KEN_2013-2017_GABIE_v01_M
GET Ahead Business Training Program Impact Evaluation 2013 - 2017
Baseline & Follow-up Surveys
Name | Country code |
---|---|
Kenya | KEN |
1-2-3 Survey, phase 1 [hh/123-1]
The baseline survey was conducted in 2013. This was followed by four rounds of follow-up surveys, conducted in order to measure outcomes approximately one year and three years after training occurred (see timeline Appendix 1 of the Working Paper available under Related Materials). Two types of surveys were used. A comprehensive long-form survey collecting data on a wide range of business outcomes was used in rounds 2 and 4. These were supplemented by much shorter surveys in rounds 3 and 5. These short surveys were conducted two or three months after the long surveys, and were intended to provide a second observation on volatile business outcomes like sales and profits, as well as an additional opportunity to gather data from individuals who could not be found at the time of the long survey rounds. Appendix 6 describes how key outcomes are measured.
Sample survey data [ssd]
The scope of the study includes:
Kakamega and Kisii counties in the Western region, and Embu and Kitui counties in the Eastern region.
Women operating in markets in four counties in Kenya: Kakamega and Kisii in the Western region, and Embu and Kitui in the Eastern region
Name | Affiliation |
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David McKenzie | World Bank |
Name | Affiliation |
---|---|
Silvia Paruzzolo | International Labour Organization |
Susana Puerto | International Labour Organization |
Name |
---|
International Initiative for Impact Evaluation |
Private Enterprise Development in Low-Income Countries |
Strategic Research Program |
International Labour Organization |
World Bank - Jobs Umbrella multi-donor trust fund |
The selection of the study areas was the result of a participatory process that involved the Technical Committee of the ILO Women Entrepreneurship and Economic Empowerment (WEDEE) project as well as other relevant stakeholders. A Stakeholder retreat in October 2012 was used to pre-select 10 counties from the 47 counties in Kenya as possible locations for the study. A more detailed review of these 10 counties and consultations with the stakeholders were then used to select 4 counties in which to provide the ILO Gender and Entrepreneurship Together (GET Ahead) training: Kakamega and Kisii in the Western region, and Embu and Kitui in the Eastern region.
In each of Kakamega, Kisii, Embu and Kitui counties field staff from Innovations for Poverty Action, Kenya, mapped out all market centers deemed as medium or large outside of the main cities. Field staff then conducted a market census, applying a 31-question listing questionnaire to each female-owned enterprise operating on a non-market day in these markets. This questionnaire took a median time of 15 minutes to complete, and collected data on business type, education, age, profits and sales, membership in women's associations or merry-go-rounds, and contact follow-up information. The listing operation took place one county at a time between June 3, 2013 and November 1, 2013.
After the census, three markets in Kakamega county were dropped because the number of women in these markets was too few. Researchers then applied an eligibility filter to determine which women to include in the baseline survey. This filter required the women to have reported profits, and not to have reported profits that exceeded sales; to have a phone number that could be used to invite them for training; to be 55 years old or younger; to not be running a business that only dealt with phone cards or m-pesa, or that was a school; that the person responding not be an employee; that the business not have more than 3 employees; that the business have profits in the past week between 0 and 4000 KSH; that sales in the past week be less than or equal to 50,000 KSH; and that the individual had at least one year of schooling. These criteria were chosen to reduce the amount of heterogeneity in the sample (thereby increasing our ability to detect treatment effects), and to increase the odds of being able to contact and find individuals again.
Applying this eligibility filter reduced the 6,296 individuals to 4,037 individuals (64%). Out of a target of 4,037 individuals, the team was able to interview 3,538 (87.6%) in time to consider them for inviting to training.
Randomization process
The individuals who had satisfied the screening criteria and completed the baseline survey were then assigned to treatment and control in a two-stage process:
First, markets were assigned to treatment (have some individuals in them invited to training) or control (no one in the market would be invited to training) status. Randomization was done within 35 strata defined by geographical region (within county) and the number of women surveyed in the market.
Then within each market, individuals were assigned to treatment (be invited to training) or control (not be invited to training) within treated markets by forming four strata, based on quartiles of weekly profits from the census (<=450, 451-800, 801-1500, 1501-4000), and then assigning half the individuals within each strata to training. When the number of individuals in the strata was odd, the odd unit was also randomly assigned to training. This resulted in 1,173 of the 2,161 individuals in treated markets being assigned to treatment, and 988 to control groups.
Additoinal details on sampling are abailable in Section 2 of the Working Paper provided under Related Materials.
Overall we were able to interview 95.0 percent of the sample in at least one of round 2 or 3, and 92.3 percent in at least one of round 4 or 5. In addition, in cases where we were unable to interview someone due to refusal, travel, death, or other reasons, we collected information from other household members or close contacts on whether the individual in our sample was currently operating a business. This enables us to have data on survival status for 99.3 percent of the sample at one year, and 97.2 percent at three years. There is no significant difference in data availability with treatment status at the three year horizon, although those assigned to treatment are 1 to 2 percentage points more likely to have data available at the one year horizon. See Appendix Table 2 of the working paper provided under Related Materials details response rates.
No weights used
The following survey instruments were used for data collection:
The Market census questionnaire took a median time of 15 minutes to complete. It collected data on business type, education, age, profits and sales, membership in women's associations or merry-go-rounds, and contact follow-up information.
The baseline questionnaire took a median time of 90 minutes to complete. The 30-page questionnaire asked detailed questions about the business owner, her family and business activities.
Start | End | Cycle |
---|---|---|
2013-06-01 | 2013-11-01 | Listing and baseline surveys |
2014-06 | 2014-10 | Round 2 follow-up |
2014-11 | 2015-02 | Round 3 follow-up |
2016-02 | 2016-07 | Round 4 follow-up |
2016-05 | 2016-10 | Round 5 follow-up |
2017-06 | 2017-08 | Long-run market census and customer survey |
Name |
---|
Innovations for Poverty Action Kenya |
Is signing of a confidentiality declaration required? | Confidentiality declaration text |
---|---|
yes | Identifying information have been removed to anonymize the data for public release. It should only be used for research purposes, with no attempt made to identify individual participants. |
Public access granted for research purposes only
The use of the datasets must be acknowledged using a citation which would include:
Example:
David McKenzie and Susana Puerto (2017) "Growing Markets through Business Training for Female Entrepreneurs: A Market-Level Randomized Experiment in Kenya", Mimeo. World Bank. Ref. KEN_2013-2017_GABIE_v01_M. Dataset downloaded from [URL] on [date].
The user of the data acknowledges that the original collector of the data, the authorized distributor of the data, and the relevant funding agency bear no responsibility for use of the data or for interpretations or inferences based upon such uses.
Name | |
---|---|
David McKenzie | dmckenzie@worldbank.org |
DDI_KEN_2013-2017_GABIE_v01_M_WB
Name | Affiliation | Role |
---|---|---|
Development Data Group | World Bank | Study documentation |
2017-12-11
v03 (December, 2017)
This is an update from DDI_KEN_2013_GABIE-B_v01_M_WB
Data and documents for rounds 2, 3, 4 and 5 added.
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