IMAGINE Baseline Analysis Report

November 27, 2019

Table of Contents Background ...... 6 Evaluation Design ...... 6 Study Setting ...... 7 Evaluation Study Design ...... 8 Intervention Program Design ...... 9 Methods ...... 11 Study population ...... 11 Sampling ...... 11 Sample size ...... 12 Weighting procedure ...... 15 Bangladesh ...... 15 Niger ...... 15 Baseline Analysis: Weighted Demographics, Priority Indicators, and Scales ...... 16 Characteristics of Primary and Secondary Samples ...... 21 Overall Age, Marriage, and Pregnancy ...... 21 Comparison between Primary and Secondary Demographics ...... 21 Bangladesh ...... 22 Niger ...... 23 Demographic Characteristics of Primary Sample Respondents ...... 24 Bangladesh ...... 24 Niger ...... 26 Priority Indicators ...... 28 Bangladesh ...... 29 Niger ...... 33 Priority Scales and Indices ...... 37 Bangladesh ...... 37 Niger ...... 39 Standardized Differences in Scale Means ...... 41 Correlations of Priority Indicators, Scales, and Indexes...... 44 Appendix A: Unweighted Estimates ...... 46 Bangladesh Unweighted Demographics ...... 46

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Niger Unweighted Demographics ...... 47 Bangladesh Unweighted Priority Indicators ...... 48 Niger Unweighted Priority Indicators ...... 50 Bangladesh Unweighted Priority Scales and Indices ...... 52 Niger Unweighted Priority Scales and Indices ...... 53 Appendix B: Primary and Secondary Sample Summary Tables ...... 54 Bangladesh Primary and Secondary Sample Counts ...... 54 Niger Primary and Secondary Sample Counts ...... 55 Appendix C: Sampling and Response Rates ...... 56 Bangladesh Primary Sampling and Response Rates by Village ...... 56 Bangladesh Secondary Sampling and Response Rates by Village ...... 58 Niger Primary Sampling and Response Rates by Village ...... 61 Niger Secondary Sampling and Response Rates by Village ...... 64 Appendix D: Preliminary Endline Analysis Plan ...... 67 Outcome Measures for Endline Analysis ...... 67 Analytic Steps and Methods ...... 68 References ...... 71

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List of Tables Evaluation Design ...... 6 Table 1. Summary of study design and methods used in Niger and Bangladesh...... 9 Table 2. IMAGINE Theory of Change ...... 9 Methods ...... 11 Table 3. Initial estimates of sample sizes, 2 countries, for small and large effect sizes ...... 13 Table 4. Bangladesh Sampling Weights ...... 15 Table 5. Niger Sampling Weights ...... 16 Characteristics of Primary and Secondary Samples ...... 21 Table 6. Weighted Age, Marriage, and Pregnancy by Country and Study Arm – Combined Primary & Secondary Samples Baseline Data ...... 21 Table 7. Bangladesh Comparison between Primary and Secondary Sample Weighted Demographics – by Treatment & Control Baseline Data ...... 22 Table 8. Niger Comparison between Primary and Secondary Sample Demographics – by Treatment & Control Baseline Data ...... 23 Demographic Characteristics of Primary Sample Respondents ...... 24 Table 9. Bangladesh Weighted Means and Percentages of Primary Sample Demographics – Combined Treatment & Control Baseline Data ...... 24 Table 10. Bangladesh Weighted Primary Sample Demographics – by Treatment & Control Baseline Data ...... 25 Table 11. Niger Weighted Means and Percentages of Primary Sample Demographics – Combined Treatment & Control Baseline Data ...... 26 Table 12. Niger Weighted Primary Sample Demographics – by Treatment & Control Baseline Data ... 27 Priority Indicators ...... 28 Table 15. Niger Weighted Means and Percentages of Primary Sample and Select Secondary Sample Priority Indicators – Combined Treatment & Control Baseline Data ...... 33 Table 16. Niger Weighted Means and Percentages of Primary and Secondary Priority Indicators – by Treatment & Control Baseline Data ...... 34 Priority Scales and Indices ...... 37 Table 17. Bangladesh Primary Sample Weighted Means and Cronbach’s Alphas of Priority Scales – Combined Treatment & Control Baseline Data ...... 37 Table 18. Bangladesh Primary Sample Weighted Means and Cronbach’s Alphas of Priority Scales and Indexes – by Treatment & Control Baseline Data ...... 38 Table 19. Niger Primary Sample Weighted Means and Cronbach’s Alphas of Priority Scales – Combined Treatment & Control Baseline Data ...... 40

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Table 20. Niger Primary Sample Weighted Means and Cronbach’s Alphas of Priority Scales and Indexes – by Treatment & Control Baseline Data ...... 41 Table 21. Standardized Differences in Weighted Means of Treatment and Control, by Country (Primary Sample Baseline Data) ...... 42 Correlations of Priority Indicators, Scales, and Indexes...... 44 Table 22. Bangladesh Pairwise Correlations of Priority Indicators and Scales - Baseline Data ...... 44 Table 23. Niger Pairwise Correlations of Priority Indicators and Scales - Baseline Data ...... 45 Appendix A: Unweighted Estimates ...... 46 Table 1a. Bangladesh Unweighted Means and Percentages of Demographics – Combined Treatment & Control Sample Baseline Data ...... 46 Table 2a. Bangladesh Unweighted Primary Sample Demographics – by Treatment & Control Baseline Data ...... 46 Table 3a. Niger Unweighted Means and Percentages of Demographics – Combined Treatment & Control Sample Baseline Data ...... 47 Table 4a. Niger Unweighted Primary Sample Demographics – by Treatment & Control Baseline Data 47 Table 5a. Bangladesh Unweighted Means and Percentages of Primary and Secondary Sample Priority Indicators – Combined Treatment & Control Sample Baseline Data ...... 48 Table 6a. Bangladesh Unweighted Means and Percentages of Primary and Secondary Priority Indicators – by Treatment & Control Baseline Data ...... 49 Table 7a. Niger Unweighted Means and Percentages of Primary, and Secondary Sample Priority Indicators – Combined Treatment & Control Sample Baseline Data ...... 50 Table 8a. Niger Unweighted Means and Percentages of Primary and Secondary Sample Priority Indicators – by Treatment & Control Baseline Data ...... 51 Table 9a. Bangladesh Unweighted Means and Cronbach’s Alphas of Priority Scales – Combined Treatment & Control Sample Baseline Data ...... 52 Table 10a. Bangladesh Unweighted Means and Cronbach’s Alphas of Priority Scales and Indexes – by Treatment & Control Baseline Data ...... 52 Table 11a. Niger Unweighted Means and Cronbach’s Alphas – Combined Treatment & Control Sample Baseline Data ...... 53 Table 12a. Niger Unweighted Means and Cronbach’s Alphas of Priority Scales and Indexes – by Treatment & Control Baseline Data ...... 53 Appendix B: Primary and Secondary Sample Summary Tables ...... 54 Table 1b. Actual vs Expected Sample Size by Union in Bangladesh - Primary Sample ...... 54 Table 2b. Actual vs Expected Sample Size by Union in Bangladesh – Secondary Sample ...... 54 Table 3b. Actual vs Expected Sample Size by Commune in Niger – Primary Sample ...... 55 Table 4b. Actual vs Expected Sampling by Commune in Niger – Secondary Sample ...... 55

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Appendix C: Sampling and Response Rates ...... 56 Table 1c. Bangladesh Sampling and Response Rates for Primary Sample...... 56 Table 2c. Bangladesh Sampling and Response Rates for Secondary Sample ...... 58 Table 3c. Niger Sampling and Response Rates for Primary Sample ...... 61 Table 4c. Niger Sampling and Response Rates for Secondary Sample ...... 64

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Background Approximately 90% of adolescent pregnancies in the developing world occur among married girls, putting at risk their highest future potential and in some cases their lives. Sexual and reproductive health and other development initiatives often fail to reach married girls. The international community typically focuses on preventing child marriage by targeting unmarried adolescents, or by serving adult married women. This approach tends to ignore married adolescents who thereby lack the services and support they need to lead healthy and productive lives.

To address this challenge, CARE has partnered with the Bill & Melinda Gates Foundation to implement IMAGINE, a project that examines how to support married adolescent girls and their families. The project aims at helping young women in Niger and Bangladesh to delay their first birth and to envision, value, and pursue alternative life trajectories. IMAGINE’s goal is two-fold: to identify, design, and test interventions that hold promise for delaying the timing of first birth among married adolescents and to document and share learning from this initiative with the wider development community to inform others working to address the issue of adolescent childbearing. IMAGINE is multifaceted, with components that enable married adolescent girls to delay first birth and to afford greater choice in pursuing an alternative life course. To this end, the intervention is designed to effect change in three domains:

• the health system and alternative future opportunity structures, • relations and community social norms and values, and • individual agency and control. The goal of this report is to analyze and report on the baseline demographics and priority program indicators with a special focus on the balance of treatment and control sampling prior to the start of the intervention.

Evaluation Design Key messages: • The primary aim of the intervention is to extend the time between marriage and first birth by six months or more. • The program targets adolescent girls age 15-19 in Bangladesh and Niger. Both countries have a treatment and control group. • Extensive identifying information was collected to track girls over the 3-year project period.

The central question for this evaluation is whether exposure to the intervention will extend the time between marriage and first birth by 6 months or more (compared to the control group). To test this hypothesis, the intervention will be assessed using a longitudinal before-and-after design where the same respondents are surveyed at baseline and endline. A sample comprised of both married and unmarried nulliparous 15- to 19-year-old adolescent girls was obtained from both treatment and comparison communities. Treatment areas were purposively selected; control areas were then selected and matched by subject matter experts to major regions in each country based on available geographic

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and demographic characteristics. Treatment and control samples were matched on baseline covariates (e.g. religion, ethnicity, literacy, etc.) to ensure comparability.

Study Setting

Bangladesh Kurigram Sadar Upazila is situated in Kurigram District, in the far northeast of Bangladesh bordering the Indian state of Assam. The population is predominantly Bengali (99%), 49% female, 97% Muslim, and 94% rural. Fifty percent of the population is under the age of 18. The average literacy rate is 26% (male 28%, female 24%). The main sources of income are agriculture (53%) followed by non-agricultural labor (10%). Food insecurity is high due to inadequate physical infrastructure, extreme poverty, and natural disasters. Heavy rainfall is usually observed during the rainy season and 98 percent of the land area is prone to flooding.

1 Two geographic subregions (“unions”), Belgachha and Punchgachhi0F , were purposively assigned to the treatment arm. Bhogdanga and Kanthalbari unions were assigned to the control arm based on geographic and demographic similarity to the treatment areas.

Figure 1 shows the geographic distribution of surveyed households in Kurigram Sadar. Households with respondents in the treatment arm are plotted in green against a light-yellow background; those assigned to the control arm are plotted in blue against a light blue background.

1 Several households in an exclave of neighboring Mogalbachha Union were assigned to the treatment arm and are included in the totals for Punchgachhi. These households were sampled from the villages Kadamtala, Dakshin Sitaijar, Uttar Sitaijar, Jakua Para, and Nama Char. Proprietary and Confidential Page 7 of 71

Niger Mirriah Department is situated in the Zinder Region in south-central Niger. The population is predominantly of the Hausa ethnicity (92%), 99% Muslim, 49% female and 98% rural. Fifty-seven percent of the population is below the age of 18. The average literacy rate is 15% (male 18%, female 12%). The main sources of income are agriculture (87%) followed by non-agricultural labor (4%).

Two geographic subregions (“communes”), Dogo and Kolleram, were purposively selected for the treatment arm. Gaffati, Gouna, Hamdara, and Zermou communes were assigned to the control arm based on geographic and demographic similarity to the treatment areas.

Figure 2 shows the geographic distribution of surveyed households in Mirriah Department. Households with respondents in the treatment arm are plotted in green within a light-yellow background; those assigned to the control arm are plotted in blue within a light blue background.

Evaluation Study Design The intervention will be assessed using a longitudinal before-and-after study design (baseline data collection plus a 3-year follow-up among the primary sample). The same respondents within the primary baseline sample will be surveyed at endline.

The primary research question is: does the intervention delay childbirth among married adolescents by at least six months as compared with the control group?

The relevant features of the survey design are:

• Stratified sampling of villages conducted to achieve equivalence on observed village-level factors related to the timing of childbirth across the treatment and control communities,

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• Multi-staged sampling design (i.e. villages -> subjects), • Sample weighting of individual respondents to achieve representativeness at the village level, • At endline, post-stratification, non-response, or propensity weighting can be conducted in order to achieve equivalence across treatment and control sample on subject-level factors (measured in the baseline questionnaire).

The longitudinal design of the study has several characteristics that make it ideal for evaluating the primary research question. First, the entire marriage to birth process can be observed in both treatment and control samples. This measurement is especially important given the possibility that the intervention might delay marriage timing among the treated unmarried, which might then counteract the impact on duration of marriage to first birth if not properly modeled. The design thus enables evaluation of change over and above any change in age at marriage. Second, individual participants can be matched on covariates measured at a common baseline timepoint to ensure comparability (such as religion, ethnicity, literacy, or distance from health facility). Third, the longitudinal study enables a richer modeling of individual change (in beliefs, norms, knowledge, etc.) rather than depending on the simple group mean comparisons inherent in a repeated cross-sectional study. Finally, for secondary outcome measures that are not modeled as time-to-event (such as contraception use), the longitudinal design also allows us to employ difference-in-difference estimators in our evaluation of the program’s impact. This helps us understand the how the difference in these measures between the treatment and control group changes from baseline to endline.

Table 1. Summary of study design and methods used in Niger and Bangladesh. Country Regions Study Design Study Population Sampling Strategy Bangladesh Treatment: Belgachha and Punchgachhi Primary: Adolescent girls Control: Two-stage design (aged 15-19) who have Bhogdanga and purposive selection of Longitudinal never been pregnant Kanthalbari regions. Random before-and-after selection among all study Secondary: Childbearing Niger Treatment: Dogo eligible adolescents adolescent girls (aged 15- and Kolleram from all villages. Control: Gaffati, 19) Gouna, Hamdara, and Zermou

Intervention Program Design To achieve program objectives, the designers of the program worked from an explicit Theory of Change based on prior intervention work, early field research and focus groups in similar areas of each country. Table 1 summarizes the program components and intervention strategies as related to the intermediate and primary outcomes.

Table 2. IMAGINE Theory of Change Problem Adolescents lack the skills, capacity and support, (from their families, communities and health system) they need Statement to be able to delay first birth and pursue alterative futures to early motherhood Because of….

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Analysis of Key Health Workers lack the skills and capacity to tailor services to adolescent’s specific needs, and often hold values, Dynamics beliefs and norms that act as barriers to the equitable provision of FP services to adolescents.

Adolescent girls have a limited awareness of the alternatives to early motherhood available to them, lack access to financial capital and control over resources, and are often neglected by existing vocational training opportunities and positive economic secular trends.

Newlyweds face significant pressure from their families and communities to have a child soon after marriage, with the husband as the primary decision maker in unions often characterized by a lack of gender equity.

Adolescent girls lack the capacity to envision and pursue alternative life trajectories other than early motherhood.

Adolescent girls lack the knowledge, skills, capacity, and links to formal health sector needed to make the healthy timing of pregnancy a reality. However, if we do… Interventions / Engage in reflective dialogue practice and counseling skills building activities with health workers; Strategies Offer transformative vocational opportunities in IT, mobile technology, and handicraft sectors;

Provide in-home couples counseling services to newlywed couples and mothers-in-laws (Bangladesh only);

Engage the wider public in visible, positive, events;

And deliver a comprehensive curriculum to girls’ collective solidarity groups. Then we expect that… Intermediate Health care workers will adopt supportive behaviors toward married adolescents who wish to delay first birth; Outcomes Improved engagement in alternative opportunities among married adolescents

Increased support to delay first birth among young men / husbands;

Increased support to delay first birth among mothers-in-laws;

Increased support to delay first birth among young men / husbands and mothers-in-laws;

Married adolescent girls will be able to envision and perceive value in alternatives to early first birth;

Married adolescent girls will have enhanced agency and assets relevant to delaying first birth and pursuing alternative futures;

Increased use of and satisfaction with sexual and reproductive health services among married adolescents.

And, as a result… Primary The timing of first birth will be delayed by 6 months or more above the average among married, 15-19 year olds Outcomes in intervention areas.

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Methods Key messages: • Eligibility for the study was assessed by baseline age (15-19) and by history of pregnancy. • The required sample sizes were estimated accounting for attrition, ineligibility, and design effects. Baseline sample sizes in Bangladesh were lower than anticipated, but the conservative sample size calculations should still enable power to detect differences at endline. • In this baseline report, unweighted and weighted means were calculated for demographic variables and priority indicators and scales. Also, differences in means tests were performed for each study arm within Bangladesh or Niger. • Weighted correlations among priority indicators and scales were computed for the full primary sample in both Bangladesh and Niger.

Study population The primary target population in both countries for this study consists of adolescent girls (aged 15-19) who have never been pregnant and who reside in the villages purposively selected to receive treatment. In both countries, data from a control population were collected using identical eligibility criteria of the target population, but who reside in a different, comparable region of the country. The data from the control population will be used exclusively for the construction of a counterfactual (i.e. estimates of the study outcomes that would have been observed in the target population had that population never been exposed to the treatment condition).

Three questions were used to assess eligibility:

1. How old were you at your last birthday? IF AGE IS BETWEEN 15-19, CONTINUE WITH SURVEY 2. Have you ever been pregnant? IF ‘NO’ OR ‘UNSURE’, CONTINUE WITH SURVEY 3. Are you currently pregnant? IF ‘NO’ OR ‘UNSURE’, CONTINUE WITH SURVEY If age at last birthday (question 1) was unknown, interviewers probed the respondent by asking what year and season that the respondent was born. If at least 15 years since the earliest point in that season- year and no more than 20 years since the latest point in that season-year, the participant was considered eligible for participation. Since data collection took place in the autumn of 2018, girls born as early as the autumn of 1998 or as late as the autumn of 2003 were eligible.

The secondary sample includes girls who were also 15-19 years old, but who indicated a prior pregnancy or birth or were currently pregnant.

Sampling Sampling began with a selection of geographic regions in Niger and Bangladesh. These regions were then allocated into one of two study arms – intervention and comparison. The primary target population of the IMAGINE intervention was nulliparous girls aged 15 to 19 years old. Within each of the study regions, every village with an estimated eligible target population of at least 19 girls were included as the Primary Sampling Unit (PSU), henceforth referred to as the primary population. Villages with fewer

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than 19 girls were excluded for programmatic reasons (e.g., group activities required a threshold of participants).

As previously mentioned, the study also included a smaller sample of girls that had begun childbearing (i.e. either currently pregnant or having had at least one live birth) within the same age range, henceforth referred to as the secondary (childbearing) population.

Every village was assigned a target primary and secondary sample size based on an estimated eligible population from publicly available records. In order to randomly select sample-eligible participants from within these villages, the survey team conducted “on-the-ground”, village-level enumeration to identify all households containing at least one girl aged 15 to 19 years old. During this enumeration phase, the survey team generated a sampling frame of all study-eligible girls found within all households. The sampling frame contained the following information for each household:

• The GPS coordinates, • The name of the head of the household, and • A listing of all adolescent girls in the household ages 15-19 years old (identified by first name) that were eligible for the primary or the secondary (childbearing) samples, disaggregated by eligibility status (primary vs. secondary sample). At the end of the enumeration phase, the survey team generated two independent sampling frames within every village (one for the primary population and one for the secondary population). These independent frames were randomly ordered using a random number generator. The survey team then returned to households containing eligible subjects according to the randomized frame order until achieving the target sample size for each village. If a village was found to have fewer than the target eligible size, teams attempted to collect data on every eligible girl.

Sample size The baseline data for the primary and secondary surveys were collected from the field during the months of November and December 2018. Starting sample sizes were computed under the assumption of a simple random sample (SRS). Samples were then inflated to account for women likely to be ineligible at endline because of a) married women unaware that they were pregnant at baseline (i.e., within their first two trimesters) or b) unmarried women being unmarried at endline. After estimating the number needed under the assumption of an estimated sample (SRS) with adjustments for ineligibility determined at baseline, the sample size was then inflated by another 25% to conservatively accommodate for an estimated 20% attrition that could result from outmigration or for enumerators who were unable to locate baseline participants at follow-up. Finally, since the sample is not actually SRS, the sample size was multiplied by two (2.0) to adjust for design characteristics such as clusters (i.e., villages) and design weights. These design factors tend to increase variance estimates in statistical models, requiring larger sample sizes than SRS.

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Table 3. Initial estimates of sample sizes, 2 countries, for small and large effect sizes Country Effect Size: Delay Estimated Sample Size with Final Eligible ‘Skip Out’ Quick of first birth after Sample Adjustment for Sample Size with Survey: marriage (SRS) Attrition (1.25) Adjustment for Ineligible Women Design (2.00) (Estimate) Bangladesh 6-months 1300 1625 3250 518 Niger 6-months 1100 1375 2750 585

In Bangladesh, initial target sample sizes were 3,250 and 518 adolescent girls for the primary and secondary samples respectively. During enumeration the data collectors learned that three villages from the original frame were lost due to flooding, resulting in lower-than-expected sampling. In addition, enumeration in many villages was less than the published census estimates. Since target sample sizes were specified for each village, the sampling rates naturally increased to make up this loss, yet due to smaller overall enumeration and unexpected loss of 3 villages due to flooding, the target sample was approximately 19% less than expected. In the end, field teams attempted to visit 3,455 girls from the frame across 90 villages in order to reach the remaining village target sample sizes; 3,149 ended up

completing the survey (primary n=2,629; secondary n=5201F).

In Niger, initial target sample sizes were 2,750 and 586 adolescent girls for the primary and secondary samples respectively. Due to challenging field conditions, simultaneous enumeration and sampling (unlike Bangladesh), as well as higher refusal rates, the sample was about 10% less than the target. In the end, the teams attempted to visit 3,888 cases from the frame across 86 villages to obtain the sample size; 3,041 completed the survey (primary n=2,480; secondary n=561). For more information on sample counts and target sample sizes, please see Appendix B.

Figure 3 represents the Bangladesh enumerated population, condition, sample, and respondents; Figure 4 presents the analogous information for Niger.

In Bangladesh, 31 adolescent girls who completed primary sample surveys, were deemed ineligible during the data cleaning process due to reporting a previous pregnancy. In Niger, there were 5 girls who were misclassified as primary due to a previous pregnancy. “True” primary and secondary counts are included in Figures 3 and 4. These counts refer to the frame, sample, and respondent counts using the correct classifications for adolescent girls who either have never had a previous pregnancy (primary) or who have begun childbearing (secondary). The “true” counts were used in weighting procedures, which are discussed with greater detail below.

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Figure 3. Population, Sample, and Response Counts for Bangladesh

Bangladesh Enumerated population 4446

Primary Secondary 3040 1406 (true primary 3009) (true secondary 1437)

Unsampled Sampled Sampled Unsampled 117 2923 532 874 (true primary 2892) (true secondary 563)

Treatment Control Treatment Control 1534 1389 276 256 (true primary 1520) (true primary 1372) (true secondary 290) (true secondary 273)

Completed surveys Completed Completed surveys Completed surveys 1384 1276 253 236 (true primary 1370) (true primary 1259) (true secondary 267) (true secondary 253)

Figure 4. Population, Sample, and Response Counts for Niger

Enumerated population 6662 (6653 true population, 3 admin dups, 6 true dups)

Primary Secondary 4210 2452 (true primary 4205) (non-duplicates 2443 -> true secondary 2448)

Unsampled Sampled Sampled Unsampled 1047 3169 719 1728 (true primary 3164) (true secondary 724)

Treatment Control Treatment Control 1716 1458 371 348 (true primary 1711) (true secondary 376)

Completed surveys Completed Completed surveys Completed surveys 1269 1216 283 278 (true primary 1264) (true secondary 288)

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Weighting procedure All villages with at least 19 expected eligible girls were included in the frame enumeration, and within these villages, all households were visited in order to enumerate the population.

This enumerated population of girls aged 15-19 was split into two independent sampling frames (primary and secondary). The primary study sample was randomly drawn from the frame of married or unmarried girls aged 15-19 with no history of prior pregnancy as enumerated in each village. The secondary sample was drawn from the subpopulation of girls aged 15-19 who had indicated a prior or current pregnancy. The secondary sample will not be followed longitudinally; this sample was smaller in size and collected in order to report baseline rates and timing of births and marriages across the 15-19- year-old female population in the treatment and control areas.

Sampling weights were calculated using the village-level inverse probability of response (Seaman & White, 2011; Mansournia & Altman, 2016). The probability of response was calculated by dividing the number of primary or secondary girls who completed a survey from each village by the enumerated population of primary or secondary girls for the respective village in the frame. The sample weight assigned to each case was the inverse of the response probability. All weights sum to the total frame count for the full sampled population (Table 4). The weights adjust the sampled cases to account for the adolescent girls who were not sampled. Weights were not adjusted for differential non-response in the baseline report due to limited availability of full-frame demographics. However, it is important to note that response rates were relatively high at 89.94% in Bangladesh and 78.36% in Niger. Therefore, weighted results are expected to be in line with the actual population rates in these study areas.

Bangladesh Due to the high sampling proportions (Figure 3), the average sampling weight among the primary sample was 1.14 with a range of 1.00 to 1.76. A weight of 1.00 means that the identified village population was sampled at 100% and girls with a weight of 1.00 represent one girl in the population. In villages where girls were sampled at less than 100%, weights are greater than 1.00 to adjust sampled estimates up to the population level. During data cleaning and sample checks, it was discovered that 31 respondents in the primary sample were ineligible due to reporting a previous pregnancy. These 31 girls were reclassified as secondary on the frame and sample to ensure correct calculation of weights.

Table 4. Bangladesh Sampling Weights Weight Respondents Frame New Frame Sum Mean Min Max Sample weight 3149 4446 4446 4446.00 1.41 1.00 6.86 Primary weight 2629 3040 3009 3009.00 1.14 1.00 1.76 Secondary weight 520 1406 1437 1437.00 2.76 1.00 6.86

Niger The enumerated population was larger than anticipated. Reaching target sample sizes required lower sampling rates than in Bangladesh. These lower sampling proportions resulted in somewhat larger sampling weights. The average sampling weight among the primary sample was 1.70 with a range of 1.00 to 4.47.

Initial data cleaning and checks revealed 5 respondents who were sampled in the primary group who were ineligible due to a previous pregnancy. These respondents were reclassified as the secondary

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sample and reclassified on the frame. Reclassification the respondents across frame counts ensures that respondent weights are representative of the true population of nulliparous and parous girls.

During the weight calculations, it was also discovered that there were 62 pairs of duplicate IDs, 124 total, on the frame in Niger. Fifty-three of these duplicate pairs were not duplicated girls but were recycled respondent IDs of girls from the same household, yet one girl appearing in the primary frame and the other in the secondary. These duplicates were assigned new IDs to enable proper linkage to sample data.

Three of these duplicate pairs, six records in total, were determined to be administrative duplicates and were thus removed from the secondary frame to enable correct weighting; none completed the survey. There were six pairs of true duplicates. These six duplicate girls were only sampled on the primary frame and were mistakenly also listed on the secondary frame. Their counts on the secondary frame were removed to enable correct weighting to the true population.

The original frame in Niger included 6662 observations. After accounting for the three administrative duplicates and the six true duplicates the weights should be representative of and equal to a frame of 6653. However, in the included village of Kournawa Bougage there was an enumerated population of 11 adolescent girls in the secondary sample, none of which were sampled, slightly reducing the weighted frame total.

Table 5. Niger Sampling Weights Weight Respondents Frame New Frame Sum Mean Min Max Sample weight 3046 6662 6653 6642.00 2.18 1.00 14.20 Primary weight 2480 4210 4205 4205.00 1.70 1.00 4.47 Secondary weight 566 2452 2448 2437.00 4.31 1.00 14.20

Baseline Analysis: Weighted Demographics, Priority Indicators, and Scales The purpose of the baseline analysis is to report on demographic balance for the baseline sample, as well as to report on a set of priority indicators specified by CARE and the sponsor. In addition to the broad set of demographic indicators in the survey, priority indicators are detailed below to be included in this baseline report. Weighted indicators for each country are reported in separate tables by treatment arm and overall. Unweighted estimates are included in Appendix A.

Demographics, Priority Indicators, and Scales Means and percentages were calculated for demographic characteristics, priority indicators, and scales that were outlined by CARE. Demographic variables • Mean age • Percentage of girls married • Mean age at marriage • Percentage of girls receiving an education and at what level • Frequency of ethnicity • Frequency of religious affiliation (Bangladesh only).

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Priority indicators • Mean satisfaction with sexual and reproductive health right (SRHR) services score • Contraceptive prevalence • Percentage of unmet need for FP • Percentage of girls receiving healthcare from either a fieldworker or from a health facility in the past 6mos • Rate of discussing family planning with health worker or promoter in the past 6 months • Percentage of adolescent girls with knowledge of modern contraceptives or services • Percentage who report being engaged in an income generating activity, VSLA, or training activity, by type • Mean reported monthly income • Percentage of girls who report having personal savings (liquid) • Percentage of girls who report having capital assets (non-liquid) • Secondary Samples only: average duration from marriage to first birth Priority scales and indices • Early pregnancy risk knowledge (Family planning myths and misconceptions) • SRHR knowledge • Ownership of household assets / resources • Self-efficacy to go to health facility • Self-efficacy to engage in economic activities • Rosenburg Self-Esteem Scale • Social Cohesion • Collective Efficacy • Mobility • Self-efficacy to discuss and use family planning • Self-efficacy to refuse sex • Participation in household general decision-making • Participation in household financial decision-making

Baseline-Endline Analysis Considerations One limitation of the endline evaluation arises from the definition of our eligible population. Adolescents who have already had a child at baseline are purposely excluded from our primary sample for programmatic reasons. Given that exclusion, baseline differences in the timing of first birth among all girls residing in treatment and control areas cannot by directly adjusted for during endline analysis. To alleviate this concern, baseline secondary (childbearing) samples were collected in order to report estimates of the full-population demographics and rates by treatment and control. Sample-weighted data from both the primary and secondary sample can be combined to enable close inspection of the baseline differences.

Analysis and variable construction The primary purpose of the baseline analysis was to report on the demographic characteristics and important variables that may change during intervention roll out. Means and percentages were reported for demographic variables and priority indicators. Difference of means tests between the

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treatment and control group were conducted for these variables. Due to the complex sampling and desire to generalize results to the frame populations, all difference of means tests were adjusted for using sampling weights and clustering at the village level. Statistical testing of differences in weighted estimates (between the treatment and control groups) employed a two-proportion chi-squared test for differences in percentages and a two-sample t-test for differences in means of continuous factors (e.g., age) at p<.05. Correlation matrices were also computed for the all priority indicators, scales, and indices.

Most variables were recorded in survey responses, but some variables of interest required additional construction. Surveys were similar in Bangladesh and Niger and variables were coded similarly across both sampled. Percentage of unmet family planning need was calculated for girls who reported being married and wanted to avoid being pregnant in the next twelve months or reported a desire to never have children but were not using any contraceptives to avoid a pregnancy. If a respondent met these conditions, she was coded as “1” and all others were coded as “0” to obtain the proportion/percentage of girls reporting unmet family planning needs.

Girls who reported receiving care from either a field worker or at a health facility were coded as “1” for receiving healthcare in the past six months. These same girls were asked whether they discussed family planning with the field worker or health facility provider. If a respondent reported discussing family planning during her visit in the past 6 months with one of these health service providers she was coded “1” and all others coded “0”.

All married girls were asked whether they were currently using a contraceptive to avoid pregnancy. If a girl indicated that she was using contraception, she was asked which kind she most recently used. Most girls reported only using one contraceptive, but there were 13 girls in Bangladesh who reported relying on condoms and the pill. These girls were coded as only using the most effective form (the pill). In Niger, there were two girls who reported using injectables, pills, and implants. These girls were coded as only using the implant.

At endline follow-up, all sampled girls will be asked their exact dates of birth, marriage, and first child’s birth. At baseline however, analyses are primarily based upon reported age at time of life events and first child’s birth date. To identify the time between marriage and first birth, girls in the secondary sample were asked what age they were at time of their child’s first birth and what age they were when they were married. In Bangladesh, it was discovered that the variable “age of marriage” among married was always the same as the respondents current age. For this reason, average duration between marriage and first birth could not be directly calculated. However, using the first child’s date of birth and the date of the survey, the child’s exact age in years was calculated. The respondent’s age at the time of interview was subtracted by the child’s age to get an approximate maternal age at first birth. As an example of how the calculation of maternal age at first birth is inexact, if at the time of interview a respondent was 17 and her child’s age was approximately 1.25 years, her calculated age of birth would be 15.75. However, if the respondent turned 18 in a couple weeks after the time of interview her actual age at first birth would be closer to 16.75. The approximation error should be randomly distributed about the true mean, enabling baseline inspection of treatment and control.

In Niger, girls in the secondary sampled reported the age they were when they were married in addition to how old they were at the time of their first child’s birth. Age at first birth was subtracted by the age at marriage to achieve the approximate duration between marriage and first birth. This variable is limited

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in the same respects described for Bangladesh because it does not indicate the exact timing between when girls were married and when they have their first child.

All scales in both Bangladesh and Niger were constructed using survey questions specified by CARE. Early pregnancy risk knowledge is a count of true or false questions girls answered correctly regarding early pregnancy risk. Girls were asked questions related to when a woman can or cannot get pregnant. For example, these included questions about whether a girl can get pregnant during the very first time she has sexual intercourse or whether a woman can conceive a child after she has recently given birth but before her period has resumed. Correct answers were coded as 1 and incorrect answers as 0 and responses were summed to create a total score with a minimum of 0 and maximum of 4.

The family planning myths scale was constructed based on a girl’s agreement with a set of family planning myths. The family planning scale include items like “contraceptives cause disabilities/birth defects” and “using contraceptives at any point causes infertility.” Responses were coded as 5 or “strong agreement with myth” to 1 or “strong disagreement with myth.” After coding, response values were summed together and then divided by the number of questions to calculate an average belief in myths.

Self-efficacy to go to a health facility was also moderate. Health facility self-efficacy was derived from a series of questions which asked girls to indicate how confident they were that they could get to a health facility despite obstacles (e.g., if their husband or another family member objected). Answers were coded as 5 or “completely sure” to 1 or “not at all sure.” Questions were summed together and divided by the number of questions to create an average score for the scale. Higher scale scores indicate higher self-efficacy to go to a health facility.

Self-efficacy to engage in economic activities was constructed using a series of questions which asked girls how sure they were that they could engage in a set of specific economic activities (e.g., ability to negotiate prices for selling or purchasing in a local market or ability to create a budget). “Completely sure” was coded as 5 and “not at all sure” was coded as 1. Higher scale scores indicate higher self- efficacy to engage in economic activities.

The Rosenburg Self-Esteem scale was constructed from 10 questions with response values ranging from 1 to 5. All questions were coded such that a 5 is a high self-esteem score and a 1 is a low self-esteem score. For instance, for the question “I certainly feel useless at times” a 5 would correspond to “strongly disagree”, but for the question “I take a positive attitude toward myself” 5 would correspond to strongly agree. All questions were summed to create a total score range of 10 to 50.

The social cohesion scale assesses the level of reliance a girl feels she has on others in the community around her. Question examples include “I can rely on people in my community to borrow money” and “I can rely on people in my community if I need to talk about my problems.” All responses were coded such that a high score indicates more social cohesion. The response values ranged from 1 “strongly disagree” to 5 “strongly agree.”

Collective efficacy captures the extent to which a girl feels she can work with other girls in her community to prevent a variety of social problems. These problems included preventing each other (other girls) from being beaten or injured by family members or improving how adolescents were

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treated at a health facility. Response choices range from “completely sure” to “not at all sure.” A higher score indicates more certainty that the respondent can work together with other girls in her community.

The mobility scale assessed the level of independent mobility a girl has in and beyond her immediate community. Girls indicated whether they were permitted to go on their own, permitted if they were accompanied, or not permitted at all. Some of these places include the market, to fetch water, or go to training courses. All questions were summed then divided by the number of questions to calculate an average mobility.

Ownership of household assets is an index representing the contribution by the respondent to her household. Girls were asked whether they owned any assets, whether they worked for pay or goods, or if they had any savings. “Yes” responses were coded as 1 and “no” responses were coded as 0. Answers were summed to create a total score ranging from 0 to 3.

Questions regarding respondents’ self-efficacy to discuss and use family planning were only asked of married girls. Each girl was asked how sure she was regarding a series of questions related to discussing the topic of family planning with her husband or using family planning even if her husband or other family members did not desire it. All items were summed then divided by the number of questions to create an average. The response scale ranges from 5 or “completely sure” to 1 or “not at all sure.”

Married girls were asked a series of questions related to when she feels she can or cannot refuse sex from her husband. The items asked questions like “how sure are you that you could refuse sex from your husband when you don’t want to have sex but he does?” or “how sure are you that you could refuse to have sex with you husband if he threatens to have sex with other women if you don’t have sex with him?” Response scale values ranged from 5 ‘’completely sure” to 1 “not at all sure.” Questions were summed then divided by the number of questions to create an average certainty to refuse sex.

Household decision making is a scale that captures what portion of household decisions a woman makes either herself or jointly with her husband. These decisions included timing for when she will visit a friend or family member as well as decisions about her own healthcare. Responses to these questions measured whether she, her husband, both her and her husband, her in-laws, or someone else makes these decisions. If the respondent indicated that she or both she and her husband made the decision, then she was given a score of 2 all other choices were coded as 1. These questions were then summed together and divided by the number of questions to achieve an average decision-making score.

The household financial decision-making scale was calculated in the same manner as the household decision making scale. Married girls indicated whether they were responsible for making financial decisions or if they made these decisions jointly with her husband. The decisions that were asked about included choice over household purchases or how to use money that is brought into the household. If the girl or the girl and her husband made the decision, she was given a score of 2. All other answers were coded as 1. These questions were then summed together and divided by the number of questions to achieve an average score.

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Characteristics of Primary and Secondary Samples Key messages: • Overall rates of pregnancy were higher in the treatment group in Niger. • Overall age was close to early predictions used for power calculations. • In both Bangladesh and Niger, girls in the secondary sample report less education. • In the Bangladesh treatment areas, more girls in the primary sample are Hindu and report more control over assets.

Overall Age, Marriage, and Pregnancy Table 6 includes the weighted average age, percent who are married, and percent who have ever been pregnant for the combined primary and secondary samples by treatment and control in Bangladesh and Niger. These combined sample estimates provide the population statistics among all girls aged 15-19 in the selected villages in both countries.

Table 6. Weighted Age, Marriage, and Pregnancy by Country and Study Arm – Combined Primary & Secondary Samples Baseline Data Bangladesh n=3149 Niger n=3046 Treatment n=1637 Control n=1512 Treatment n=1552 Control n=1494 Variables 푥̅ 표푟 % 95% CI 푥̅ 표푟 % 95% CI 푥̅ 표푟 % 95% CI 푥̅ 표푟 % 95% CI Age 16.72 16.64 16.79 16.64 16.67 16.81 16.44 16.34 16.54 16.47 16.37 16.56 Married 49.08% 42.99% 55.18% 52.82% 48.66% 56.99% 53.58% 47.77% 59.39% 48.60% 40.82% 56.38% Pregnancy 31.15% 26.66% 35.65% 33.57% 29.89% 37.24% 39.88%* 35.93% 43.84% 32.70% 28.53% 36.86% *significantly different from control at p<.05; 푥̅ = mean; CI = confidence interval

There were few population differences between the full sample treatment and control groups within either Bangladesh or Niger. In Niger only, the prevalence of pregnancy was significantly higher in the treatment group in Niger (39.88%), than it was in the control group (32.70%). No other demographic estimates suggested differences between treatment and control in either country.

In Bangladesh roughly 1/3rd of girls have reported any previous pregnancy with 31.15% in the treatment group and 33.57% in the control group. Average age was close to the predicted estimates of 16.33 for both countries. In Bangladesh the average age was 16.72 and 16.64 in the treatment and control groups, respectively. In Niger, the average age in the treatment group was 16.44 and in the control group it was 16.47. Roughly half of girls reported being married in both Bangladesh and Niger.

Comparison between Primary and Secondary Demographics The following tables compare the primary and secondary samples in Bangladesh and Niger. Comparisons among the primary and secondary sample are intended to provide context for the differences that exist within the broader population between parous and nulliparous girls.

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Bangladesh In Table 7, weighted demographic differences between the secondary and primary samples by treatment and control group within Bangladesh are reported.

Table 7. Bangladesh Comparison between Primary and Secondary Sample Weighted Demographics – by Treatment & Control Baseline Data Treatment n=1637 Control n=1512 Primary n=1370 Secondary n=267 Primary n=1259 Secondary n=253 Variables 푥̅ or % 95% CI 푥̅ 표푟 % 95% CI 푥̅ or % 95% CI 푥̅ or % 95% CI Age 16.37* 16.28 16.46 17.49 17.31 17.68 16.40 16.32 16.48 17.42 17.27 17.57 Married 26.53%* 21.66% 31.39% 98.93% 97.67% 100.00% 29.07% 25.85% 32.30% 99.83% 99.49% 100.00% Attended school 99.86% 99.68% 100.00% 97.03% 93.86% 100.00% 99.61% 99.29% 99.93% 99.20% 97.68% 100.00% Primary 6.91%* 4.21% 9.62% 24.78% 14.01% 35.56% 6.84%* 4.55% 9.13% 19.95% 14.97% 24.94% Secondary 66.78% 62.42% 71.14% 69.09% 58.87% 79.32% 68.13% 64.64% 71.63% 71.75% 66.04% 77.47% Higher 26.31%* 26.31% 26.31% 6.12% 2.34% 9.91% 25.03%* 21.38% 28.68% 8.29% 4.20% 12.39% Income activity (week) 6.88% 4.58% 9.18% 6.91% 3.78% 10.04% 5.61% 3.83% 7.40% 8.67% 4.48% 12.87% Control of Assets 94.25%* 90.91% 97.60% 88.95% 82.50% 95.40% 98.54% 97.79% 99.29% 97.34% 95.08% 99.61% Bengali 100.00% 100.00% 100.00% 100.00% Religious affiliation Muslim 90.29%* 90.29% 90.29% 95.14% 91.96% 98.31% 93.09% 89.83% 96.36% 95.45% 92.41% 98.49% Hindu 9.71% 4.84% 14.59% 4.15% 1.12% 7.18% 6.91% 3.64% 10.17% 4.55% 1.52% 7.59% *significantly different from secondary at p<.05; 푥̅ = mean; CI = confidence interval

There were a few differences between the primary and secondary samples in Bangladesh. Girls in the secondary sample were older than girls in the primary sample, as would be expected for those having had a current or prior pregnancy (a selection criterion for the secondary sample). Pregnancy rarely occurs outside of the context of marriage (98.93% of secondary sample girls were married in the treatment; 98.83% were married in the control). Marriage, conception, and birth take time, which leads to girls in the secondary sample being older.

Most girls attend school in Bangladesh, however, girls in the primary samples had a greater prevalence of completing education beyond the secondary level. Girls in the secondary samples had greater prevalence of only completing education through the primary and secondary levels. In the treatment group only, girls in the primary sample were more often Hindu and reported more control of and ownership of household assets. The prevalence of employment in the past week was similar across primary and secondary samples. Finally, all respondents reported being Bengali.

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Niger Table 8 provides the demographic comparisons between the primary and secondary samples within treatment and control areas for Niger.

Table 8. Niger Comparison between Primary and Secondary Sample Demographics – by Treatment & Control Baseline Data Treatment n=1552 Control n=1494 Primary n=1264 Secondary n=288 Primary n=1216 Secondary n=278 푥̅ or % 95% CI 푥̅ or % 95% CI 푥̅ or % 95% CI 푥̅ or % 95% CI Variables Age 15.71* 15.58 15.84 17.54 17.36 17.72 15.80* 15.70 15.89 17.85 17.65 18.04 Married 25.12%* 19.51% 30.74% 96.47% 94.09% 98.85% 25.22%* 16.74% 33.70% 96.78% 94.57% 98.99% Attended school 59.32%* 49.24% 69.40% 30.69% 21.78% 39.60% 59.47%* 49.40% 69.54% 35.91% 28.27% 43.56% Primary 38.41%* 25.51% 51.32% 54.66% 42.80% 66.52% 39.55% 29.36% 49.73% 38.61% 27.16% 50.06% Secondary 61.17%* 48.37% 73.97% 45.34% 33.48% 57.20% 60.01% 50.14% 69.89% 61.39% 49.94% 72.84% Higher 0.41% 0.02% 0.81% 0.00% 0.44% 0.00% 0.96% 0.00% Income activity (week) 28.10% 19.57% 36.62% 24.68% 18.26% 31.10% 35.01% 24.16% 45.86% 29.26% 21.16% 37.37% Control of Assets 21.32% 14.96% 27.67% 27.00% 19.11% 34.88% 14.58%* 8.94% 20.21% 20.67% 12.77% 28.58% Ethnicity Hausa 87.41% 80.06% 94.77% 86.09% 76.63% 95.54% 95.66% 93.37% 97.96% 93.22% 88.90% 97.54% Djerma 0.24% 0.00% 0.57% 0.00% 0.22% 0.00% 0.61% 0.00% Taureg 5.25% 0.88% 9.62% 7.16% 0.08% 14.25% 1.47%* 0.00% 3.33% 4.12% 1.27% 6.98% Fulani 6.57% 0.81% 12.34% 6.15% 0.00% 12.71% 1.75% 0.45% 3.05% 2.66% 0.30% 5.01% Kanuri 0.52% 0.17% 0.87% 0.60% 0.00% 1.83% 0.81% 0.25% 1.36% 0.00% Toubou 0.00% 0.00% 0.09% 0.00% 0.26% 0.00% Arab 0.00% 0.00% 0.00% 0.00% Gurma 0.00% 0.00% 0.00% 0.00% *significantly different from secondary at p<.05; 푥̅ = mean; CI = confidence interval

There were similar differences found between the primary and secondary samples in Niger. As in Bangladesh, the secondary sample girls were older than the nulliparous girls in the primary sample. Only about 25% of primary sample girls were married compared to roughly 96% of the secondary sample in both treatment and control groups.

In treatment and control areas, more girls in the primary sample reported attending school than girls in the secondary sample. For both the treatment and control groups, close to 60% of girls in the primary sample report attending school. In comparison, about 31% in the secondary sample treatment group and close to 36% in the secondary sample control group report attending school. In the treatment group, there were also differences in the level of education completed between the primary and secondary sample girls. Among girls who did attend school in the secondary sample treatment group, there was a higher prevalence of only completing their primary education (54.66%) compared to the primary sample where many completed their secondary education (61.17%).

In the control areas, it was less common for nulliparous girls to report control over household assets (14.58%) compared to the secondary sample (20.67%). In the control group, a higher proportion of girls in the secondary sample reported being Taureg than in the primary sample.

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Demographic Characteristics of Primary Sample Respondents Key messages: • In Bangladesh, there were no significant differences across demographic characteristics. • In Niger, there were variations in ethnicity between treatment and control groups, but there were no significant differences across other demographic characteristics identified.

The next major sections focus on the demographic differences between treatment and control areas among the primary samples in Bangladesh and Niger. The comparisons among the primary samples are intended to assess overall balance between the treatment and control groups and identify any differences among variables identified by CARE that may support extending the time of first birth after marriage among adolescent girls.

Bangladesh Table 9 includes the weighted means of sociodemographic factors of interest for the Bangladesh primary sample across both treatment and control areas.

Table 9. Bangladesh Weighted Means and Percentages of Primary Sample Demographics – Combined Treatment & Control Baseline Data Primary sample Variables N 푥̅ or % 95% CI Age 2629 16.38 16.33 16.43 Married 2629 27.74% 24.81% 30.66% Age at marriage 733 15.95 15.84 16.05 Attended school 2629 99.74% 99.56% 99.93% Primary 2622 6.88% 5.13% 8.62% Secondary 2622 67.42% 64.67% 70.17% Higher 2622 25.70% 22.22% 29.19% Bengali 2629 100.00% Religious affiliation Muslim 2629 91.62% 88.72% 94.52% Hindu 2629 8.38% 5.48% 11.28% 푥̅ = mean; CI = confidence interval

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There were no statistical differences among demographic characteristics in Bangladesh (Table 10).

Table 10. Bangladesh Weighted Primary Sample Demographics – by Treatment & Control Baseline Data Treatment Control Variables N 푥̅ or % 95% CI N 푥̅ or % 95% CI Age 1370 16.37 16.30 16.44 1259 16.40 16.33 16.47 Married 1370 26.53% 21.81% 31.24% 1259 29.07% 25.89% 32.26% Age at marriage 368 15.99 15.84 16.14 365 15.91 15.76 16.05 Attended school 1370 99.86% 99.68% 100.00% 1259 99.61% 99.30% 99.92% Primary 1370 6.91% 4.29% 9.53% 1259 6.84% 4.58% 9.10% Secondary 1370 66.78% 62.56% 71.00% 1259 68.13% 64.68% 71.58% Higher 1370 26.31% 20.53% 32.09% 1259 25.03% 21.42% 28.64% Bengali 1370 100% 1259 100% Religious affiliation Muslim 1370 90.29% 85.57% 95.01% 1259 93.09% 89.87% 96.32% Hindu 1370 9.71% 4.99% 14.43% 1259 6.91% 3.68% 10.13% *significantly different from control at p<.05; 푥̅ = mean; CI = confidence interval

Bangladesh demographics key findings There were no statistical differences among demographic characteristics in Bangladesh. The average age of adolescent girls in the Bangladesh study was 16.38. Approximately 27.74% of adolescent girls in the primary sample were married. Most adolescent girls attend school in Bangladesh. Overall, 99.74% of girls have attended school with the vast majority completing secondary (67.42%) or higher (25.70%) levels of education. The entire population of girls in Bangladesh were Bengali. Overall, 91.62% of girls were Muslim and 8.38% were Hindu.

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Niger Table 11 includes the weighted means of sociodemographic factors of interest for the Niger primary sample across both treatment and control areas.

Table 11. Niger Weighted Means and Percentages of Primary Sample Demographics – Combined Treatment & Control Baseline Data Primary sample Variables N 푥̅/% 95% CI Age 2480 15.76 15.71 15.79 Married 2480 25.15% 23.35% 26.96% Age at marriage 652 14.88 14.78 14.99 Attended school 2480 59.39% 57.32% 61.46% Primary 1459 38.94% 36.28% 41.61% Secondary 1459 60.63% 57.96% 63.30% Higher 1459 0.43% 0.08% 0.77% Ethnicity Hausa 2480 91.26% 90.09% 92.43% Djerma 2480 0.23% 0.04% 0.42% Taureg 2480 3.49% 2.74% 4.24% Fulani 2480 4.32% 3.47% 5.18% Kanuri 2480 0.66% 0.33% 0.98% Toubou 2480 0.04% 0.00% 0.12% Arab 2480 0.00% Gurma 2480 0.00% *significantly different from control at p<.05; 푥̅ = mean; CI = confidence interval

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There were minor differences in ethnicity among Niger treatment and control groups. All demographic means and percentages for each study arm are reported in Table 12.

Table 12. Niger Weighted Primary Sample Demographics – by Treatment & Control Baseline Data Treatment Control Variables N 푥̅ or % 95% CI N 푥̅ or % 95% CI Age 1264 15.71 15.65 15.77 1216 15.80 15.73 15.86 Married 1264 25.12% 19.63% 30.61% 1216 25.19% 4.18% 16.88% Age at marriage 309 14.90 14.75 15.05 343 14.87 14.73 15.01 Attended school 1264 59.31% 49.47% 69.15% 1216 59.49% 49.61% 69.37% Primary 775 38.43% 25.88% 50.97% 684 39.53% 29.56% 49.50% Secondary 775 61.16% 48.71% 73.60% 684 60.03% 50.36% 69.69% Higher 775 0.41% 0.03% 0.80% 684 0.44% 0.00% 0.95% Ethnicity Hausa 1264 87.41%* 80.22% 94.60% 1216 95.67% 93.42% 97.92% Djerma 1264 0.24% 0.00% 0.57% 1216 0.22% 0.00% 0.61% Taureg 1264 5.25% 0.97% 9.52% 1216 1.48% 0.00% 3.29% Fulani 1264 6.57%* 0.94% 12.21% 1216 1.75% 0.48% 3.02% Kanuri 1264 0.52% 0.18% 0.87% 1216 0.81% 0.26% 1.35% Toubou 1264 0.00% 1216 0.09% 0.00% 0.26% Arab 1264 0.00% 1216 0.00% Gurma 1264 0.00% 1216 0.00% *significantly different from control at p<.05; 푥̅ = mean; CI = confidence interval

Niger demographics key findings Most girls in Niger were ethnically Hausa (91.26%). There were ethnic differences between treatment and control groups with fewer girls reporting being Hausa. Also, there was a higher proportion of Fulani respondents in the treatment group. The average age in Niger was 15.76. Roughly a quarter (25.15%) of girls in Niger were married with the average age at marriage being 14.88. Approximately 59.39% of girls in the primary group have ever attended school. Of those who have received some education, most of them have only completed up to a secondary level (99.57%). Due to sensitivities related to religion in Niger, religious affiliation was not asked of respondents in the Niger, but country level data demonstrates that approximately 98% of all Nigerien citizens practice Islam.

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Priority Indicators Key messages: • In Bangladesh 51.11% of married girls report using any contraceptive to avoid pregnancy. • In Niger, 5.5% of married girls report using any contraceptive to avoid pregnancy. • Unmet family planning need was higher in Niger (20.78%) than in Bangladesh (6.27%).

Table 13 through Table 16 highlight the differences among the primary sample priority indicators SRHR service satisfaction, contraceptive prevalence and method use, unmet family planning need, healthcare service use, discussing family planning with a health worker, knowledge of modern contraceptive services, economic activities, monthly income, person savings, and capital assets. In Bangladesh, age at marriage, first birth, and the time between these two events were calculated for the secondary sample. In Niger, only age at first birth could be calculated and compared among treatment and control areas due to missing data issues.

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Bangladesh Table 13 shows that the weighted sample means and percentages of key indicators in Bangladesh, combined across treatment and control areas.

Table 13. Bangladesh Weighted Means and Percentages of Primary Sample and Select Secondary Sample Priority Indicators – Combined Treatment & Control Baseline Data Primary sample Variables N 푥̅ or % 95% CI Primary sample indicators SRHR service satisfaction (5=very satisfied) 318 4.27 4.20 4.35 Contraceptive prevalence 712 51.11% 46.92% 55.29% Female sterilization 364 0.00% Male sterilization 364 0.00% IUD 364 0.00% Injectable 364 0.28% 0.00% 0.85% Implant 364 0.00% Pill 364 39.23% 32.59% 45.88% Male condom 364 27.44% 22.83% 32.05% Female condom 364 0.00% Emergency contraception 364 0.58% 0.00% 1.42% Standard days 364 0.00% Rhythm 364 10.92% 7.69% 14.14% Withdrawal 364 21.52% 15.27% 27.78% Other 364 0.00% Traditional 364 0.00% Unmet family planning need 712 6.27% 4.46% 8.08% Healthcare visit (past 6mos) 2629 13.27% 11.96% 14.58% Discussed family planning (past 6mos) 2629 0.46% 0.20% 0.73% Knowledge of modern contraceptive services 2629 94.84% 94.01% 95.67% VSLA/savings group (past 6mos) 2629 1.16% 0.75% 1.56% Economic training (past 6mos) 2629 1.76% 1.25% 2.28% Income generating activity (past week) 2629 6.28% 2.69% 4.13% Income generating activity (past year) 2629 3.41% 2.69% 4.13% Monthly income (BDT) 76 1570.61 1216.36 1924.87 Has personal savings 2629 58.66% 56.77% 60.56% Amount (BDT) 1537 471.16 382.48 559.84 Has capital assets 2629 13.76% 12.44% 15.08% Secondary sample indicator Age at first birth 358 15.85 15.67 16.03 *significantly different from control at p<.05; 푥̅ = mean; CI = confidence interval

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Weighted priority indicator means shown by treatment and control group are provided in Table 14.

Table 14. Bangladesh Weighted Means and Percentages of Primary and Secondary Priority Indicators – by Treatment & Control Baseline Data Treatment Control Variables N 푥̅ or % 95% CI N 푥̅ or % 95% CI Primary Indicators SRHR service satisfaction (5=highly satisfied) 185 4.19* 4.09 4.29 133 4.39 4.28 4.51 Contraceptive prevalence 356 49.17% 43.59% 54.74% 356 53.05% 47.07% 59.04% Female sterilization 175 0.00% 189 0.00% Male sterilization 175 0.00% 189 0.00% IUD 175 0.00% 189 0.00% Injectable 175 0.59% 0.00% 1.76% 189 0.00% Implant 175 0.00% 189 0.00% Pill 175 38.43% 27.85% 49.01% 189 39.98% 31.86% 48.09% Male condom 175 24.14% 17.26% 31.03% 189 30.51% 24.72% 36.29% Female condom 175 0.00% 189 0.00% Emergency contraception 175 0.64% 0.00% 1.93% 189 0.53% 0.00% 1.60% Standard days 175 0.00% 189 0.00% Rhythm 175 10.56% 5.98% 15.14% 189 11.25% 7.00% 15.50% Withdrawal 175 25.60% 14.86% 36.33% 189 17.74% 11.93% 23.55% Other 175 0.00% 189 0.00% Traditional 175 0.00% 189 0.00% Unmet family planning need 356 6.69% 4.17% 9.21% 356 5.85% 3.47% 8.23% Healthcare visit (past 6mos) 1370 15.00% 11.79% 18.22% 1259 11.36% 9.10% 13.61% Discussed family planning (past 6mos) 1370 0.39% 0.01% 0.77% 1259 0.54% 0.19% 0.89% Knowledge of modern contraceptive services 1370 92.99%* 88.75% 97.24% 1259 96.88% 95.39% 98.37% VSLA/savings group (past 6mos) 1370 1.18% 0.58% 1.79% 1259 1.13% 0.54% 1.72% Economic training (past 6mos) 1370 2.05% 0.95% 3.15% 1259 1.44% 0.78% 2.11% Income generating activity (past week) 1370 6.88% 4.65% 9.10% 1259 5.61% 3.85% 7.38% Income generating activity (past year) 1273 3.61% 2.16% 5.05% 1190 3.20% 2.15% 4.24% Monthly income (BDT) 45 1279.55 904.28 1654.81 31 1996.72 1357.02 2636.42 Has personal savings 1370 51.34%* 44.06% 58.62% 1259 66.76% 62.68% 70.83% Amount (BDT) 1370 492.80 406.15 579.45 1259 452.76 306.18 599.34 Has capital assets 1370 8.37%* 6.32% 10.43% 1259 19.71% 14.87% 24.55% Secondary Indicator Age at first birth 194 15.87 15.60 16.14 164 15.83 15.60 16.05 *significantly different from control at p<.05; 푥̅ = mean; CI = confidence interval

Bangladesh priority indicator key findings There were statistical differences between the treatment and control group on SRHR service satisfaction, percent with knowledge of modern contraceptive services, percent reporting a personal savings, and percent reporting owning capital assets. Only 13.27% of girls in the primary sample had received any kind of services from a health care provider in Bangladesh in the 6 months preceding the survey. Among those that had used health services, average client satisfaction with SRHR services was between “satisfied” and “very satisfied” (4.27) indicating that most girls who receive services were satisfied with them. There were statistical differences in service satisfaction among the treatment and control groups. Girls in the treatment group reported being less satisfied with services at 4.19 compared

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to the control group at 4.39. Despite few girls receiving healthcare or discussing family planning (0.46%), knowledge of where to obtain modern contraceptives and services was high in Bangladesh at 94.84%. Girls in the treatment group were less familiar with service access (92.99%) than in the control area (96.88%).

In addition to whether girls had any savings, in Bangladesh they were also asked how much they had in savings. Overall, 58.66% of girls reported having savings. The average amount saved was ৳471.16 ($5.56 USD). The distribution of reported savings was skewed (i.e. extreme values that inflate the mean). The median savings was ৳97.22 ($1.15 USD) in the overall sample. Girls in the treatment group reported having savings at a significantly higher rate of 66.93% compared to the control group rate of 51.18%. Amounts of savings did not differ across groups. In the treatment group, the average amount saved was ৳492.80 ($5.81 USD) and in the control it was ৳452.76 ($5.34 USD). The median amount saved in the treatment group was ৳97.83 ($1.15 USD) and ৳95.33 ($1.12) in the control.

Capital assets are any item, food, or animal that could be sold if a family needed money. Despite more than half of girls reporting liquid assets, only 13.76% reported owning any capital assets. Girls in the control group (19.71%), were also more likely to report owning capital assets than those in treatment group (8.37%).

Questions regarding current contraceptive use were only asked of married girls in the primary sample (about one-quarter of the sample). Of these girls, only 51.11% reported using any contraceptive behavior or method to prevent pregnancy. Most girls were using modern forms of contraception such as the male condom (37.44%), the pill (39.23%) and a few girls reported using injectables (0.28%) and emergency contraception (0.58%). A little over one third of married girls were relying on non-modern forms of contraception such as the rhythm method (10.92%) or withdrawal (21.52%). Contraceptive prevalence and method type did not significantly differ among the primary married treatment and control groups.

There were also 21 girls, 12 in the treatment group and 9 in the control, who reported being married but indicated never having intercourse. Four of these girls presently reside with their husbands but 17 of them reported that their husband resides elsewhere. These girls were a range of ages (15-19) and some had been married for over two years or more (difference between age at time of marriage and current age).

The average unmet need among girls in Bangladesh was approximately 6.27%. This indicates that among 48.89% of married girls who were not using contraceptives most were doing so because they do not want to avoid being pregnant. Only 0.46% of girls reported discussing family planning with a health worker or promoter in the past 6 months. Combined with only 13.27% girls receiving health care services in the 6 months preceding the survey and that only 0.46% of them reported discussing family planning, this indicates a substantial need for service and education.

Few girls reported participating in an income generating activity either in the past week (6.28%) or past year (3.41%). In the Bangladesh, only girls who reported engaging in economic activity were asked their

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monthly income. Average monthly income in the primary sample was approximately ৳1570.61, which is 2 roughly equivalent to $18.49 USD 5F .

The average age at first birth in the secondary sample, was 15.85. The average age at first birth in the secondary sample was lower than the mean age at first marriage among the primary sample (15.94). This may be due to girls in the secondary sample being married at younger ages than the primary sample because only two women reported previous pregnancies but indicated not being married. Due to age at first marriage suffering from data errors in the secondary sample, time between birth and marriage could not be calculated.

2 All monetary conversions were calculated using Google, LLC exchange rates. Proprietary and Confidential Page 32 of 71

Niger Weighted sample means and percentages of key indicators are provided in Table 15 combined across treatment and control areas.

Table 15. Niger Weighted Means and Percentages of Primary Sample and Select Secondary Sample Priority Indicators – Combined Treatment & Control Baseline Data Primary sample Variables N 푥̅ or % 95% CI Primary Sample Indicators SRHR service satisfaction (5=highly satisfied) 247 4.15 4.03 4.27 Contraceptive prevalence 552 5.50% 3.21% 7.79% Female sterilization 25 0.00% Male sterilization 25 0.00% IUD 25 0.00% Injectable 25 31.19% 9.71% 52.67% Implant 25 12.07% 0.00% 27.94% Pill 25 49.49% 26.44% 72.54% Male condom 25 0.00% Female condom 25 0.00% Emergency contraception 25 0.00% Standard days 25 0.00% Rhythm 25 0.00% Withdrawal 25 0.00% Other 25 4.48% 0.00% 13.71% Traditional 25 2.77% 0.00% 8.59% Unmet family planning need 552 20.78% 17.23% 24.33% Healthcare visit (past 6mos) 2480 19.35% 17.59% 21.10% Discussed family planning (past 6mos) 2480 4.11% 3.19% 5.03% Knowledge of modern contraceptive services 2480 64.92% 62.90% 66.93% VSLA/savings group (past 6mos) 2480 3.90% 3.05% 4.75% Economic training (past 6mos) 2480 2.67% 1.97% 3.36% Income generating activity (past week) 2480 31.32% 29.38% 33.27% Income generating activity (past year) 2480 28.05% 26.16% 29.93% Monthly income (CFA) 2480 2765.68 2538.68 2992.68 Has personal savings 2480 8.01% 6.94% 9.08% Has capital assets 2480 18.19% 16.58% 19.81% Secondary Sample Indicators Age at marriage 539 14.39 14.25 14.52 Age at first birth 515 15.76 15.62 15.89 Years between marriage and first birth 494 1.40 1.30 1.51 *significantly different from control at p<.05; 푥̅ = mean; CI = confidence interval

Weighted means and tests of differences between treatment and control areas for these priority indicators are shown in Table 16.

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Table 16. Niger Weighted Means and Percentages of Primary and Secondary Priority Indicators – by Treatment & Control Baseline Data Treatment Control Variables N 푥̅ or % 95% CI N 푥̅ or % 95% CI Primary Sample Indicators SRHR service satisfaction (5=highly satisfied) 157 4.09 3.95 4.24 90 4.26 4.05 4.46 Contraceptive prevalence 283 7.54%* 3.91% 11.17% 269 2.75% 0.63% 4.86% Female sterilization 18 0.00% 7 0.00% Male sterilization 18 0.00% 7 0.00% IUD 18 0.00% 7 0.00% Injectable 18 29.64% 4.96% 54.32% 7 36.93% 0.00% 78.96% Implant 18 15.32% 0.00% 35.16% 7 0.00% Pill 18 51.51% 24.54% 78.48% 7 41.97% 2.53% 81.41% Male condom 18 0.00% 7 0.00% Female condom 18 0.00% 7 0.00% Emergency contraception 18 0.00% 7 0.00% Standard days 18 0.00% 7 0.00% Rhythm 18 0.00% 7 0.00% Withdrawal 18 0.00% 7 0.00% Other 18 0.00% 7 21.10% 0.00% 59.25% Traditional 18 3.52% 0.00% 10.92% 7 0.00% Unmet family planning need 283 22.02% 16.98% 27.05% 269 19.11% 14.28% 23.93% Healthcare visit (past 6mos) 1264 20.69% 18.20% 23.18% 1216 17.81% 15.36% 20.27% Discussed family planning (past 6mos) 1264 4.59% 3.26% 5.92% 1216 3.57% 2.31% 4.83% Knowledge of modern contraceptive services 1264 65.34% 62.50% 68.19% 1216 64.43% 61.59% 67.27% VSLA/savings group (past 6mos) 1264 4.55% 3.27% 5.82% 1216 3.15% 2.07% 4.24% Economic training (past 6mos) 1264 2.95% 1.91% 3.99% 1216 2.34% 1.44% 3.25% Income generating activity (past week) 1264 24.69%* 19.07% 30.30% 1216 29.23% 23.23% 35.24% Income generating activity (past year) 1264 28.10%* 25.43% 30.77% 1216 35.02% 32.19% 37.84% Monthly income (CFA) 1264 2342.07 2118.85 2565.28 1216 3250.95 2841.05 3660.84 Has personal savings 1264 7.33% 5.94% 8.72% 1216 8.78% 7.12% 10.44% Has capital assets 1264 19.80%* 17.42% 22.17% 1216 16.36% 14.22% 18.50% Secondary Sample Indicators Age at marriage 273 14.25* 14.06 14.45 266 14.59 14.44 14.75 Age at first birth 262 15.58* 15.39 15.77 253 16.04 15.87 16.21 Years between marriage and first birth 253 1.34 1.19 1.50 241 1.50 1.37 1.63 *significantly different from control at p<.05; 푥̅ = mean; CI = confidence interval

Niger priority indicators key findings There were statistical differences among contraceptive prevalence, income generating activities, income, and percentage owning capital assets between the treatment and control groups. Contraceptive prevalence was only asked of married girls and few reported using any form of contraception (5.50%). Contraceptive prevalence was statistically higher in the treatment group at 7.54% than the control group at 2.75%.

Among this small group of contraceptive users, most girls were relying on hormonal pills (49.49%). A few girls were also using implants (12.07%) and injectables (31.19%). There were observable differences in

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contraceptive use among treatment and control groups. However, the subsample of women using contraceptives was too small to determine any statistical differences. All implant use (15.32%) and traditional (3.52%) methods occurred in the treatment group. In the control group, 21.10% report relying on “other” methods, but it is unclear to what this may refer and was not reported in the treatment group. Similar to the Bangladesh sample, about one-quarter (652) of girls in the primary sample were married (309 in the treatment and 343 in the control). Among married girls, 26 in the treatment and 74 in the control group reported never having intercourse. However, in Niger, it appears that most of these girls were either 15 or 16 and were married at younger ages (12-14). Thirty-one of these girls reside with their husbands, but 69 of them reported that their husbands lived elsewhere.

Approximately 31.32% of girls in Niger reported participating in an income generating activity in the past week. In the past year, 28.05% of girls had performed any work for pay. Girls in the treatment group reported engaging in income generating activities less frequently than girls in the control group. In the past week, 24.69% of girls in treatment areas engaged in work for pay compared to 29.23% in the control. Over the past year, only 28.10% reported working for pay in the treatment areas and in the control 35.02% had in the past year.

The average monthly income in the full primary sample was $2765.68CFA ($4.66 USD). Income included a few extreme high values, which inflated the mean. The median monthly income is $987.57CFA ($1.67USD). Income was slightly lower in the treatment group, which may be related to the lower rates of girls participating in economic activity this area. The mean monthly income was $2342.07 with a median of 962.22 in the treatment area. Whereas in the control average monthly income was $3250.95 with a median of $994.59.

In the full sample. 18.19% of girls reported having at least one capital asset that could be sold. The rate of capital assets was significantly higher in the treatment group (19.80%) than in the control group (16.36%). Overall, 8.01% of girls reported having a personal savings. In the Niger data, these did not report how much they had in savings.

In Niger, 247 girls had interacted with SRHR services in the past six months. Among those who had received services, most reported being “satisfied” with services (average of 4.15 on a 5-point Likert item). Satisfaction with services was slightly lower among the treatment group at 4.08 compared to 4.27 in the control. This difference was not significant.

Unmet family planning needs were much higher in Niger. In the full sample, 20.78% of girls were classified as having an unmet need. Despite one fifth of girls reporting unmet family planning need, in the past 6 months, only 19.35% of girls have received healthcare and 4.11% of girls have discussed family planning with a health worker. These findings indicate the high need for family planning services in Niger.

In the secondary sample, the average age of marriage was 14.39. The average age at first birth was 15.76 and the average time between marriage and birth was 1.40 years. Age at marriage and age at first birth in the treatment group were significantly lower than in the control group. In the treatment group, average age at marriage was 14.25, age at first birth was 15.58, and the time between marriage and first birth was 1.34 years. In the control group, average age at marriage was 14.59, age at first birth was 16.04, and the time between was 1.50 years. If similar marital age differences between the treatment

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and control areas emerge among girls in the primary sample (as more of them enter marriage), the implications for endline analyses will be important to consider carefully and report.

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Priority Scales and Indices Key messages: • In Bangladesh, baseline scale and index means were generally more positive in the control group than the intervention group (e.g., higher reported levels of self-efficacy, self-esteem, and/or economic independence). • Self-esteem had low internal consistency in both Bangladesh and Niger.

Bangladesh All scales were constructed per CARE guidelines and scoring ranges are listed at the bottom of each table (see the “scale construction” section for more information). Table 17 shows the scale characteristics including the alpha coefficients for internal consistency reliability. Alphas of 0.70 of higher suggest good internal consistency scale reliability (Santos 1999).

Table 17. Bangladesh Primary Sample Weighted Means and Cronbach’s Alphas of Priority Scales – Combined Treatment & Control Baseline Data Primary sample Variables N 푥̅ or % 95% CI α Priority scales full sample 2629 Early pregnancy risk knowledgea 2.72 2.68 2.76 Belief in family planning mythsb 2.55 2.53 2.58 0.82 SE to go to a health facilityb 3.18 3.14 3.22 0.79 SE to engage in economic activityb 3.89 3.86 3.92 0.73 Rosenburg self-esteem scalec 35.46 35.34 35.59 0.43 Social Cohesionb 3.89 3.87 3.90 0.75 Collective efficacyb 3.78 3.75 3.82 0.91 Mobilityd 2.64 2.63 2.65 0.86 Total assetsa 0.76 0.73 0.78 Priority Scales for married only 733 SE to discuss family planningb 3.92 3.87 3.97 0.58 SE to refuse sexb 3.20 3.12 3.28 0.85 Non-financial household decisionse 1.55 1.53 1.57 0.71 Financial household decisionse 1.24 1.21 1.26 0.91 *significantly different from control at p<.05; 푥̅ = mean; CI = confidence interval SE=Self-efficacy α = Cronbach’s alpha a 4=high 0=none b 5=high 1=low c 50=high 10=low d 3=high 1=low e 2=high 1=low

Weighted estimates of priority scale and index indicators are reported in Table 18.

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Table 18. Bangladesh Primary Sample Weighted Means and Cronbach’s Alphas of Priority Scales and Indexes – by Treatment & Control Baseline Data Treatment Control Variables N 푥̅ or % 95% CI α N 푥̅ or % 95% CI α Priority scales full sample 1370 1259 Early pregnancy risk knowledgea 2.64* 2.57 2.70 2.81 2.75 2.87 Belief in family planning mythsb 2.65* 2.61 2.68 0.83 2.45 2.42 2.49 0.81 SE to go to a health facilityb 3.02* 2.97 3.07 0.79 3.36 3.30 3.41 0.77 SE to engage in economic activityb 3.74* 3.70 3.78 0.75 4.06 4.02 4.09 0.68 Rosenburg self-esteem scalec 35.07* 34.91 35.23 0.42 35.90 35.71 36.09 0.45 Social Cohesionb 3.75* 3.73 3.78 0.74 4.03 4.01 4.05 0.71 Collective efficacyb 3.60* 3.54 3.65 0.93 3.99 3.94 4.03 0.86 Mobilityd 2.66 2.64 2.67 0.83 2.62 2.61 2.64 0.88 Total assetsa 0.63* 0.60 0.67 0.90 0.86 0.93 Priority Scales for married only 368 365 SE to discuss family planningb 3.77* 3.69 3.84 0.59 4.08 4.01 4.15 0.53 SE to refuse sexb 3.04* 2.93 3.15 0.86 3.36 3.24 3.48 0.85 Non-financial household decisionse 1.49* 1.46 1.52 0.69 1.61 1.58 1.64 0.71 Financial household decisionse 1.17* 1.14 1.20 0.90 1.30 1.27 1.34 0.91 *significantly different from control at p<.05; 푥̅ = mean; CI = confidence interval SE=Self-efficacy α = Cronbach’s alpha a 4=high 0=none b 5=high 1=low c 50=high 10=low d 3=high 1=low e 2=high 1=low

Bangladesh priority scale and indices key findings In Bangladesh all priority scales and indices, except for the mobility scale, differed significantly between the treatment and control groups. The measures were somewhat more positive in the control areas, reflecting higher levels of self-efficacy, self-esteem, and economic independence. The average pregnancy risk knowledge score was close to three questions in Bangladesh at 2.72. Early pregnancy risk knowledge was lower in the treatment group at 2.64 compared to 2.81 in the control group at baseline. Belief in family planning myths was moderate. The average agreement with family planning myths was 2.55. There was also a high level of reliability among questions in the overall sample with an alpha of 0.82. Belief in family planning myths was higher in the treatment group at 2.65 compared to the control at 2.45.

Average self-efficacy to go to a health facility was 3.18 which is closest to “neither sure/unsure” in the original scale. The alpha coefficient indicated good reliability at 0.79. Health facility self-efficacy was lower in the treatment group than in the control group. The average self-efficacy to go to a health facility was 3.02 in the treatment group compared to 3.36 in the control group. Self-efficacy to engage in economic activities was also lower in the treatment group. In the full sample, economic self-efficacy was close to a score of 4 or “somewhat sure” (3.89) with an alpha reliability of 0.73. In the treatment group, economic self-efficacy was 3.74 and in the control group it was 4.06.

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The Rosenburg self-esteem scale had poor reliability with an alpha coefficient of 0.43. After variables were correctly coded and matched such that a 50 was equivalent with high self-esteem, Questions 8 and 9 in the Bangladesh data were negatively correlated with the overall scale. This mismatch between these questions and other self-esteem questions may be due to translation difficulties. Question 8 was “I wish I could have more respect for myself” and question 9 was “all in all, I am inclined to feel that I am a failure.” With these items either removed or the coding scheme changed the alpha reliability improved to 0.53, but this is still poor.

Overall sample mean self-esteem was somewhat low at 35.46. The treatment average was slightly lower and significantly different than the control group at 35.07 compared to 35.90. However, due to the poor reliability, these averages do not directly reflect true self-esteem in these populations.

Community characteristics social cohesion and collective efficacy were lower in the treatment areas. Average social cohesion was 3.89 with an alpha reliability of 0.75. In the treatment group social cohesion was 3.75 compared to 4.03 in the control. In the full primary sample, collective efficacy was 3.78 with a high alpha reliability of 0.91. The treatment average was of 3.59 compared to the control average of 3.99.

Most girls have mobility either independently or at least when accompanied. The average mobility across both treatment and control was 2.64 with an alpha of 0.86.

Few girls reported owning any assets. For those who reported any assets, most reported having only one kind of asset. The average count of assets was 0.76 in the full sample. Control group asset ownership on this scale was 0.90 on average and 0.63 in the treatment group.

Self-efficacy to discuss family planning, self-efficacy to refuse sex, and participation in both general household decisions and household financial decisions were only reported for married women. Self- efficacy to discuss family planning was close to a score of “somewhat sure” (3.92) however reliability of family planning self-efficacy was poor at 0.58. The control group also reported higher family planning self-efficacy with an average score of 4.08 compared to the treatment at 3.77. Average self-efficacy to refuse sex (3.20) was close to the neutral response category “neither sure/unsure.” The reliability of this scale was high at 0.85. Married girls in the treatment (3.04) group also report less efficacy in the ability to refuse sex than married girls in the control (3.36) group.

Girls expressed that they contribute to decision making some of the time. The average overall decision- making score was 1.55 with an alpha reliability of 0.71. Decision making was higher in the control group at 1.61 compared to 1.49 in the treatment. However, fewer girls report contributing to financial household decisions than non-financial decisions. The average financial decision-making score overall was 1.24 with a high alpha reliability of 0.91. Financial decision making was reported less frequently in the treatment group at 1.17 compared to 1.30 in the control group.

Niger Scales were constructed in Niger in the same way as in Bangladesh. See scale construction for detail descriptions of scale constructions. Overall, scale internal consistency reliability was somewhat higher in Niger compared to Bangladesh for all scales except for self-efficacy to engage in economic activity and non-financial decision making. Table 19 details these scale properties.

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Table 19. Niger Primary Sample Weighted Means and Cronbach’s Alphas of Priority Scales – Combined Treatment & Control Baseline Data Primary sample Variables N 푥̅ or % 95% CI α Priority scales full sample 2480 Early pregnancy risk knowledgea 2.17 2.11 2.23 Belief in family planning mythsb 3.05 3.02 3.09 0.90 SE to go to a health facilityb 2.60 2.56 2.65 0.77 SE to engage in economic activityb 3.23 3.20 3.26 0.69 Rosenburg self-esteem scalec 35.33 35.10 35.55 0.43 Social Cohesionb 3.71 3.68 3.74 0.83 Collective efficacyb 3.13 3.08 3.18 0.91 Mobilityd 2.44 2.42 2.46 0.78 Total assetsa 0.60 0.57 0.64 Priority Scales for married only 652 SE to discuss family planningb 2.44 2.35 2.54 0.78 SE to refuse sexb 1.99 1.89 2.08 0.91 Non-financial household decisionse 1.30 1.27 1.32 0.66 Financial household decisionse 1.18 1.16 1.21 0.86 *significantly different from control at p<.05; 푥̅ = mean; CI = confidence interval SE=Self-efficacy α = Cronbach’s alpha a 4=high 0=none b 5=high 1=low c 50=high 10=low d 3=high 1=low e 2=high 1=low

Niger priority scale and indices key findings There were no statistical differences in priority scales or indices in Niger (Table 20). Early pregnancy risk knowledge was moderate in Niger. On average girls in Niger answered two of the four (2.17) questions correctly. Agreement with family planning myths was also moderate at 3.05, which was close to “neither agree/unsure” in the original scale. Average self-efficacy to go to a health facility was 2.60 which was below the “neither sure/unsure” and above “somewhat unsure” response category. The alpha reliability indicated good reliability at 0.77. Economic self-efficacy was close to a score of 3 “neither sure/unsure” (3.23) in the overall sample with a low alpha reliability of 0.69.

In Niger, the overall reliability of the Rosenberg Self-Esteem scale was 0.43. Like Bangladesh, question 8 was negatively correlated with the overall scale which may be due to the wording of this question during translation “I wish I could have more respect for myself.” With either question 8 removed or the coding scheme changed the alpha reliability improved to 0.54, which is still poor. Overall sample mean self- esteem was 35.33. However, due to the poor reliability, these averages do not directly reflect true self- esteem scores in these populations.

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Table 20. Niger Primary Sample Weighted Means and Cronbach’s Alphas of Priority Scales and Indexes – by Treatment & Control Baseline Data Treatment Control Variables N 푥̅/% 95% CI α N 푥̅/% 95% CI α Priority scales full sample 1264 1216 Early pregnancy risk knowledgea 2.15 2.07 2.23 2.19 2.10 2.28 Belief in family planning mythsb 3.05 3.01 3.09 0.88 3.06 3.01 3.11 0.90 SE to go to a health facilityb 2.53 2.47 2.59 0.75 2.69 2.63 2.76 0.79 SE to engage in economic activityb 3.22 3.18 3.27 0.68 3.24 3.19 3.29 0.71 Rosenburg self-esteem scalec 35.22 34.91 35.52 0.41 35.45 35.13 35.77 0.46 Social cohesionb 3.68 3.64 3.73 0.83 3.73 3.69 3.78 0.83 Collective efficacyb 3.13 3.07 3.20 0.90 3.13 3.06 3.21 0.91 Mobilityd 2.43 2.40 2.45 0.76 2.47 2.44 2.49 0.80 Total assetsa 0.59 0.54 0.64 0.62 0.57 0.67 Priority Scales for married only 309 343 SE to discuss family planningb 2.51 2.38 2.64 0.77 2.37 2.23 2.50 0.79 SE to refuse sexb 1.98 1.84 2.12 0.90 1.99 1.86 2.13 0.92 Non-financial household decisionse 1.30 1.26 1.33 0.63 1.29 1.26 1.33 0.67 Financial household decisionse 1.19 1.15 1.22 0.87 1.18 1.15 1.21 0.85 *significantly different from control at p<.05; 푥̅ = mean; CI = confidence interval SE=Self-efficacy α = Cronbach’s alpha a 4=high 0=none b 5=high 1=low c 50=high 10=low d 3=high 1=low e 2=high 1=low

Social cohesion and collective efficacy were moderate in Niger. Average social cohesion was 3.71 which was close to “agree” in the original scale with a high alpha reliability of 0.83. Collective efficacy was close to “neither agree/disagree” at 3.13 with a high alpha reliability of 0.91. Most girls have mobility either independently or at least when accompanied. The average mobility across both treatment and control was 2.44 with an alpha of 0.78. Few girls reported owning any assets. Most girls who reported owning capital assets reported only one asset. The average was 0.60 in the full sample.

Self-efficacy to discuss and use family planning and to refuse sex and participation in both general and financial household decision making were only reported for married girls. Average self-efficacy to discuss family planning was 2.44 which is in-between “somewhat unsure” and “neither sure/unsure.” The alpha reliability for the family planning scale was 0.78. Mean self-efficacy to refuse sex (1.99) was near the “somewhat unsure” scale point. The reliability of this scale was high at 0.92. Girls expressed that they contribute to decision making some of the time. The average overall decision-making score was 1.30, but the alpha reliability was low at 0.66. Girls report contributing to financial household decisions less often than non-financial decisions. The average financial decision-making score overall was 1.18 with a high alpha reliability of 0.86.

Standardized Differences in Scale Means To identify whether the differences in the scale means between treatment and control groups in Bangladesh are meaningful, standardized differences in the weighted means were calculated (Table 21).

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These standardized differences were calculated by subtracting the treatment weighted mean by the control weighted mean and dividing by the pooled weighted standard deviations squared between the treatment and control groups in both Bangladesh and Niger. Negative differences indicate a larger mean value in the control areas, and positive differences indicate a larger mean value in the treatment areas.

푠푑2+푠푑2 Standardized difference in means: 푀 − 푀 ∕ √ 푡 푐 푡 푐 2

Standardized differences enable one to assess the magnitude of the difference relative to the distribution of each variable for the separate groups. Standardized mean differences of .20 are considered small, 0.50 are considered medium, and 0.80 are considered large (Lakens, 2013).

Mean differences between treatment and control groups in Bangladesh and Niger were small (above 0.20) and medium (above 0.50). In Bangladesh, the largest difference between treatment and control groups is in social cohesion (-0.58). The next largest differences were self-efficacy to discuss family planning (-0.41), self-efficacy to engage in economic activity (-0.40), collective efficacy (-0.40), and non- financial household decision making (-0.40). In Niger, the differences between the treatment and control were marginal. The largest difference in Niger was in self-efficacy to go to a health facility (0.12), yet it falls below the 0.20 (small) threshold.

Table 21. Standardized Differences in Weighted Means of Treatment and Control, by Country (Primary Sample Baseline Data) Bangladesh Niger Std. Std. Variables Difference in Difference in Mean Mean Priority scales full sample Early pregnancy risk knowledgea -0.14 -0.02 Belief in family planning mythsb 0.30 -0.01 SE to go to a health facilityb -0.31 -0.12 SE to engage in economic activityb -0.40 -0.01 Rosenburg self-esteem scalec -0.24 -0.03 Social Cohesionb -0.58 -0.05 Collective efficacyb -0.40 0.00 Mobilityd 0.09 -0.07 Total assetsa -0.37 -0.02 Priority Scales for married only SE to discuss family planningb -0.41 0.10 SE to refuse sexb -0.27 -0.01 Non-financial household decisionse -0.40 0.00 Financial household decisionse -0.38 0.02 SE=Self-efficacy

It will be important to consider the ways in which these differences in the standardized means between the baseline treatment and control groups inform the endline analyses (Altman, 1985). Because treatment and control groups were selected based on geographic location, the observed scale mean

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differences are likely related to the region or communities in which girls reside. Lower social cohesion and collective efficacy in the treatment group suggests that there may be less social connectedness among the treatment villages compared to the control villages. Self-efficacy to discuss and use family planning may also be shaped by the resources in the community and the ability to discuss family planning with a health worker in the area. For instance, in Bangladesh the treatment group also rated satisfaction with health services as lower. These baseline differences should provide necessary context for the endline analyses and discussion of results.

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Correlations of Priority Indicators, Scales, and Indexes Table 22. Bangladesh Pairwise Correlations of Priority Indicators and Scales - Baseline Data Variables 1 SRHR service satisfaction 1.00 2 Contraceptive use -0.01 1.00 3 Unmet family planning need -0.17 -0.26* 1.00 4 Healthcare visit past 6mos 0.00 -0.06 0.01 1.00 5 Discussed FP in the past 6mos 0.05 -0.03 -0.03 0.17* 1.00 6 Knowledge of modern contraceptives 0.01 0.08* 0.03 0.04* -0.01 1.00 7 VSLA/savings group (past 6mos) 0.06 0.04 -0.03 0.01 -0.01 -0.02 1.00 8 Economic training (past 6mos) -0.02 0.00 -0.03 0.05* 0.03 0.01 -0.01 1.00 9 Income generating activity (week) -0.01 -0.04 -0.02 0.08* 0.02 0.05* 0.01 0.04 1.00 10 Income generating activity (year) 0.01 0.06 -0.01 0.06* 0.02 0.04 0.00 -0.01 0.00 1.00 11 Income 0.13 -0.07 0.00 -0.14 -0.10 0.09 0.00 -0.02 -0.08 -0.13 1.00 12 Personal savings -0.02 0.10* 0.01 0.02 0.01 0.19* 0.04* 0.03 -0.01 0.03 0.08 1.00 13 Capital assets 0.00 -0.05 -0.10* 0.01 0.05* 0.04* 0.07* 0.05* 0.09* 0.03 0.07 0.09* 1.00 14 Early pregnancy risk knowledge 0.08 -0.04 0.03 0.02 0.03 0.19* 0.03 0.02 0.06* 0.08* -0.10 0.09* 0.00 1.00 15 Contraceptive myths 0.09 -0.08* -0.01 0.06* 0.00 -0.03 0.02 0.00 -0.04* 0.00 -0.10 -0.05* -0.01 -0.01 1.00 16 SE to go to a health facility -0.04 0.15* 0.02 0.01 0.01 0.18* 0.03 0.02 0.01 0.08* 0.04 0.2* 0.10* 0.02 -0.22* 1.00 17 SE to engage in economic activity 0.03 0.18* 0.00 0.04* 0.01 0.14* 0.04 0.06* 0.03 0.09* -0.02 0.19* 0.10* 0.09* -0.08* 0.52* 1.00 18 Rosenberg self-esteem 0.15* 0.10* 0.03 0.00 -0.02 0.01 0.02 0.02 0.01 0.01 -0.06 0.03 0.04 0.05* -0.08* 0.02 0.15* 1.00 19 Social cohesion 0.14* 0.07 -0.03 -0.03 0.00 0.09* 0.01 -0.02 0.04* 0.00 0.05 0.07* 0.08* 0.07* -0.14* 0.13* 0.18* 0.21* 1.00 20 Collective efficacy 0.06 0.14* 0.01 0.02 0.04 0.08* 0.04 0.01 0.02 0.05* 0.16 0.08* 0.12* 0.02 -0.05* 0.38* 0.49* 0.13* 0.29* 1.00 21 Mobility -0.06 0.15* 0.03 0.10* 0.00 0.17* 0.03 0.07* 0.09* 0.09* -0.02 0.12* 0.08* 0.04* -0.02 0.24* 0.34* 0.15* 0.15* 0.2* 1.00 22 Total assets -0.02 0.05 -0.06 0.05* 0.03 0.17* 0.07* 0.06* 0.16* 0.14* 0.11 0.8* 0.59* 0.07* -0.04* 0.21* 0.21* 0.04* 0.08* 0.13* 0.15* 1.00 23 SE to discuss family planning 0.04 0.12* 0.06 0.00 0.01 0.15* 0.08* -0.02 -0.05 0.03 0.34 0.19* 0.11* -0.04 -0.08* 0.52* 0.45* 0.15* 0.14* 0.33* 0.20* 0.2* 1.00 24 SE to refuse sex 0.20 0.18* 0.02 0.06 0.06 0.02 0.03 0.06 -0.02 0.09* -0.44 0.06 0.07 -0.10* -0.03 0.51* 0.40* 0.08* 0.14* 0.37* 0.17* 0.09* 0.42* 1.00 25 Non-financial household decisions 0.10 0.05 -0.05 0.03 0.05 -0.02 0.02 0.01 -0.01 0.02 0.13 0.12* 0.13* -0.07 -0.17* 0.25* 0.27* 0.21* 0.22* 0.20* 0.20* 0.15* 0.29* 0.27* 1.00

26 Financial household decisions 0.06 0.00 -0.07 0.02 0.06 0.04 0.08* 0.04 0.06 0.11* 0.96* 0.15* 0.23* -0.07 -0.16* 0.32* 0.31* 0.17* 0.25* 0.28* 0.30* 0.24* 0.26* 0.25* 0.69* 1.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 *Correlation is significant at p<.05

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Table 23. Niger Pairwise Correlations of Priority Indicators and Scales - Baseline Data Variables 1 SRHR service satisfaction 1.00 2 Contraceptive use 0.24* 1.00 3 Unmet family planning need 0.05 -0.12* 1.00 4 Healthcare visit past 6mos 0.00 0.15* -0.12* 1.00 5 Discussed FP in the past 6mos -0.03 0.3* -0.06 0.42* 1.00 6 Knowledge of modern contraceptives 0.17* 0.12* 0.00 0.09* 0.11* 1.00 7 VSLA/savings group (past 6mos) 0.02 0.1* -0.03 0.02 0.01 -0.02 1.00 8 Economic training (past 6mos) -0.03 -0.03 -0.03 0.03 0.06* -0.02 0.14* 1.00 9 Income generating activity (week) -0.02 0.00 -0.01 0.03 0.04* 0.00 0.11* 0.11* 1.00 10 Income generating activity (year) -0.02 0.01 -0.01 0.02 0.05* 0.00 0.09* 0.1* 0.92* 1.00 11 Income -0.06 -0.01 0.01 0.06* 0.01 0.04* 0.08* 0.04* 0.11* 0.12* 1.00 12 Personal savings -0.03 0.04 -0.03 0.04* 0.01 0.04* 0.34* 0.06* 0.15* 0.13* 0.12* 1.00 13 Capital assets -0.08 -0.06 0.03 -0.02 -0.03 -0.05* 0.06* 0.04* 0.21* 0.22* 0.06* 0.22* 1.00 14 Early pregnancy risk knowledge 0.09 0.06 -0.01 0.13* 0.09* 0.29* 0.01 0.02 -0.07* -0.08* -0.07* 0.01 -0.03 1.00 15 Contraceptive myths 0.03 -0.11* 0.12* -0.03 -0.05* 0.01 0.05* 0.00 0.07* 0.06* 0.03 0.06* 0.07* 0.11* 1.00 16 SE to go to a health facility 0.14* 0.09* -0.02 0.08* 0.08* 0.15* 0.05* 0.00 -0.08* -0.09* 0.04 0.04* -0.03 0.07* -0.07* 1.00 17 SE to engage in economic activity 0.2* 0.16* -0.02 0.09* 0.1* 0.11* 0.01 0.06* 0.01 0.01 -0.12* 0.03 0.04* 0.18* -0.03 0.43* 1.00 18 Rosenberg self-esteem 0.17* 0.01 -0.02 -0.02 0.06* -0.02 0.01 0.05* 0.1* 0.1* 0.1* -0.06* 0.03 0.04 0.03 -0.02 0.07* 1.00 19 Social cohesion 0.15* 0.05 -0.02 0.05* 0.04* 0.06* 0.01 0.01 0.03 0.01 -0.03 -0.04* 0.05* 0.11* 0.12* 0.09* 0.19* 0.15* 1.00 20 Collective efficacy 0.05 0.06 -0.05 0.07* 0.09* 0.00 -0.04* 0.04 -0.09* -0.11* -0.05* 0.00 0.00 0.12* -0.04 0.14* 0.27* 0.03 0.38* 1.00 21 Mobility 0.02 0.1* 0.03 0.03 0.07* 0.09* 0.04 0.06* 0.12* 0.14* -0.14* 0.00 0.05* 0.2* 0.04 0.17* 0.3* 0.05* 0.11* 0.1* 1.00 22 Total assets -0.13* -0.04 -0.02 0.05* 0.02 0.02 0.25* 0.1* 0.41* 0.41* 0.19* 0.57* 0.56* -0.02 0.13* -0.03 0.03 0.01 -0.04* -0.09* 0.07* 1.00 23 SE to discuss family planning 0.11 0.24* -0.01 0.08* 0.14* 0.27* 0.05 -0.05 0.02 -0.01 -0.05 0.05 -0.06 0.05 -0.1* 0.5* 0.41* -0.11* 0.15* 0.2* 0.1* -0.01 1.00 24 SE to refuse sex 0.16 -0.07 -0.05 0.07 0.04 0.06 0.04 0.06 0.09* 0.1* -0.14* 0.13* 0.00 0.03 -0.07 0.27* 0.24* -0.13* 0.05 0.18* -0.05 0.03 0.35* 1.00 25 Non-financial household decisions -0.08 0.02 -0.06 0.08* 0.11* 0.07 -0.06 -0.02 -0.05 -0.1* 0.02 0.11* -0.04 0.00 -0.05 0.15* 0.15* -0.02 0.03 0.21* -0.02 0.01 0.23* 0.17* 1.00

26 Financial household decisions 0.03 0.04 -0.02 0.06 0.1* 0.06 -0.04 0.03 -0.02 -0.06 0.05 0.06 0.03 0.05 0.00 0.09* 0.21* 0.05 0.04 0.21* 0.04 0.05 0.12* 0.08* 0.66* 1.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 *Correlation is significant at p<.05

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Bangladesh Unweighted Demographics Table 1a. Bangladesh Unweighted Means and Percentages of Demographics – Combined Treatment & Control Sample Baseline Data Primary sample Mean/ Variables N sd percent Sociodemographic Factors Age 2629 16.38 1.27 Married 2629 27.88% ̶ Age at marriage 733 15.93 1.44 Attended school 2629 99.73% ̶ Primary 2622 7.02% ̶ Secondary 2622 67.47% ̶ Higher 2622 25.51% ̶ Bengali 2629 100.00% ̶ Religious affiliation ̶ Muslim 2629 91.75% ̶ Hindu 2629 8.25% ̶ sd = standard deviation

Table 2a. Bangladesh Unweighted Primary Sample Demographics – by Treatment & Control Baseline Data Treatment Control Mean/ Mean/ Variables N sd N sd percent percent Sociodemographic Factors Age 1370 16.36 1.29 1259 16.39 1.25 Married 1370 26.86% 1259 28.99% Age at marriage 368 15.98 1.48 365 15.90 1.40 Attended school 1370 99.85% 1259 99.60% Primary 1368 7.16% 1254 6.86% Secondary 1368 66.96% 1254 68.02% Higher 1368 25.88% 1254 25.12% Bengali 1370 100.00% 1259 100.00% Religious affiliation Muslim 1370 90.29% 1259 93.32% Hindu 1370 9.71% 1259 6.67% sd = Standard deviation

Niger Unweighted Demographics Table 3a. Niger Unweighted Means and Percentages of Demographics – Combined Treatment & Control Sample Baseline Data Full Sample Mean/ Wt. mean/ Variables N sd percent percent Sociodemographic Factors Age 2480 15.76 1.07 15.76 Married 2480 26.29% 25.10% Age at marriage 652 14.87 1.27 14.89 Attended school 2480 58.83% 59.70% Primary 1459 40.10% 39.34% Secondary 1459 59.49% 59.78% Higher 1459 0.41% 0.42% Ethnicity 2480 Hausa 2480 91.01% 91.51% Djerma 2480 0.24% 0.25% Taureg 2480 3.83% 3.23% Fulani 2480 4.23% 4.29% Kanuri 2480 0.65% 0.68% Toubou 2480 0.04% 0.05% Arab 2480 0.00% 0.00% Gurma 2480 0.00% 0.00% sd = Standard deviation

Table 4a. Niger Unweighted Primary Sample Demographics – by Treatment & Control Baseline Data Treatment Control Mean/ Mean/ Variables N sd N sd percent percent Sociodemographic Factors Age 1264 15.74 1.03 1216 15.79 1.11 Married 1264 24.44% 1216 28.22% Age at marriage 309 14.91 1.34 343 14.84 1.20 Attended school 1264 61.31% 1216 56.25% Primary 775 37.67% 684 42.83% Secondary 775 61.94% 684 56.72% Higher 775 0.39% 684 0.44% Ethnicity Hausa 1264 87.10% 1216 95.07% Djerma 1264 0.32% 1216 0.16% Taureg 1264 5.54% 1216 2.06% Fulani 1264 6.41% 1216 1.97% Kanuri 1264 0.63% 1216 0.66% Toubou 1264 0.00% 1216 0.10% Arab 1264 0.00% 1216 0.00% Gurma 1264 0.00% 1216 0.00% Other 1264 0.00% 1216 0.00% sd = Standard deviation

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Bangladesh Unweighted Priority Indicators Table 5a. Bangladesh Unweighted Means and Percentages of Primary and Secondary Sample Priority Indicators – Combined Treatment & Control Sample Baseline Data Primary sample Mean/ Variables N sd percent Primary sample indicators SRHR service satisfaction (5=very satisfied) 318 4.27 0.71 Contraceptive prevalence 712 51.12% Female sterilization 364 0.00% Male sterilization 364 0.00% IUD 364 0.00% Injectable 364 0.27% Implant 364 0.00% Pill 364 39.01% Male condom 364 27.77% Female condom 364 0.00% Emergency contraception 364 0.55% Standard days 364 0.00% Rhythm 364 10.99% Withdrawal 364 21.43% Other 364 0.00% Traditional 364 0.00% Unmet family planning need 712 6.18% Discussed family planning (past 6mos) 2629 0.46% Knowledge of modern contraceptive services 2629 94.60% VSLA/savings group (past 6mos) 2629 1.18% Economic training (past 6mos) 2629 1.71% Income generating activity (past week) 2629 6.31% Income generating activity (past year) 2629 3.41% Monthly income (BDT) 76 1598.54 1577.80 Has personal savings 2629 58.46% Amount (BDT) 1537 477.64 1936.27 Has capital assets 2629 13.81% Secondary sample indicator Age at first birth 358 16.01 1.46 sd = Standard deviation

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Table 6a. Bangladesh Unweighted Means and Percentages of Primary and Secondary Priority Indicators – by Treatment & Control Baseline Data Treatment Control Mean/ Mean/ Variables N sd N sd percent percent Primary Indicators SRHR service satisfaction (5=highly satisfied) 185 4.19 0.72 133 4.38 0.69 Contraceptive prevalence 356 49.16% 356 53.09% Female sterilization 175 0.00% 189 0.00% Male sterilization 175 0.00% 189 0.00% IUD 175 0.00% 189 0.00% Injectable 175 0.57% 189 0.00% Implant 175 0.00% 189 0.00% Pill 175 37.71% 189 40.21% Male condom 175 27.43% 189 33.86% Female condom 175 0.00% 189 0.00% Emergency contraception 175 0.57% 189 0.53% Standard days 175 0.00% 189 0.00% Rhythm 175 12.57% 189 11.11% Withdrawal 175 25.71% 189 17.46% Other 175 0.00% 189 0.00% Traditional 175 0.00% 189 0.00% Unmet family planning need 356 6.46% 356 5.90% Discussed family planning (past 6mos) 1370 0.37% 1259 0.56% Knowledge of modern contraceptive services 1370 92.55% 1259 96.82% VSLA/savings group (past 6mos) 1370 1.24% 1259 1.11% Economic training (past 6mos) 1370 1.97% 1259 1.43% Income generating activity (past week) 1370 7.08% 1259 5.48% Income generating activity (past year) 1273 3.61% 1190 3.19% Monthly income (BDT) 45 1310.87 1322.16 31 2016.13 1831.77 Has personal savings 1370 66.56% 1259 51.02% Amount (BDT) 1370 492.91 1157.54 1259 464.90 2400.50 Has capital assets 1370 8.47% 1259 19.62% Secondary Indicator Age at first birth 194 16.02 1.43 164 15.99 1.49 sd = Standard deviation

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Niger Unweighted Priority Indicators Table 7a. Niger Unweighted Means and Percentages of Primary, and Secondary Sample Priority Indicators – Combined Treatment & Control Sample Baseline Data Full Sample Mean/ Variables N sd percent Primary Sample Indicators SRHR service satisfaction (5=highly satisfied) 247 4.11 0.88 Contraceptive prevalence 552 4.53% Female sterilization 25 0.00% Male sterilization 25 0.00% IUD 25 0.00% Injectable 25 28.00% Implant 25 12.00% Pill 25 52.00% Male condom 25 0.00% Female condom 25 0.00% Emergency contraception 25 0.00% Standard days 25 0.00% Rhythm 25 0.00% Withdrawal 25 0.00% Other 25 4.00% Traditional 25 4.00% Unmet family planning need 552 21.56% Discussed family planning (past 6mos) 2480 3.55% Knowledge of modern contraceptive services 2480 64.72% VSLA/savings group (past 6mos) 2480 3.79% Economic training (past 6mos) 2480 2.62% Income generating activity (past week) 2480 31.69% Income generating activity (past year) 2480 28.19% Monthly income (CFA) 2480 2657.20 4832.52 Has personal savings 2480 8.91% 0.28 Has capital assets 2480 18.79% 0.39 Secondary Sample Indicators Age at marriage 539 14.50 1.26 Age at first birth 515 15.86 1.32 Years between marriage and first birth 494 1.41 0.95 sd = Standard deviation

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Table 8a. Niger Unweighted Means and Percentages of Primary and Secondary Sample Priority Indicators – by Treatment & Control Baseline Data Treatment Control Mean/ Mean/ Variables N sd N sd percent percent Primary Sample Indicators SRHR service satisfaction (5=highly satisfied) 157 4.05 0.85 90 4.20 0.93 Contraceptive prevalence 283 6.36% 269 2.29% Female sterilization 18 0.00% 7 0.00% Male sterilization 18 0.00% 7 0.00% IUD 18 0.00% 7 0.00% Injectable 18 27.78% 7 28.57% Implant 18 16.66% 7 0.00% Pill 18 50.00% 7 57.14% Male condom 18 0.00% 7 0.00% Female condom 18 0.00% 7 0.00% Emergency contraception 18 0.00% 7 0.00% Standard days 18 0.00% 7 0.00% Rhythm 18 0.00% 7 0.00% Withdrawal 18 0.00% 7 0.00% Other 18 0.00% 7 14.29% Traditional 18 5.56% 7 0.00% Unmet family planning need 283 23.32% 269 19.70% Discussed family planning (past 6mos) 1264 4.03% 1216 3.04% Knowledge of modern contraceptive services 1264 65.74% 1216 63.65% VSLA/savings group (past 6mos) 1264 4.59% 1216 2.96% Economic training (past 6mos) 1264 2.93% 1216 2.30% Income generating activity (past week) 1264 28.32% 1216 35.20% Income generating activity (past year) 1264 23.73% 1216 32.81% Monthly income (CFA) 1264 2423.76 4431.93 1216 2899.85 5202.60 Has personal savings 1264 8.86% 1216 8.94% Has capital assets 1264 19.86% 1216 17.68% Secondary Sample Indicators Age at marriage 273 14.42 1.31 266 14.57 1.20 Age at first birth 262 15.70 1.35 253 16.04 1.27 Years between marriage and first birth 253 1.31 0.90 241 1.51 1.00 sd = Standard deviation

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Bangladesh Unweighted Priority Scales and Indices Table 9a. Bangladesh Unweighted Means and Cronbach’s Alphas of Priority Scales – Combined Treatment & Control Sample Baseline Data Full sample Variables N Mean sd Priority scales full sample 2629 Early pregnancy risk knowledgea 2.72 1.15 Belief in family planning mythsb 2.56 0.62 SE to go to a health facilityb 3.18 1.01 SE to engage in economic activityb 3.89 0.75 Rosenburg self-esteem scalec 35.45 3.21 Social Cohesionb 3.89 0.47 Collective efficacyb 3.78 0.93 Mobilityd 2.64 0.32 Total assetsa 0.18 0.43 Priority Scales for married only 733 SE to discuss family planningb 3.93 0.73 SE to refuse sexb 3.20 1.12 Non-financial household decisionse 1.55 0.28 Financial household decisionse 1.24 0.33 SE=Self-efficacy ; sd = Standard deviation a 4=high 0=none; b 5=high 1=low; c 50=high 10=low; d 3=high 1=low; e 2=high 1=low

Table 10a. Bangladesh Unweighted Means and Cronbach’s Alphas of Priority Scales and Indexes – by Treatment & Control Baseline Data Treatment Control Variables N Mean sd N Mean sd Priority scales full sample 1370 1259 Early pregnancy risk knowledgea 2.63 1.20 2.81 1.09 Belief in family planning mythsb 2.65 0.60 2.45 0.64 SE to go to a health facilityb 3.01 0.99 3.36 1.01 SE to engage in economic activityb 3.74 0.78 4.06 0.68 Rosenburg self-esteem scalec 35.05 3.01 35.89 3.37 Social Cohesionb 3.75 0.44 4.03 0.45 Collective efficacyb 3.60 1.02 3.98 0.80 Mobilityd 2.65 0.30 2.62 0.33 Total assetsa 0.13 0.38 0.24 0.68 Priority Scales for married only 368 365 SE to discuss family planningb 3.76 0.73 4.08 0.70 SE to refuse sexb 3.04 1.07 3.36 1.14 Non-financial household decisionse 1.49 0.28 1.61 0.28 Financial household decisionse 1.18 0.29 1.30 0.36 SE=Self-efficacy sd = Standard deviation α = Cronbach’s alpha a 4=high 0=none; b 5=high 1=low; c 50=high 10=low; d 3=high 1=low; e 2=high 1=low

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Niger Unweighted Priority Scales and Indices Table 11a. Niger Unweighted Means and Cronbach’s Alphas – Combined Treatment & Control Sample Baseline Data Full sample Variables N Mean sd Priority scales full sample 2480 Early pregnancy risk knowledgea 2.16 1.40 Belief in family planning mythsb 3.03 0.80 SE to go to a health facilityb 2.63 1.04 SE to engage in economic activityb 3.23 0.84 Rosenburg self-esteem scalec 35.09 5.10 Social Cohesionb 3.70 0.73 Collective efficacyb 3.15 1.23 Mobilityd 2.44 0.46 Total assetsa 0.62 0.91 Priority Scales for married only 652 SE to discuss family planningb 2.51 1.14 SE to refuse sexb 1.97 1.16 Non-financial household decisionse 1.30 0.29 Financial household decisionse 1.18 0.27 SE=Self-efficacy sd = Standard deviation α = Cronbach’s Alpha a 4=high 0=none; b 5=high 1=low; c 50=high 10=low; d 3=high 1=low; e 2=high 1=low

Table 12a. Niger Unweighted Means and Cronbach’s Alphas of Priority Scales and Indexes – by Treatment & Control Baseline Data Treatment Control Variables N Mean sd N Mean sd Priority scales full sample 1264 1216 Early pregnancy risk knowledgea 2.13 1.38 2.18 1.42 Belief in family planning mythsb 3.05 0.77 3.02 0.84 SE to go to a health facilityb 2.55 1.02 2.71 1.05 SE to engage in economic activityb 3.21 0.83 3.26 0.85 Rosenburg self-esteem scalec 35.05 5.07 35.13 5.13 Social Cohesionb 3.68 0.73 3.72 0.73 Collective efficacyb 3.12 1.21 3.17 1.25 Mobilityd 2.41 0.45 2.48 0.47 Total assetsa 0.63 0.93 0.62 0.90 Priority Scales for married only 309 343 SE to discuss family planningb 2.52 1.14 2.50 1.14 SE to refuse sexb 1.91 1.06 1.94 1.12 Non-financial household decisionse 1.29 0.28 1.31 0.30 Financial household decisionse 1.17 0.27 1.18 0.26 *Statistically different from control group at p<.05 SE=Self-efficacy sd = Standard deviation α = Cronbach’s Alpha a 4=high 0=none; b 5=high 1=low; c 50=high 10=low; d 3=high 1=low; e 2=high 1=low

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Appendix B: Primary and Secondary Sample Summary Tables

Bangladesh Primary and Secondary Sample Counts

Sampling and Response Rates Tables 1b and 2b show a summary of actual versus expected sampling rates and the observed response rate for the primary and secondary samples in Bangladesh by study arm and union.

Table 1b. Actual vs Expected Sample Size by Union in Bangladesh - Primary Sample Enum. Target Final Final % of % of Pop. Sample Sample Survey Target Target Response Union Est. Pop. (Frame) Size Count Count Sampled Complete Rate Treatment Belgachha 969 820 785 774 686 98.6% 87.4% 88.6% Punchgachhi 1034 760 838 760 684 90.7% 81.6% 90.0% Treatment Totals 2003 1580 1625 1534 1370 94.5% 84.4% 89.3% Control Bhogdanga 1469 896 1082 877 790 81.1% 73.0% 90.1% Kanthalbari 743 533 545 512 469 93.9% 86.0% 91.6% Control Totals 2212 1429 1625 1389 1259 85.9% 77.4% 90.6% Combined Totals 4215 3009 3250 2923 2629 89.9% 80.9% 89.9%

Table 2b. Actual vs Expected Sample Size by Union in Bangladesh – Secondary Sample Enum. Target Final Final % of % of Pop. Sample Sample Survey Target Target Response Union Est. Pop. (Frame) Size Count Count Sampled Complete Rate Treatment Belgachha 412 224 124 133 123 107.3% 99.2% 92.5% Punchgachhi 440 491 135 157 144 116.3% 106.7% 91.7% Treatment Totals 852 715 259 290 267 112.0% 103.1% 92.1% Control Bhogdanga 629 552 173 183 168 105.8% 97.1% 91.8% Kanthalbari 317 170 85 90 85 105.9% 100.0% 94.4% Control Totals 946 722 258 273 253 105.8% 98.1% 92.7% Combined Totals 1798 1437 517 563 520 108.9% 100.6% 92.4%

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Niger Primary and Secondary Sample Counts Tables 3b and 4b show a summary of actual versus expected sampling rates and the observed response rate for the primary and secondary samples in Niger by study arm and union.

Table 3b. Actual vs Expected Sample Size by Commune in Niger – Primary Sample Enum. Target Final Final % of % of Pop. Sample Sample Survey Target Target Response Union Est. Pop. (Frame) Size Count Count Sampled Complete Rate Treatment Dogo 1036 1518 894 1088 825 121.7% 92.5% 75.8% Kolleram 657 732 569 628 439 110.3% 77.7% 69.9% Treatment Totals 1693 2250 1463 1716 1264 117.3% 86.7% 73.7% Control Gaffati 377 643 355 444 353 125.1% 99.4% 79.5% Gouna 708 862 668 672 580 100.6% 86.8% 86.3% Hamdara 198 293 188 180 164 95.7% 87.2% 91.1% Zermou 179 162 169 157 119 92.9% 70.4% 75.8% Control Totals 1462 1960 1380 1453 1216 85.9% 88.1% 83.7% Totals 3155 4319 2843 3169 2485 111.5% 87.4% 78.4%

Table 4b. Actual vs Expected Sampling by Commune in Niger – Secondary Sample Enum. Target Final Final % of % of Pop. Sample Sample Survey Target Target Response Union Est. Pop. (Frame) Size Count Count Sampled Complete Rate Treatment Dogo 451 1019 103 230 184 223.3% 178.6% 80.7% Kolleram 848 478 192 146 104 76.0% 54.2% 72.7% Treatment Totals 1299 1497 295 376 288 127.5% 97.6% 77.6% Control Gaffati 310 344 75 101 75 134.7% 100.0% 74.3% Gouna 580 388 144 162 139 112.5% 96.5% 85.8% Hamdara 160 172 39 52 36 133.3% 92.3% 69.2% Zermou 147 51 36 33 28 91.7% 77.8% 84.8% Control Totals 1197 955 294 348 278 118.4% 94.6% 79.9% Totals 2496 2452 589 765 566 122.9% 96.1% 74.3%

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Appendix C: Sampling and Response Rates

Bangladesh Primary Sampling and Response Rates by Village Table 1c. Bangladesh Sampling and Response Rates for Primary Sample Villageid Village Frame Count Sample Sampling Rate Respondents Response Rate Count 1001 Harishwar Kalua 71 61 86% 56 92% 1002 Fakir Para 41 41 100% 39 95% 1003 Pramaniktari 40 40 100% 37 93% 1004 Cherenga 36 36 100% 34 94% 1005 Adhgram 41 38 93% 34 89% 1006 Majhi Para 13 13 100% 12 92% 1007 Bepari Para 28 28 100% 27 96% 1008 Bhabaneshwar 30 30 100% 27 90% 1009 Kamar Para 23 23 100% 21 91% 1010 Dasarhat 23 23 100% 20 87% 1011 Khan Para 34 27 79% 23 85% 1012 Bhanur Bhita 23 23 100% 21 91% 1013 Sarkar Para 21 21 100% 21 100% 1014 Bepari Para 15 15 100% 15 100% 1015 Khamar Para 15 15 100% 14 93% 1016 Ragati Para 22 19 86% 18 95% 1017 Khularpar 10 10 100% 9 90% 1018 Dawpara 11 11 100% 10 91% 1019 Karji Para 12 12 100% 12 100% 1020 Kuya Para 5 5 100% 5 100% 1021 Ghosh Para 19 15 79% 14 93% 2001 Madhabram 84 84 100% 76 90% 2002 Nanda Dulalar Bhita 36 36 100% 34 94% 2003 Maudipur 47 47 100% 41 87% 2004 Bhogdanga 41 41 100% 37 90% 2005 Char Baraibari Namagram 50 50 100% 42 84% 2006 Kashi Char 26 26 100% 24 92% 2007 Kumarpur 37 37 100% 33 89% 2008 Paramali 42 41 98% 36 88% 2009 Shibuttar 35 35 100% 32 91% 2010 Gopalhalar Khamar 32 32 100% 30 94% 2011 Basur Bhita 30 30 100% 28 93% 2012 Gaurar Khama 38 34 89% 32 94%

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Table 1c. Bangladesh Sampling and Response Rates for Primary Sample Villageid Village Frame Count Sample Sampling Rate Respondents Response Rate Count 2014 Char Baraibari Upargram 24 24 100% 20 83% 2016 Dungdungirhat 31 29 94% 27 93% 2017 Nagar Khamar 30 26 87% 25 96% 2018 Kaim Baraibari 15 15 100% 15 100% 2019 Umarer Bhita 22 22 100% 20 91% 2021 Shibuttar Moylar Bhita 17 17 100% 16 94% 2022 Banchar Bhita 34 24 71% 23 96% 2023 Japur Bhita 11 11 100% 10 91% 2024 Satbhangamur 12 12 100% 10 83% 2025 Dalal Para 21 21 100% 19 90% 2026 Kanibari 30 25 83% 21 84% 2027 Lakhir Khamar 24 23 96% 21 91% 2028 Bonar Bhita 21 21 100% 20 95% 2029 Koykuri 22 19 86% 18 95% 2030 Bidasir Bhita 12 12 100% 12 100% 2031 Batuakhana 10 10 100% 10 100% 2032 Sardar para 7 7 100% 7 100% 2033 Joy Sarashwati 10 10 100% 8 80% 2034 Bhagir Bhita 11 11 100% 10 91% 2035 Daktar para 12 12 100% 11 92% 2036 Maydipur 13 13 100% 13 100% 2037 Kuranir Bhita 9 9 100% 9 100% 3001 Jakua Para 82 82 100% 78 95% 3002 Kadamtala 96 95 99% 88 93% 3003 Char Gobindapur 85 85 100% 77 91% 3004 Uttar Sitaijhar 67 67 100% 58 87% 3005 Dakshin Sitaijhar 73 73 100% 68 93% 3006 Arazi Kadamtala 52 52 100% 51 98% 3007 Jola Para 59 59 100% 53 90% 3008 Dakshin Noabash 23 23 100% 22 96% 3009 Nama Char 60 51 85% 45 88% 3010 Noyani 32 32 100% 25 78% 3011 Uttar Noabash 38 38 100% 36 95% 3012 Char Noabash 21 21 100% 19 90% 3013 Gobindapur (Kaim) 28 28 100% 25 89% 3014 Garuhara 30 30 100% 28 93%

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Table 1c. Bangladesh Sampling and Response Rates for Primary Sample Villageid Village Frame Count Sample Sampling Rate Respondents Response Rate Count 3015 Mandal Para 14 14 100% 11 79% 4001 Palashbari (Part) 174 174 100% 158 91% 4002 Railway Colony 70 70 100% 67 96% 4003 Sara 48 48 100% 41 85% 4004 Muktaram 63 53 84% 51 96% 4005 Dakshin Dhananjoy 40 40 100% 36 90% 4006 Hariram 28 28 100% 26 93% 4007 Wapda Colony 24 24 100% 22 92% 4008 Chila Para 38 38 100% 35 92% 4009 Bhogaram 30 30 100% 25 83% 4010 Uttar Dhananjoy 37 36 97% 26 72% 4011 Nilkantha 43 27 63% 27 100% 4012 Nagdaha Para 32 32 100% 25 78% 4013 Bashania Para 32 27 84% 25 93% 4014 Atmaram 44 27 61% 25 93% 4015 Gangadas 21 21 100% 18 86% 4016 Puragram 21 21 100% 18 86% 4017 Dhulaura 27 26 96% 20 77% 4018 Jukri Para 17 17 100% 14 82% 4019 Kawla Para 14 14 100% 13 93% 4020 Pathan Para 17 17 100% 14 82% Total 3009 2892 96% 2629 91%

Bangladesh Secondary Sampling and Response Rates by Village Table 2c. Bangladesh Sampling and Response Rates for Secondary Sample Villageid Village Frame Sample Sampling Respondents Response Count Count Rate Rate 1001 Harishwar Kalua 14 12 86% 10 83% 1002 Fakir Para 16 9 56% 7 78% 1003 Pramaniktari 16 6 38% 6 100% 1004 Cherenga 17 7 41% 7 100% 1005 Adhgram 9 5 56% 5 100% 1006 Majhi Para 5 5 100% 5 100% 1007 Bepari Para 4 4 100% 4 100% 1008 Bhabaneshwar 14 4 29% 4 100% 1009 Kamar Para 12 6 50% 5 83%

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Table 2c. Bangladesh Sampling and Response Rates for Secondary Sample Villageid Village Frame Sample Sampling Respondents Response Count Count Rate Rate 1010 Dasarhat 2 2 100% 2 100% 1011 Khan Para 6 5 83% 5 100% 1012 Bhanur Bhita 15 4 27% 4 100% 1013 Sarkar Para 7 3 43% 3 100% 1014 Bepari Para 10 5 50% 5 100% 1015 Khamar Para 4 3 75% 3 100% 1016 Ragati Para 5 3 60% 3 100% 1017 Khularpar . . . . . 1018 Dawpara 1 1 100% 1 100% 1019 Karji Para 6 2 33% 2 100% 1020 Kuya Para 2 2 100% 2 100% 1021 Ghosh Para 5 2 40% 2 100% 2001 Madhabram 55 19 35% 19 100% 2002 Nanda Dulalar Bhita 37 8 22% 8 100% 2003 Maudipur 36 12 33% 8 67% 2004 Bhogdanga 30 8 27% 7 88% 2005 Char Baraibari Namagram 48 8 17% 7 88% 2006 Kashi Char 11 6 55% 6 100% 2007 Kumarpur 16 7 44% 6 86% 2008 Paramali 15 7 47% 7 100% 2009 Shibuttar 24 5 21% 5 100% 2010 Gopalhalar Khamar 30 7 23% 7 100% 2011 Basur Bhita 27 6 22% 6 100% 2012 Gaurar Khama 27 5 19% 5 100% 2014 Char Baraibari Upargram 24 7 29% 7 100% 2016 Dungdungirhat 19 5 26% 5 100% 2017 Nagar Khamar 8 4 50% 4 100% 2018 Kaim Baraibari 14 5 36% 5 100% 2019 Umarer Bhita 21 7 33% 7 100% 2021 Shibuttar Moylar Bhita 4 4 100% 4 100% 2022 Banchar Bhita 10 5 50% 3 60% 2023 Japur Bhita 4 4 100% 4 100% 2024 Satbhangamur 15 4 27% 4 100% 2025 Dalal Para 4 3 75% 3 100% 2026 Kanibari 13 3 23% 3 100% 2027 Lakhir Khamar 7 5 71% 4 80%

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Table 2c. Bangladesh Sampling and Response Rates for Secondary Sample Villageid Village Frame Sample Sampling Respondents Response Count Count Rate Rate 2028 Bonar Bhita 4 3 75% 3 100% 2029 Koykuri 12 3 25% 3 100% 2030 Bidasir Bhita 7 4 57% 3 75% 2031 Batuakhana 3 3 100% 3 100% 2032 Sardar para 4 2 50% 2 100% 2033 Joy Sarashwati 9 2 22% 2 100% 2034 Bhagir Bhita 3 2 67% 2 100% 2035 Daktar para 6 6 100% 2 33% 2036 Maydipur 2 2 100% 2 100% 2037 Kuranir Bhita 3 2 67% 2 100% 3001 Jakua Para 38 16 42% 15 94% 3002 Kadamtala 54 14 26% 14 100% 3003 Char Gobindapur 78 13 17% 13 100% 3004 Uttar Sitaijhar 37 14 38% 13 93% 3005 Dakshin Sitaijhar 62 14 23% 11 79% 3006 Arazi Kadamtala 35 10 29% 10 100% 3007 Jola Para 19 10 53% 9 90% 3008 Dakshin Noabash 15 11 73% 10 91% 3009 Nama Char 33 8 24% 6 75% 3010 Noyani 13 8 62% 7 88% 3011 Uttar Noabash 19 9 47% 8 89% 3012 Char Noabash 22 7 32% 7 100% 3013 Gobindapur (Kaim) 34 11 32% 10 91% 3014 Garuhara 24 6 25% 6 100% 3015 Mandal Para 8 6 75% 5 83% 4001 Palashbari (Part) 55 29 53% 29 100% 4002 Railway Colony 15 13 87% 13 100% 4003 Sara 29 9 31% 7 78% 4004 Muktaram 9 8 89% 8 100% 4005 Dakshin Dhananjoy 17 8 47% 6 75% 4006 Hariram 6 6 100% 6 100% 4007 Wapda Colony 7 6 86% 6 100% 4008 Chila Para 5 5 100% 5 100% 4009 Bhogaram 10 8 80% 6 75% 4010 Uttar Dhananjoy 6 5 83% 5 100% 4011 Nilkantha 10 4 40% 4 100%

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Table 2c. Bangladesh Sampling and Response Rates for Secondary Sample Villageid Village Frame Sample Sampling Respondents Response Count Count Rate Rate 4012 Nagdaha Para 5 4 80% 4 100% 4013 Bashania Para 7 7 100% 4 57% 4014 Atmaram 8 4 50% 4 100% 4015 Gangadas 2 2 100% 2 100% 4016 Puragram 9 3 33% 3 100% 4017 Dhulaura 7 4 57% 3 75% 4018 Jukri Para 4 3 75% 3 100% 4019 Kawla Para 5 3 60% 3 100% 4020 Pathan Para 8 2 25% 2 100% Total 1437 563 39% 520 92%

Niger Primary Sampling and Response Rates by Village Table 3c. Niger Sampling and Response Rates for Primary Sample Villageid Village Frame Sample Sampling Respondents Response Count Count Rate Rate 1001 Zermou 96 96 100% 66 69% 1002 Garin Kouble . . . . . 1003 Angoual Magagi 15 15 100% 15 100% 1004 Chada Wanan 26 26 100% 20 77% 1005 Didiki 25 20 80% 18 90% 2001 Hamdara Laouali 109 31 28% 30 97% 2002 Saleri 37 24 65% 23 96% 2003 Kissamba Issoufou 34 22 65% 20 91% 2004 Zourou 21 21 100% 21 100% 2005 Sabon Gari 15 15 100% 14 93% 2006 Agama 8 8 100% 7 88% 2007 Kaoubouli 28 18 64% 18 100% 2008 Kaouboul Baka 21 21 100% 18 86% 2009 Galbi 20 20 100% 13 65% 3001 Gouna 213 102 48% 101 99% 3002 Guirari 131 104 79% 87 84% 3003 Bourbaram 120 72 60% 54 75% 3004 Karaye Haoussa 43 43 100% 39 91% 3005 Fouboumi 45 45 100% 38 84% 3006 Barago 32 32 100% 25 78% 3007 Kaouga 3 3 100% 2 67%

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Table 3c. Niger Sampling and Response Rates for Primary Sample Villageid Village Frame Sample Sampling Respondents Response Count Count Rate Rate 3008 Katsari Haoussa 30 30 100% 27 90% 3009 Droum Dan Ladi 27 27 100% 26 96% 3010 Rigalbaoure 34 34 100% 24 71% 3011 Garin Bounde 37 33 89% 23 70% 3012 Tchoukouloua Ii 24 24 100% 22 92% 3013 Gaouram Jagayi 15 15 100% 14 93% 3014 Dounkoulou 22 22 100% 20 91% 3015 Jan Mage 16 16 100% 15 94% 3016 Garmaki 22 22 100% 19 86% 3017 Kassari Bougage 17 17 100% 17 100% 3018 Angoual Kadi 18 18 100% 18 100% 3019 Gamou 13 13 100% 9 69% 4001 Dogon Manke 73 47 64% 43 91% 4002 Samkaka 55 40 73% 38 95% 4003 Kirchia 47 47 100% 37 79% 4004 Laoutey 60 53 88% 36 68% 4005 Gueza Mahaman 70 39 56% 33 85% 4006 Doumoumougue 76 40 53% 30 75% 4007 Kafa Safoua Katakura 31 31 100% 23 74% 4008 Badaraka 57 32 56% 25 78% 4009 Gaffati 82 40 49% 24 60% 4010 Kagna Tchikama 25 25 100% 24 96% 4011 Dankieni 35 24 69% 22 92% 4012 Natsalle Gambo 32 26 81% 18 69% 5001 Dogo Chaibou 238 183 77% 139 76% 5002 Lingui 69 57 83% 49 86% 5003 Dogo Maikassoua 80 61 76% 50 82% 5004 Gojo Gojo 159 81 51% 45 56% 5005 Djeda 54 52 96% 34 65% 5006 Rouwan Chaba 58 53 91% 26 49% 5007 Kalgo Maikassoua 48 38 79% 29 76% 5008 Angoual Alkali 27 27 100% 23 85% 5009 Garin Tamdji 30 30 100% 28 93% 5010 Zangon Nangarake 30 30 100% 15 50% 5011 Kaouri 88 32 36% 24 75% 5012 Kournawa Bougage 17 17 100% 14 82%

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Table 3c. Niger Sampling and Response Rates for Primary Sample Villageid Village Frame Sample Sampling Respondents Response Count Count Rate Rate 5013 Kalgo Tchama 26 25 96% 23 92% 5014 Koukoki 39 28 72% 22 79% 5015 Foulani Kounkoure 31 31 100% 20 65% 5016 Riga Djataou 34 28 82% 22 79% 5017 Janbirgi 10 10 100% 9 90% 5018 Kaouga 31 26 84% 21 81% 5019 Rigal Mantche 51 22 43% 21 95% 5020 Angoual Harou 56 25 45% 19 76% 5021 Rawayou 28 20 71% 20 100% 5022 Dan Kaoura 23 18 78% 18 100% 5023 Lalachi 76 19 25% 17 89% 5024 Tossomo I 19 19 100% 15 79% 5025 Zango Atta 28 27 96% 17 63% 5026 Inkamawa 18 17 94% 17 100% 5027 Chafawa I 19 19 100% 14 74% 5028 Gahorga 29 23 79% 16 70% 5029 Gada Garin Gjadi 19 19 100% 14 74% 5030 Helawa 46 16 35% 16 100% 5031 Raki Mani 20 18 90% 16 89% 5032 Zongon Mazawage 15 15 100% 12 80% 6001 Koleram 230 210 91% 167 80% 6002 Baoucheri 57 57 100% 37 65% 6003 Dineye 140 127 91% 64 50% 6004 Rigal Djerma 106 49 46% 38 78% 6005 Angoual Sountali/Koleram 76 63 83% 30 48% 6006 Garin Galadima/Diney Garin 18 18 100% 12 67% Galadima 6007 Gamdou 29 28 97% 27 96% 6008 Baoucheri 49 49 100% 45 92% 6009 Kanya Angoual Kourna/Kagna 24 24 100% 19 79% Angoual Makada Total 4205 3164 96% 3291 81%

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Niger Secondary Sampling and Response Rates by Village Table 4c. Niger Sampling and Response Rates for Secondary Sample Villageid Village Frame Sample Sampling Respondents Response Count Count Rate Rate 1001 Zermou 17 14 82% 14 100% 1002 Garin Kouble . . . . . 1003 Angoual Magagi 11 6 55% 6 100% 1004 Chada Wanan 9 9 100% 4 44% 1005 Didiki 14 4 29% 4 100% 2001 Hamdara Laouali 31 17 55% 6 35% 2002 Saleri 28 5 18% 5 100% 2003 Kissamba Issoufou 26 4 15% 4 100% 2004 Zourou 9 4 44% 4 100% 2005 Sabon Gari 13 4 31% 4 100% 2006 Agama 9 4 44% 2 50% 2007 Kaoubouli 15 4 27% 4 100% 2008 Kaouboul Baka 15 4 27% 4 100% 2009 Galbi 24 6 25% 3 50% 3001 Gouna 58 21 36% 21 100% 3002 Guirari 33 20 61% 19 95% 3003 Bourbaram 49 15 31% 11 73% 3004 Karaye Haoussa 27 12 44% 12 100% 3005 Fouboumi 24 9 38% 8 89% 3006 Barago 20 15 75% 7 47% 3007 Kaouga 5 5 100% 5 100% 3008 Katsari Haoussa 13 6 46% 6 100% 3009 Droum Dan Ladi 18 6 33% 6 100% 3010 Rigalbaoure 8 8 100% 6 75% 3011 Garin Bounde 31 5 16% 5 100% 3012 Tchoukouloua Ii 14 5 36% 5 100% 3013 Gaouram Jagayi 13 8 62% 5 63% 3014 Dounkoulou 7 4 57% 4 100% 3015 Jan Mage 19 4 21% 3 75% 3016 Garmaki 17 4 24% 4 100% 3017 Kassari Bougage 6 5 83% 4 80% 3018 Angoual Kadi 8 4 50% 4 100% 3019 Gamou 15 6 40% 4 67% 4001 Dogon Manke 79 10 13% 9 90%

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Table 4c. Niger Sampling and Response Rates for Secondary Sample Villageid Village Frame Sample Sampling Respondents Response Count Count Rate Rate 4002 Samkaka 30 12 40% 8 67% 4003 Kirchia 16 8 50% 8 100% 4004 Laoutey 31 9 29% 8 89% 4005 Gueza Mahaman 35 9 26% 7 78% 4006 Doumoumougue 36 11 31% 7 64% 4007 Kafa Safoua Katakura 14 5 36% 5 100% 4008 Badaraka 23 6 26% 5 83% 4009 Gaffati 23 14 61% 5 36% 4010 Kagna Tchikama 26 6 23% 5 83% 4011 Dankieni 19 4 21% 4 100% 4012 Natsalle Gambo 12 7 58% 4 57% 5001 Dogo Chaibou 74 38 51% 30 79% 5002 Lingui 55 15 27% 11 73% 5003 Dogo Maikassoua 27 13 48% 11 85% 5004 Gojo Gojo 87 12 14% 9 75% 5005 Djeda 14 7 50% 7 100% 5006 Rouwan Chaba 55 8 15% 6 75% 5007 Kalgo Maikassoua 15 11 73% 6 55% 5008 Angoual Alkali 14 6 43% 6 100% 5009 Garin Tamdji 20 8 40% 6 75% 5010 Zangon Nangarake 42 5 12% 4 80% 5011 Kaouri 71 6 8% 5 83% 5012 Kournawa Bougage 11 . . . . 5013 Kalgo Tchama 19 5 26% 5 100% 5014 Koukoki 44 6 14% 5 83% 5015 Foulani Kounkoure 43 7 16% 5 71% 5016 Riga Djataou 47 5 11% 5 100% 5017 Janbirgi 12 9 75% 6 67% 5018 Kaouga 7 7 100% 5 71% 5019 Rigal Mantche 20 5 25% 4 80% 5020 Angoual Harou 24 5 21% 4 80% 5021 Rawayou 21 4 19% 4 100% 5022 Dan Kaoura 36 4 11% 4 100% 5023 Lalachi 64 6 9% 5 83% 5024 Tossomo I 28 5 18% 4 80% 5025 Zango Atta 12 8 67% 4 50%

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Table 4c. Niger Sampling and Response Rates for Secondary Sample Villageid Village Frame Sample Sampling Respondents Response Count Count Rate Rate 5026 Inkamawa 26 4 15% 4 100% 5027 Chafawa I 21 4 19% 4 100% 5028 Gahorga 19 5 26% 4 80% 5029 Gada Garin Gjadi 16 3 19% 2 67% 5030 Helawa 15 3 20% 3 100% 5031 Raki Mani 31 3 10% 3 100% 5032 Zongon Mazawage 29 3 10% 3 100% 6001 Koleram 134 47 35% 36 77% 6002 Baoucheri 41 20 49% 17 85% 6003 Dineye 79 25 32% 14 56% 6004 Rigal Djerma 60 12 20% 9 75% 6005 Angoual Sountali/Koleram 47 15 32% 6 40% 6006 Garin Galadima/Diney 21 7 33% 6 86% Garin Galadima 6007 Gamdou 42 8 19% 6 75% 6008 Baoucheri 34 7 21% 6 86% 6009 Kanya Angoual 21 5 24% 4 80% Kourna/Kagna Angoual Makada Total 2448 724 30% 566 78%

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Appendix D: Preliminary Endline Analysis Plan This section outlines an early draft of the endline data analysis plan, subject to change depending on endline data availability, sample attrition, data quality, and so forth. At three-years after baseline data collection, field teams will attempt to recontact every baseline respondent within the primary eligible sample. Per the study protocol, GPS coordinates and mobile phone information has been collected to aid in locating these baseline participants.

Once data collection at endline is completed, this endline analysis plan will be updated to reflect changes in response to endline data collection parameters or challenges. Next, data cleaning will ensure that endline data are correctly linked to the baseline data, and all variables have been cleaned and prepared for analysis. Scale measures and indices will be created for use during analysis. Comprehensive weighting will then be performed to enable inferential analysis. After examination the endline data, the analysis will proceed as described below.

Outcome Measures for Endline Analysis

Principal Outcome The principal outcome for this study is timing or hazard of first birth conditional on marriage among subjects sampled from the target population. The principal research question, for which the study was powered, is as follows:

• Does the treatment group exhibit a 6-month greater delay of first birth after marriage than the control group?

Subjects who are not married at the baseline measurement occasion will only enter the intent to treat population for the primary outcome if they get married within the 3-year follow up period. At the follow-up measurement occasion, the timing of marriage measure in relation to the date of the baseline measurement occasion will be recorded for each subject. We will also record the timing of first birth from the date of marriage. This will allow us to construct an event history of marriage and first birth for each subject within the 3-year study windows. Subjects who never marry during the study window will not contribute data to the primary outcome. Subjects who are married but who don’t have a first birth before the end of the study window will be right censored. Marriage and birth rates from baseline to endline will inform the types of analyses that are required (particularly if there are challenges with low follow up response rates).

Provisional Key Outcomes (To be confirmed by CARE; subject to multiple comparison adjustment) • Modern contraceptive prevalence rate among adolescent girls age 15-19 • Percentage of adolescent girls age 15-19 who report discussing family planning with health worker or promoter in the past 6 months

Provisional Exploratory Outcomes (To be confirmed by CARE; not subject to multiple comparison adjustments) • Mean early pregnancy risk knowledge score • Self-efficacy to discuss and use family planning

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• Self-efficacy to refuse sex • Self-efficacy to go to health facility • Self-efficacy to engage in economic activities • Rosenburg Self-Esteem Scale

Analytic Steps and Methods The endline analysis will proceed in stages. In the first stage, we will clean the data, assess nonresponse or loss-to-follow up, ensure linkability, and construct measures and indices. During this stage we will develop the composite measures and check psychometric properties using exploratory factor analysis and related techniques. In the second stage, we may implement a matching/weighting method to attempt to approximate a cluster-randomized control trial. In the final stage, we will estimate statistical models to produce intent-to-treat estimates for our principal and additional outcomes. These stages are summarized below.

Measurement / Scale Development Internal consistency reliability for each psychometric scale will be assessed by computing point estimates and 95% confidence intervals (CIs) for coefficient-α. Multivariate normality will be assessed (e.g., Mardia kurtosis test, Henze-Zirkler and Fattorini test). In the presence of significant multivariate non-normality, a distribution-free technique for estimating confidence intervals will be applied. Previously validated scales will be calculated according to guidelines provided by CARE. Exploratory structural equation modeling (ESEM; Asparouhov & Muthén, 2009) or exploratory factor analysis (EFA) will be used to evaluate the factor structure of any new scales developed from survey variables.

Weighting Procedures for Endline Analysis

Variables Due to the purposive selection of coarsely-matched regions that encompass all villages in the treatment and comparison population, this study design represents a clustered, non-equivalent control trial of the intervention. Baseline analyses indicate that the treatment and control group areas are well balanced on observable parameters of interest. At endline, we will reassess the balance of respondent groups and the affect of attrition on the respondent sample. If needed, we will use propensity score related methods to weight the two samples on a list of observed village and subject-level characteristics measured at baseline. Doing so (under the condition of unbalanced groups) will help approximate the data that we would have obtained under a more robust study design – i.e. a cluster-randomized control trial.

To do so, we would balance on measures with a presumptive causal relationship to the primary outcome and/or a known (and salient) source of non-equivalence across the two study populations, including:

• Age • Marital Status • Educational Attainment • Religion • Wealth Index (e.g., ownership of agricultural land, livestock, household items and other assets; Alkire & Santos, 2014)

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Matching and Weighting Should it be necessary based on the data collected at endline, the sample from the comparison population will be weighted so that it is equivalent to the treated population on the multivariate distribution of the matching variables identified above. A propensity-score method will be utilized to produce an analytic dataset that approximates random sampling into treatment and control arms from a theoretical population closely resembling the baseline treatment group.

The propensity score is the probability of assignment to the treatment group conditional on a subject's measured baseline covariates (Rosenbaum & Rubin, 1983). A common and relatively straightforward implementation of propensity-score methods is inverse propensity-score weighting. In the presence of measured confounders, an unbiased estimate of treatment effect can be obtained by weighting by the inverse probability that individuals with a given set of covariates are assigned to treatment (Rosenbaum, 1987).

Adding some complexity to propensity methods, matching of the two samples might be conducted at both the village and the subject level. Village-level matching and weighting will be explored depending on the availability and reliability of village-level characteristics (and determined prior to endline analysis). Under this weighting scheme, villages from both samples would be sorted into a small number of strata formed from the cross-classification of a very limited number of salient contextual variables (e.g. village size, distance from major urban area, access to contraception/clinic, rurality, etc.). We will explore using coarse matching in which stratum probabilities will be used to construct village- level sampling weights such that the distribution of comparison villages across strata is identical to what is observed in the treatment villages. If practical to create village-level weights, subject-level weighting will then take place within each coarse village stratum, such that the comparison sample matches the treatment sample on the within-stratum multivariate distribution of the subject-level matching variables. Should within stratum sample size be too small to support within-strata estimation, we might pursue an iterative model fitting strategy with data pooled across strata.

However if the village-level weighting approach is not found to be practical given constraints of the data, we will fall back to the more typical strategy of matching the control to the treatment group within each country sample, using an appropriate propensity methodology at the individual-level (Iacus et al, 2012; D’Agostino, 1998; Yanovitzky et al, 2005). Under this scheme we might include a set of village characteristics during estimation of the individual-level propensity scores.

We will use balance statistics to evaluate the adequacy of the specification of the propensity score model. Given the large number of matching variables and relatively small sample size, this process is likely to be iterative and involve trimming of extreme weights (Crump et al, 2009; Lee et al., 2011).

Subject-level sampling weights that were developed to adjust for the non-equivalent sampling probabilities can also be easily combined with the propensity score based matching weights. The procedure will be to first match the unweighted treatment control samples (i.e. exclude sampling weights in the estimation of the propensity score) and then combine the derived propensity score weights with the sampling weights that were constructed for treatment sample. Sampling weights for the comparison sample will not be constructed.

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Analysis of Principal Outcome The analysis of the principal outcome (time to first birth after marriage) and the estimation of the intent to treat effects of the intervention will be estimated using survival analysis. Two modeling strategies will be evaluated: (1) the Cox proportional hazards model, and (2) a discrete-time hazard model. The choice of the model will ultimately be determined by the data. The Cox proportional hazards model treats time as continuous whereas the discrete-time hazard model treats time as equally spaced time intervals. Ideally, time would be recorded to the exact date. However, we anticipate the possibility that some dates will not be precisely known and are instead defined as the interval between two dates. If exact dates are known for all or most of the sample, continuous time models can be fit; if not, continuous times can be treated as discrete-time events (e.g., the event occurred between the first and last day of the month). While discrete-time modeling would appear to accommodate a lack of precision in dates, discrete-time models assume a uniform time interval, which would correspond to the lowest temporal resolution in the data. Thus, ultimately, the determination of the treatment of time will have to be evaluated with regard to the amount of dates that are not precisely known and when the dates are not precisely known, the intervals will have to be evaluated with regard to the loss of temporal precision. If neither of the prior steps sufficiently addresses the treatment of time, problematic cases will be treated as missing and available time data will be used to impute time and models will be fit using multiple imputation techniques.

Regardless of specific modeling strategy, we plan to estimate a mixed-effects (or multi-level) logistic regression with a village-level random intercept in order to properly estimate the contextual-level treatment effect. Each of these procedures allow for weighting at multiple levels of analyses. Flexibility is important should we have sample weights at both the village and subject level. The use of sample weights in multi-level models requires the scaling of sample weights in order to properly estimate variance components. This is an active area of research in the applied statistic literature and we will consult this literature before submitting a final endline analysis plan.

Analysis of Related and Exploratory Outcomes The method for analyzing the additional outcomes of interest will be identical to the above except in a couple of important respects. First, none of the secondary outcomes involve time to event data. Thus, the model specification (e.g. choice of link function) will be changed to reflect measurement scale of each outcome (e.g. continuous, binary, ordinal, etc.). Second, for each subject we will have measures of the secondary outcomes from the baseline and post-intervention measurement occasions. This allows for difference-in-difference (DID) estimation of the intent-to-treat effect. By identifying the treatment effect as a group difference in the average change observed in the outcome across the pre- and post- measurement occasions, DID estimations controls for unobserved and temporally stable sources of non- equivalence not captured by our matching/weighting procedures. The addition of another subject-level observation to the analysis requires adding a subject-level random effect to the models that already include a village-level random effect. As described above, the sample weights at the village and subject level will be also be included.

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