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Description
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This public data release contains de-identified research datasets and documentation for the project “Improving Childcare Quality through Social Franchising” (IZA/FCDO G²LM|LIC Project GA-6-857). The study is a cluster-randomised controlled trial conducted across 51 urban informal settlements in Kenya, implemented with Kidogo, a social enterprise that provides training and support to home-based childcare providers. The study evaluates whether a social franchising model can improve childcare quality, firm survival, and market structure, and the downstream effects on households, maternal labour supply, and child development.
All childcare firms in the 51 settlements were first mapped and enumerated through a full census. A baseline survey was conducted with 978 firms. Communities were then randomly assigned: 26 treatment communities in which Kidogo entered and offered its programme, and 25 control communities in which Kidogo did not enter. Prior to programme entry, a half-day workshop was deployed in all communities; approximately 60% of daycares in both arms attended. Workshop attendance is highly correlated with programme take-up in treatment areas (82.5% of attendees ultimately enrolled). This design allows identification of direct programme effects among likely takers (workshop attendees) and spillover effects among competitor firms (non-attendees).
The Kidogo programme consists of a standardised, multi-month training and mentorship programme, a one-time capital improvement grant of approximately USD 200, monthly deliveries of fortified porridge for enrolled children, branding rights (the Kidogo logo), and regular quality-standard checks. Participating providers pay a small monthly membership fee. The model is designed to be scalable and cost-effective.
Data collection includes: (i) a listing/census of 1,663 childcare providers across all settlements; (ii) a firm baseline and midline survey of 978 providers, structured in wide format with wave suffixes (_b for baseline, _m for midline), covering childcare quality, operations, prices, enrolment, revenues, provider well-being, and competitive environment; and (iii) a representative household baseline survey of 2,820 households with children under 6, covering childcare demand and use, labour force participation, and child health and development. HTML codebooks are provided for all three datasets.
Key midline results (one year post-programme entry) show that social franchising increased overall childcare quality by approximately 0.20 standard deviations, driven by improvements in hygiene and safety (0.28 s.d.), availability of toys and manipulatives (0.19 s.d.), and quality of child experiences (0.10 s.d.). The probability of firm closure fell significantly, with a 12.8-percentage-point reduction in exit among home-based providers (a 34% reduction relative to control). Spillover benefits were also found among non-participating competitor firms. Prices, enrolment, and revenues did not change significantly in the short run.
The release includes Stata .dta datasets with variable and value labels, HTML codebooks for all instruments, and a README describing file structure and merge keys. Direct personal identifiers have been removed; users must not attempt re-identification.
The release includes Stata .dta datasets with variable and value labels, HTML codebooks for all instruments, and a README describing file structure and merge keys. Direct personal identifiers have been removed; users must not attempt re-identification. (2026-02)
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Keyword
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childcare quality, social franchising, informal settlements, randomised controlled trial, early childhood education, female entrepreneurship, food provision, child development, maternal labour supply, gender equality |
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Notes
| Intervention: Kidogo social franchising — ~3-month training and mentoring programme, $200 one-time capital grant, quality-standard checks, branding (Kidogo logo), and monthly fortified-porridge delivery. Providers pay a small monthly membership fee. Workshop attendance used as proxy for likely take-up to estimate spillover effects on competitor firms using machine-learning-based classification. |