additional Trial Information

Status

Completed

Start date

2014-03-01

End date

2015-09-01

Keywords

Labor

Additional Keywords

JEL code(s)

J64, J68, O18, O15

Secondary IDs

Abstract

This study evaluates the impacts of three different active labour market policies for unemployed youth in Addis Ababa, Ethiopia. We look at the impacts of the following interventions: i) a transport subsidy to lower the costs of searching for employment in the centre of the city, ii) a screening intervention where individuals are given a range of personnel tests and issued with certificates reporting their outcomes for these test (this intervention is given with training on how these certificates can be used to apply for jobs), and iii) a job fairs intervention, where individuals are invited to attend an event where they can interact with a number of large firms looking looking for recruits. The project also seeks to estimate the spillover effects from treated individuals to untreated individuals in neighborhoods where a randomly varying proportion of unemployed individuals are given the treatments.

External Link(s)

Registration Citation

Citation

Abebe, Girum et al. 2015. "Active Labour Market Policies in Addis Ababa." AEA RCT Registry. October 13. https://doi.org/10.1257/rct.911-1.0

Former Citation

Abebe, Girum et al. 2015. "Active Labour Market Policies in Addis Ababa." AEA RCT Registry. October 13. https://www.socialscienceregistry.org/trials/911/history/5591

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Experimental Details

Interventions

Intervention(s)

See Section 4 of our (publicly available) pre-analysis document attached.

Intervention Start Date

2014-09-01

Intervention End Date

2015-02-28

Primary Outcomes

Primary Outcomes (end points)

See Sections 5 and 7 of our (publicly available) pre-analysis document attached.

Primary Outcomes (explanation)

See Sections 5 and 7 of our (publicly available) pre-analysis document attached.

Secondary Outcomes

Secondary Outcomes (end points)

Secondary Outcomes (explanation)

Experimental Design

See Section 3 of our (publicly available) pre-analysis document attached.

Experimental Design Details

Randomization Method

See Section 3 of our (publicly available) pre-analysis document attached.

Randomization Unit

See Section 3 of our (publicly available) pre-analysis document attached.

Was the treatment clustered?

Yes

Experiment Characteristics

Sample size: planned number of clusters

234 local areas.
See Section 3 of our (publicly available) pre-analysis document attached for more detail.

Sample size: planned number of observations

4096 individuals. See Section 3 of our (publicly available) pre-analysis document attached for more detail.

Sample size (or number of clusters) by treatment arms

See Section 3 of our (publicly available) pre-analysis document attached.

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

We anticipate that our job search interventions will have impacts on a number of job‐search behaviors, type of employment, duration of unemployment spells, and wages. However, finding full‐time,permanent and formal employment in Addis Ababa is challenging, and thus a change in this single binary outcome will likely be the most challenging impact to detect. Our study has been designed and the sample size chosen with the intention of having adequate power to detect this effect. Our power estimates should therefore be understood as the most conservative, lower bound estimates of the power of our design. We calculate power through detailed simulation designs (corroborated with analytical results from comparable and simpler designs, run in the software package “Optimal Design”) in the presence of large spillovers (contamination) and possibly high intra‐cluster correlation (ICC) of up to 30%. ICC’s are introduced through the simulation of a cluster specific employment shock. In these “worst case scenarios”, we are able to detect a 5 ppt increase in the probability of employment on a baseline probability of 10% more than 80% of the time, with the size of the test at the 5% significance level. It should be noted that in the binary outcomes framework this effect is equivalent to a one sixth (1/6) standard deviation (sd) standardized effect size, much smaller than the usual rule of thumb of 0.2 sds suggested by Cohen (1998). There are numerous ways in which we expect to have more power by looking at various other intermediate outcomes, or more easily influenced job‐market outcomes. Recall that the binary model estimates above could be estimated at each period in our high frequency data set, giving us the power to estimate detailed impact trajectories. However if we wanted to look at average outcomes over time we might have considerably more power from pooling all post‐treatment observations. As argued by McKenzie (2011), this is especially useful when outcomes of interest are measured with error or are weakly correlated over time, which could certainly be the case for temporary employment outcomes. When employment is relatively informal, wages and hours worked could be irregular and difficult to recall. At a single snapshot, treated individuals might not be significantly more likely to be employed, but the cumulative effect averaged over a few months might be significantly different.

Supporting Documents and Materials

IRB