Ferre 2014.

Study characteristics
Methods	Study design: PCS Study grouping: N/A How were missing data handled? exclusions: the high attrition rate in Narayanganj, while not unusual given the high frequency of in and out migration among the residents of urban slums, was clearly a challenge for any social programme targeted towards the urban slum population. Due to this high attrition rate in Narayanganj, the impact evaluation was restricted to the Jaldhaka sample. Randomisation ratio: N/A Recruitment method: project: following a public information campaign on the project objectives and duration, the targeting and enrolment processes were carried out. Shombhob set up an open registration process where interested HHs with ≥ 1 child aged 0–36 months or at ≥ 1 primary school‐aged child (or both) were invited to apply for selection. Out of the 37,801 families who applied, the poorest 15,952 families were selected based on their PMT scores. The list of eligible beneficiaries was validated by community leaders, and verified by UP chairmen, and the Mayor's office in case of Narayanganj City Corporation. Sample size justification and outcome used: NR, but eligible families were requested to enrol, and this process was completed in April 2012. The final number of enrolled HHs was 14,125. Sampling method: random: 1. random sample of 3000 HHs drawn from the census list (all HHs in the 5 project unions of Jaldhaka) and interviewed. 2. HHs were randomly selected within each of the 4 demographic groups and within each of 2 PMT score groups (below 25th percentile and between 25th and 50th percentiles). (Note: HHs were assigned to the treatment group by a non‐random assignment rule based on the assignment variable. The eligibility for becoming beneficiaries of a programme was solely determined by whether they were below or above the unique cut‐off point.) Study aim or objective: 1. to test the delivery of CCTs to the poorest HHs through local governments to reduce their HH poverty levels; 2. increase school attendance of beneficiary children going to primary school, and 3. improve the nutritional status of beneficiary children aged 0–36 months. Study period: baseline (survey conducted): May/June 2011. Implementation: April 2012–December 2013 (although transfers only provided for 13 months). Follow‐up: May/June 2013. Unit of allocation or exposure: cluster: HHs
Participants	Baseline characteristics Intervention or exposure Age: NR Place of residence: NR Sex: NR Ethnicity and language: NR Occupation: NR Education: table 4: school enrolment (aged 6–15 years), %: 88.7; school attendance, mean, years: 5.4 SES: table 4: based on total HH consumption: BDT 5548/month Social capital: NR Nutritional status: table 4: stunting (aged 0–3 years), %: 47.2; underweight, %: 47.1; wasting, %: 27.8; dietary diversity, %: 12.1 Morbidities: NR Concomitant or previous care: NR Control Age: NR Place of residence: NR Sex: NR Ethnicity and language: NR Occupation: NR Education: table 4: school enrolment (aged 6–15 years), %: 87.3%; school attendance, mean, years: 5.8 SES: table 4: based on total HH consumption: BDT 5780/month Social capital: NR Nutritional status: table 4: stunting (aged 0–3 years), %: 43.3; underweight, %: 42.9; wasting, %: 22.9; dietary diversity, %: 12.5 Morbidities: NR Concomitant or previous care: NR Overall Age: table B2: HH head, years: 40.5 Place of residence: NR Sex: NR Ethnicity and language: NR Occupation: table B2: agri Labourer, 44.5 Education: table B2: proportion of HH head with no education, %: 74.6 SES: table B2: total monthly consumption BDT 6066/month (?), proportion of HHs with bamboo wall, %: 48.8; proportion of HHs with tin roof, %: 97.7; proportion of HHs with pit latrines, %: 44; proportion of HH with access to electricity, %: 8.4; proportion of HH who own house, %: 80.5. Social capital: NR Nutritional status: NR Morbidities: NR Concomitant or previous care: NR Inclusion criteria: project used PMT scores to determine HH eligibility. Of the 37,801 families who applied for the programme, the poorest 15,952 were selected based on their PMT scores. This meant the cut‐off thresholds for selection was a PMT score of 660 for the 2 rural Upazilas. Eligible families had scores below the treatment cut‐off (treatment group) and ineligible families with had scores above the cut‐off (control group). Exclusion criteria: PMT score above the cut‐off. Pretreatment: most of the differences between the treatment and control HHs were nutrition outcomes. Treatment HHs appeared to be worse off compared to the control HHs in the incidence of stunting, wasting, underweight, knowledge of breastfeeding and dietary diversity. The same is true in terms of HH consumption. However, school attendance (defined as the number of classes missed in the last 2 weeks) and enrolment rates were almost identical. The DiD estimator assumes that the mean change in the control group represents the counterfactual change in the treatment group if there was no treatment. This allows a reliable inference of programme impact by comparing the pre‐ to postintervention change in the outcome of interest for the treated group relative to a control group. Attrition per relevant group: Jaldhaka (rural): 114 Description of subgroups measured and reported: NR Total number completed and analysed per relevant group: Jaldhaka (rural) analysed only. Total 2287; treatment: 700; control: 1587 Total number enrolled per relevant group: 2401: 700 treatment and 1587 control (and 114 that were lost during follow‐up – unclear to which group these belonged). Total number randomised per relevant group: N/A
Interventions	Intervention characteristics Intervention or exposure Food access intervention category: improve buying power Intervention type: CCT Description: table 2: HHs with children aged 0–36 months: nutrition allowance of BDT 400/month. Condition: monthly attendance at growth monitoring of children aged 0–36 months, and nutrition session for mother/carer. HHs with children going to primary school (aged 6–15 years): education allowance of BDT 400/month. Condition: regular school attendance (≥ 80% every month) for enrolled children in primary school. Duration of intervention period: due to start up delays, implementation began in February 2012, allowing transfers to be provided for 21 months (from April 2012 to December 2013). In addition, the project faced additional implementation challenges due to the annual floods that hit the project locations during August and September. School closures due to these floods constituted the most serious implementation challenge given focus of the transfers on regular school attendance. These implementation challenges coupled with the fact that the impact evaluation only covered 13 months of cash transfers were likely to affect the impact of the intervention on some indicators. Frequency: bi‐monthly cash transfers Number of study contacts: 2 Providers: local governments Delivery: innovative electronic payments system developed with the BPO, which provided cash cards to beneficiary mothers to make transfers electronically to their accounts with the Post Office. Payments were made using point‐of‐sales machines with the option of accessing the cash either at the village level using a mobile payments team or at the Upazila level BPO branch office. The BPO arranged for a mobile team to travel with the machines and cash to the village centres on a designated day during each payment cycle. Alternatively, the beneficiary had the option to withdraw cash at any point from the Upazila BPO branch office. Co‐interventions: NR Resource requirements: mobile team BPO; point‐of‐sales machines Economic indicators: Shombhob was able to deliver USD 1.78 million in cash transfers to 14,125 families and monitoring growth and school attendance of 22,778 children. The administrative cost associated with the delivery of the services under the pilot intervention, including the development of an automated MIS was about USD 641,000. This meant that the cost to deliver the transfer was USD 1.4 per child per month, or about 28% of the monthly transfer amount of USD 5. Control: no intervention
Outcomes	Proportion of HH expenditure on food Diet diversity: proportion of children aged ≥ 6 months fed from ≥ 4 food groups Anthropometry: stunting (HAZ < –2SD); wasting (WHZ < –2SD); underweight (WAZ < –2SD)
Identification	Sponsorship source: South Asia Food and Nutrition Security Initiative (SAFANSI) and the Rapid Social Response (RSR) MDTF of the World Bank Country: Bangladesh Setting: rural only. Due to the high attrition rate in Narayanganj, the impact evaluation was restricted to the Jaldhaka sample. 10 Unions from 2 rural Upazilas (Jaldhaka and Hatibandha). Authors' names: Céline Ferré and Iffath Sharif Email: isharif@worldbank.org Declarations of interest: quote: "The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organisations, or those of the Executive Directors of the World Bank or the governments they represent." Study or programme name and acronym: Shombhob project Type of record: report
Notes
Risk of bias
Bias	Authors' judgement	Support for judgement
Random sequence generation (Selection bias)	High risk	Quote: "One way to deal with possible selection bias is to use a Regression Discontinuity Design (RDD) technique that exploits the targeting design itself. RDD is a quasi‐experimental design and makes use of discontinuities generated by program eligibility criteria such that program assignment is based on a cut‐off point of some assignment variable. Households are assigned to the treatment group by a non‐random assignment rule based on the assignment variable. The eligibility for becoming beneficiaries of a program is solely determined by whether they are below or above the unique value of a cut‐off point."
Allocation concealment (Selection bias)	High risk	Prospective cohort study
Baseline characteristics similar (Selection bias)	Low risk	Table B3 in Annexe II provides descriptive statistics of other HH level characteristics of both groups. The data suggest the groups are quite similar in terms of their observable characteristics, but treatment HHs are slightly bigger despite having the same number of young children (aged 0–3 years). Asset ownership is similar across the 2 groups (land, cattle, tube well, fan, television, bicycle, number of rooms) except for house ownership (slightly higher for the control group). Control houses have fewer children on average (0.74 vs 1.27 for treatment families), leading to slightly smaller HHs (4.5 members on average vs 5 for treatment families).
Baseline outcome measurements similar (Selection bias)	Low risk	Treatment HHs appeared worse off compared to control HHs in incidence of stunting, wasting, underweight, knowledge of breastfeeding, dietary diversity and HH consumption. However, this seemed to be addressed in the analysis. Quote: "Instead of computing this double difference in means, we run a set of DiD regressions, allowing controlling for differences in observable characteristics."
Blinding of participants and personnel (Performance bias)	Low risk	Participants knew if they received a cash transfer or not. It is unlikely that lack of blinding influenced intervention received.
Blinding of outcome assessment (Detection bias)	High risk	Unclear if the interviewers were blinded. School enrolment, attendance, nutritional status (stunting, wasting, underweight), semi‐solid food intake and MDD are all objective outcomes. However, knowledge on infant feeding and consumption outcomes were self‐reported outcomes.
Protection against contamination (Performance bias)	High risk	If not enrolled for a cash transfer, a HH would not be able to receive it. There may be other bias. Quote: "The results on knowledge however are not able to take into account potential 'spillover effects' since nutrition sessions were delivered via classes held out in the open. In some villages, the growth monitoring was also conducted in courtyards. This modality of conducting the nutrition and growth monitoring sessions allowed non‐beneficiary mothers to have access nutrition‐related knowledge that this analysis is not able to capture." (page 33)
Incomplete outcome data (Attrition bias)	Unclear risk	Unclear how the 114 attrition cases were distributed over treatment and control. It is also unclear if the numbers that do not have a certain outcome (which could be because the outcome was not relevant or missing) were distributed evenly over the groups.
Selective outcome reporting (Reporting bias)	Unclear risk	No protocol available.
Other bias	Low risk	None identified.