# Exploring the heterogeneous impacts of Indonesia’s conditional cash transfer scheme (PKH) on maternal health care utilisation using instrumental causal forests

## TL;DR

This study uses a new machine learning method to show how Indonesia's cash transfer program affects maternal health care differently depending on local resources and other factors.

## Contribution

The novel use of instrumental causal forests reveals treatment effect heterogeneity in a conditional cash transfer scheme.

## Key findings

- Mothers in areas with more healthcare workers benefited more from the program in terms of good assisted delivery.
- The impact on post-natal visits in 2013 showed the largest heterogeneity among outcomes.
- Household poverty and survey wave were significant factors in treatment effect differences.

## Abstract

This paper uses instrumental causal forests, a novel machine learning method, to explore the treatment effect heterogeneity of Indonesia’s conditional cash transfer scheme on maternal health care utilisation. Using randomised programme assignment as an instrument for enrollment in the scheme, we estimate conditional local average treatment effects for four key outcomes: good assisted delivery, delivery in a health care facility, pre-natal visits, and post-natal visits. We find significant treatment effect heterogeneity by supply-side characteristics, even though supply-side readiness was taken into account during programme development. Mothers in areas with more doctors, nurses, and delivery assistants were more likely to benefit from the programme, in terms of increased rates of good assisted delivery outcome. We also find large differences in benefits according to indicators of household poverty and survey wave, reflecting the possible impact of changes in programme design in its later years. The impact on post-natal visits in 2013 displayed the largest heterogeneity among all outcomes, with some women less likely to attend post-natal check-ups after receiving the cash transfer in the long term.