Girl's Economic Empowerment: Long Term Evidence from a Randomized Experiment in Tanzanian Schools

2013-07-01

2013-09-30

B1, B2 /B3, B4, G1, G2, E1, E2, W1, W2, W3, W4, W5 as described below

Behavior:

B1. Pregnancy. Indicator variable=1 if the girl is or has been pregnant. This is question 148 in the long-term follow-up questionnaire.

B2 = Startup of business/farming. Indicator variable=1 if the girl has business and/or a farming/poultry/livestock activity. This is question 27, 28 and 29 in the long-term follow-up questionnaire.

B3= Sales in a normal week from these business and/or farming/poultry/livestock activities. This is the sum of question 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128 & 132 in the long-term follow-up questionnaire.
Since many girls are likely to have zero-total sales, we use the inverse hyperbolic sine transformation of the outcome (see,Card and DellaVigna, 2013)

B4=Patience. Indicator variable =1 if the girl chooses to wait for 1 month before receiving her phone voucher (and thereby receiving 5000 TZS instead of receiving 2000 TZS today). This is question number 164 in the long-term survey.

Gender equality

G1 = Indicator variable=1 if the girl responds yes to the following question ( that comes from the DHS – Tanzania): “Do you agree that a husband is justified in hitting or beating his wife if she goes out without telling him?” (Yes/No) This is question 151 in the long-term follow-up questionnaire.

G2 = Response value to the following question: “It is acceptable to me that a wife earns more money than her husband” (question 152 in the long-term follow-up questionnaire). The response is on a scale of 1 to 5, where 1 is “strongly disagrees” and 5 is “strongly agrees”.

Empowerment

E1 = Response value to the following question, where we ask whether they agree that “I have little control about things that happen to me”. The response is on a scale of 1 to 5, where 1 is strongly disagrees and 5 is “strongly agrees”. This is question 149 in the long-term follow-up questionnaire.

E2 =Response value to the following question, where we ask whether they agree that “I certainly feel useless at times”. The response is on a scale of 1 to 5, where 1 is strongly disagrees and 5 is “strongly agrees”. This is question 150 in the long-term follow-up questionnaire.


Welfare

W1= Response value to the following question, where we ask whether they agree that “I am very happy with my life», using the scale « 1. strongly disagree, 2. disagree, 3. neither agree nor disagree, 4. Agree, 5. strongly agree». This is question 153 in the long-term follow-up questionnaire.

W2 = Response value to the following question, where we ask “How happy are you with your health?”,using the scale “1. not happy at all, 2. somewhat unhappy, 3. neither happy nor unhappy, 4. somewhat happy, 5. very happy”. W2 is equal to the response value. This is question 154 in the long-term follow-up questionnaire.

W3 = Response value to the following question, where we ask “How happy are you with your economic situation?», using the scale « “1. not happy at all, 2. somewhat unhappy, 3. neither happy nor unhappy, 4. somewhat happy, 5. very happy”». W2 is equal to the response value. This is question 155 in the long-term follow-up questionnaire.

W4 = Indicator variable=1 if the girl responds a strictly positive number on the following question: “During the last week, how many days were you too sick to work?”. This is question 157 in the long-term follow-up questionnaire.

W5 = This is the sum of all income and profit the girls gets in a normal week, which is
the sum of question 26, 37, 41, 45, 49, 53, 57, 61, 65, 69, 73, 77, 81, 85, 89, 93, 97, 101, 105, 109, 113, 117, 121, 125, 129, 133, 135, 137 in the long-term follow-up questionnaire.
Since many girls are likely to have zero-income, we use the inverse hyperbolic sine transformation of the outcome (see, Card and DellaVigna, 2013).

We sampled schools with at least 20 girls in Form IV in the following regions: Tabora, Singida, Morogoro and Dodoma. In each school, we did a baseline survey of the girls enrolled in Form IV and surveyed the headmaster of each school, who provided us with detailed information about school characteristics.

The schools were then randomly allocated to the control group or one of the three treatments. The randomization was blocked by school-size (below or above 40 girls in Form IV) and by region.

After the baseline survey, one or two teachers per (treated) school attended a one week instructor session organized by Femina HIP (two weeks for the teachers involved in the combined treatment). After this instructor sessions, the teachers implemented the training sessions (treatments) with all the Form IV girls of their school. The single treatments had 8 training sessions of 1.5 to 2 hours, 1 session per week. The cross-treatments had 16 training sessions of 1.5 to 2 hours, two sessions per week.

We randomized at the school-level, with blocking to ensure balance in two dimensions:
- school size (more or less than 40 girls in Form IV)
- region (Tabora, Singida, Morogoro and Dodoma)

We followed David McKenzie and Miriam Bruhn’s recommendations in dealing with the uneven numbers in some strata and in doing the randomization used the stata code they shared on the World Bank’s “Development Impact” blog on the 11th of June 2011.

We randomized at the school level. In each school, the unit of observation is all the girls enrolled in Form IV in the school-year of 2013.

Yes

As per our contract with the funding agency, The Research Council of Norway, we planned to survey 62 schools. During the baseline, we actually surveyed 80 schools.

As per our contract with the funding agency, we planned to survey an average of 50 students per school. Thus a total of 3 100 students. With the increase in the number of schools surveyed, we reached a total of 3 485 students at baseline.

We planned to have 17 schools in the control group and 15 schools in each treatment. Having reached 18 more schools than initially planned, we randomly allocated 20 schools in each arm.

The sample size was powered to detect changes in pregnancy rate, which is the most demanding variable to measure and therefore serves as a conservative estimate for the other variables of interest. We do not presently have pregnancy rates broken down by school (which is the relevant cluster unit), but data from the DHS 2010 combined with our qualitative insights from discussions with local partners indicate that pregnancy rates among past students from Form IV should be between 15% and 35% one year after the completion of Form IV. Taking into account the effect of clustering and the fact that we have three different treatment groups in addition to a control group, we have with the planned sample a power of 80% (with a 5% confidence interval) to detect a decrease in pregnancy rate from 25% to 20% (using the approach of Hayes and Moulton, 2009).