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How Does Part-time Work Affect Labor Productivity? Evidence from a Field Experiment on Female Data Entry Clerks in Ethiopia

Last registered on April 20, 2022

Pre-Trial

Trial Information

General Information

Title
How Does Part-time Work Affect Labor Productivity? Evidence from a Field Experiment on Female Data Entry Clerks in Ethiopia
RCT ID
AEARCTR-0001829
Initial registration date
December 04, 2016

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
December 04, 2016, 8:25 PM EST

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

Last updated
April 20, 2022, 5:40 AM EDT

Last updated is the most recent time when changes to the trial's registration were published.

Locations

Region

Primary Investigator

Affiliation
Cornell University

Other Primary Investigator(s)

PI Affiliation
Cornell University, Samuel Curtis Johnson Graduate School of Management
PI Affiliation
University of Kansas

Additional Trial Information

Status
Completed
Start date
2016-05-01
End date
2017-12-31
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
This project studies the effects of part-time work (compared to full-time) on selection and productivity in the context of data entry jobs in Ethiopia. We will examine these issues in a close collaboration with Africa Future Foundation (AFF), an international non-governmental organization (NGO) that will hire part- and full-time data entry clerks in Ethiopia. This study will make two sets of contributions. First, it will contribute to the literature on how job attributes (i.e., compensation schemes and labor contracts) affect worker selection and labor productivity by examining how part-time employment affects productivity. Second, this study will explore what types of applicants will be self-selected into part-time jobs, which are preferred by women especially those who have young children. Findings from our study will have important implications for labor markets in which women could benefit from part-time work.
External Link(s)

Registration Citation

Citation
Zhu, John, Hyunseob Kim and Hyuncheol Kim. 2022. "How Does Part-time Work Affect Labor Productivity? Evidence from a Field Experiment on Female Data Entry Clerks in Ethiopia." AEA RCT Registry. April 20. https://doi.org/10.1257/rct.1829
Former Citation
Zhu, John, Hyunseob Kim and Hyuncheol Kim. 2022. "How Does Part-time Work Affect Labor Productivity? Evidence from a Field Experiment on Female Data Entry Clerks in Ethiopia." AEA RCT Registry. April 20. https://www.socialscienceregistry.org/trials/1829/history/141396
Sponsors & Partners

Sponsors

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Experimental Details

Interventions

Intervention(s)
Intervention (Public)

The original study design included 81 village groups. However, because of security concerns, 10 village groups in Ejere were excluded from the study sample. The original design also included long-term employment and further randomization at the data-entry unit. However, because AFF had to evacuate from the study areas due to political turmoil, during which more than 500 people are estimated to have been killed, it was not able to proceed as planned. AFF was able to hire 122 applicants (61 full time and 61 part time) as interns, and decided to evacuate from the study area at the end of 2017.

See https://www.theguardian.com/world/2016/oct/02/ethiopia-many-dead-anti-government-protest-religious-festival.

AFF distributed job flyers to women with high school diploma during a census of approximately 22,000 households in the catchment area. Part-time job opportunities were given to randomly-selected 35 villages and full-time opportunity to the remaining 36 villages. The total number of people who submitted application was 456 (preliminary job applicants). From December 19 to 31, 2016, 333 job applicants participated in a baseline job survey and job aptitude tests (job applicants)

Among those who completed the baseline job survey and job aptitude tests, 122 applicants joined three-week long training sessions (interns) in August to December 2017 (five batches), which entailed basic computer training (such as Excel), specifics of data entry work, and tests.
Intervention Start Date
2016-07-01
Intervention End Date
2017-12-31

Primary Outcomes

Primary Outcomes (end points)
First, we are interested in multi-dimensional self-selection to the jobs offered. In particular, we explore whether worker’s self-selection is based on measurable characteristics including 1) demographic factors such as age and family structure (e.g., existence of parental and spousal support), 2) socioeconomic status such as levels of education and income, and 3) ability and motivation and expectations for work.

Second, we measure selection effects of part-time jobs on labor productivity. We collected various information that captures the quantity and quality of data entry clerks’ labor productivity such as quantity and quality of the data entry during the training and at work. Specifically, we measured the number of total entries (per hour) and fraction of entries with errors, which allow us to measure quality-adjusted labor productivity. We also measured other aspects of job performance capturing work ethics and working hours: whether the clerk comes to the work (absenteeism).
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
1. Experiment Chronology

AFF, the collaborating NGO, hired full- (eight hours per day) and part-time (four hours per day) female data entry clerks in their catchment area. AFF distributed job flyers to women with high school diploma during a census of approximately 22,000 households in the catchment area from May to July 2016. Part-time job opportunities were given to randomly selected 35 villages and full-time opportunity to the remaining 36 villages. Flyers provide information on the job description, working conditions including part- or full-time work, and expected salaries and benefits. A resume and a copy of high school graduation exam report were required to apply for both types of jobs. The total number of preliminary applicants was 456. From December 19 to 31, 2016, 333 job applicants participated in a baseline job survey which measures demographics, educational background, employment history, household income and assets, job preferences and motivation, personality, cognitive skills (clerical and computation abilities), fine motor ability, and basic computer skills.

Among those who completed the baseline job survey, 122 applicants joined three-week long training sessions in August to December 2017 as interns, which entails basic computer training (such as Excel), specifics of data entry work, and tests. Also, interns practiced data entry at least for an hour every day.

2. Data

The primary data sources are (1) census data including household demographic and socioeconomic information, (2) administrative data used to select data entry clerks including the baseline job survey and training outcomes, and (3) data on job performance including labor productivity and attendance rates.

The census covers approximately 22,000 households in the catchment area, where the job flyers have been distributed. The census has collected a variety of socioeconomic, health, psychological variables. This project uses key household characteristics such as the number of children, marital status, intra-household dynamics, and household income to examine the characteristics of workers who select into part-time (relative to full-time) jobs.

The baseline job survey collected information on demographics, educational background, employment history, household income and assets, job preferences, attitudes and expectations toward work, and cognitive skills (measuring clerical and computation abilities). Fine motor ability and basic computer skills will be also measured.

Data on workers’ performance at the data-entry firm were collected by AFF from August to December 2017. Data collection on job performance began from training sessions. During the training, AFF collected training performance such as quiz score to measure understanding of the computer program, and quantity and quality of data entry output. Also AFF collected information on labor productivity at work. Data entered by each clerk were automatically stored on a central server from individual laptop computers.
Experimental Design Details
Randomization Method
Assigning the treatment to villages in which individuals reside was done randomly in the project office of AFF by a computer random number generator. Job applicants were not aware of this randomization. It remains unknown to them even after they are hired.
Randomization Unit
Village groups were randomly assigned to full- vs. part-time groups.
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
35 villages for part-time jobs and 36 villages for full-time jobs.
Sample size: planned number of observations
Women of approximately 22,000 households in the catchment area. The job flyers were distributed by AFF to women who have a high school diploma during the census of approximately 22,000 households in the catchment area from May to July 2016. The number of individuals who applied to either full- or part-time jobs is 456 (230 from part-time villages and 226 from full-time villages). 333 job applicants (162 from part-time villages and 171 from full-time villages) joined the job aptitude tests and survey, and 122 people (61 from part-time villages and 61 from full-time villages) worked as interns for 15 working days.
Sample size (or number of clusters) by treatment arms
1. First-round RCT (job application)
Group 1 (35 villages): part time job applicants
Group 2 (36 villages): full time job applicants

2. First-round RCT (intern: 15 working days)
Group 1 (35 villages): part-time employees during the internship
Group 2 (36 villages): full time employees during the internship
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials

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IRB

Institutional Review Boards (IRBs)

IRB Name
Institutional Review Board for Human Participants, Cornell University
IRB Approval Date
2016-05-07
IRB Approval Number
1604006319
Analysis Plan

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Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
Yes
Intervention Completion Date
December 31, 2017, 12:00 +00:00
Data Collection Complete
Yes
Data Collection Completion Date
December 31, 2017, 12:00 +00:00
Final Sample Size: Number of Clusters (Unit of Randomization)
Group 1 (35 villages): part time job applicants
Group 2 (36 villages): full time job applicants
Was attrition correlated with treatment status?
No
Final Sample Size: Total Number of Observations
6,295 eligible women from 22,000 households, 456 preliminary job applicants, 333 job applicants, and 122 interns
Final Sample Size (or Number of Clusters) by Treatment Arms
The job flyers were distributed by AFF to women who have a high school diploma during the census of approximately 22,000 households in the catchment area from May to July 2016. The number of individuals who applied to either full- or part-time jobs is 456 (230 from part-time villages and 226 from full-time villages). 333 job applicants (162 from part-time villages and 171 from full-time villages) joined the job aptitude tests and survey, and 122 people (61 from part-time villages and 61 from full-time villages) worked as interns for 15 working days.
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials