Cash flowing to children

Cash Transfers: Are They Really Helping? The Truth About Poverty Alleviation

Mira Elwood in Politics & Policy September 2025 • 4 min read.

"Dive deep into the hidden complexities of cash transfer programs: what works, what doesn't, and what's REALLY making a difference for families in need."

Conditional Cash Transfer (CCT) programs have emerged as a popular strategy to combat poverty across middle-income countries. These initiatives aim to provide immediate relief through monetary aid, incentivizing participation in activities like school attendance and healthcare check-ups.

But how effective are these programs in truly making a difference? While the intention is to address immediate needs and encourage virtuous behaviors for long-term change, the practical impact involves several complexities and debates. Are the costs of enforcing these conditions worthwhile? Who actually benefits, and who might be left behind?

A study in Morocco, known as the Tayssir program, sought to understand these dynamics. This innovative field experiment compared a standard CCT model, where cash transfers are dependent on school attendance, against a Labelled Cash Transfer (LCT) approach. LCTs offer cash payments labelled to improve school attendance, mandated by local school principals but without strict enforcement. In this model, the expectation is that some benefits of CCTs can be achieved without additional costs in time, money, and autonomy. But what does this tell us about how aid can be most effectively distributed?

Unpacking the Reality: Who Really Benefits from Cash Transfers?

The original evaluation of the Tayssir program highlighted that any form of treatment, whether conditional or labelled, significantly improved school attendance. However, the effects on academic achievement, particularly in math test scores, require a closer look. The key question is not just whether these programs work, but for whom do they work best? Addressing drivers of heterogeneity and understanding which factors influence treatment effects is crucial.

Causal machine learning methods offer the potential to identify subgroups that experience differential benefits from cash transfer programs. In the context of the Tayssir program, researchers explored how various factors, such as prior education levels, poverty measures, and household dynamics, shaped the impact of cash transfers on math test scores. By analyzing a dataset of 1936 pre-treatment variables, they were able to uncover hidden complexities of the cash transfer initiative.

Baseline Education: Students with better prior education participation often see more significant gains.
Poverty Levels: Those with greater existing disadvantage may benefit less, or even experience negative impacts, from conditional programs.
Program Understanding: Misunderstanding of a program's conditions can affect the effectiveness of cash transfers.

This detailed level of insight shows the challenges of high-dimensional analysis and interpreting atheoretical results, leading to the proposal of a novel causal tree method for more interpretable modelling of causal effects. This method, known as the Distilled Doubly Robust Causal Trees, creates the clusters of the heterogeneity for better treatment. This method highlights that the effectiveness of cash transfer programs depends significantly on the baseline characteristics of the recipients. This is critical for policy design, since the program can be best fitted for it's audiences.

Rethinking Cash Transfers: A Path Forward

To maximize the benefits of cash transfer programs, understanding and addressing heterogeneity are critical. Policy must be designed to consider varied factors: prior education, poverty levels, household dynamics, and how well the program is understood. Flexible methods like causal machine learning, supported by qualitative research, offer great potential for uncovering the impact for specific communities.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: https://doi.org/10.48550/arXiv.2401.07075,

Title: Heterogeneous Treatment Effect Estimation With High-Dimensional Data In Public Policy Evaluation -- An Application To The Conditioning Of Cash Transfers In Morocco Using Causal Machine Learning

Subject: econ.gn q-fin.ec

Authors: Patrick Rehill, Nicholas Biddle

Published: 13-01-2024

Everything You Need To Know

What are Conditional Cash Transfer (CCT) programs and what is their primary goal?

Conditional Cash Transfer (CCT) programs are initiatives designed to alleviate poverty, primarily in middle-income countries. They involve providing monetary aid to individuals or families, contingent upon their participation in specific activities such as ensuring school attendance or undergoing healthcare check-ups. The main aim is to provide immediate financial relief while incentivizing behaviors that are expected to lead to long-term improvements in well-being. However, their effectiveness is subject to debate, considering enforcement costs and varied impacts.

How does the Labelled Cash Transfer (LCT) approach differ from the traditional Conditional Cash Transfer (CCT) model, as seen in the Tayssir program?

The Labelled Cash Transfer (LCT) approach, exemplified by the Tayssir program, distinguishes itself from the traditional Conditional Cash Transfer (CCT) model by reducing the strictness of enforcement. While LCTs also provide cash payments intended to improve school attendance, these are 'labelled' for this purpose, often encouraged by local school principals, but without the rigorous enforcement mechanisms typical of CCT programs. The intention is to achieve some of the benefits of CCTs while reducing costs related to time, money, and oversight, offering a potentially more streamlined approach to aid distribution. The success of the Tayssir program highlighted the need to further investigate the effects on academic achievement.

What role do causal machine learning methods play in understanding the effectiveness of cash transfer programs?

Causal machine learning methods are instrumental in identifying subgroups that experience differential benefits from cash transfer programs. In the context of the Tayssir program, these methods helped researchers analyze a dataset of 1936 pre-treatment variables to uncover how factors like prior education levels, poverty measures, and household dynamics influenced the impact of cash transfers on math test scores. By revealing these nuanced effects, causal machine learning offers the potential to tailor programs to specific recipient characteristics, enhancing their overall effectiveness. The use of Distilled Doubly Robust Causal Trees improves the interpretability of the model.

According to the research, how do baseline education levels and existing poverty levels affect the impact of cash transfer programs like the Tayssir program?

Research indicates that baseline education levels and existing poverty levels significantly modulate the impact of cash transfer programs. Students with better prior education participation often experience more substantial gains from such programs. Conversely, individuals facing greater existing disadvantage or higher poverty levels may benefit less, or even experience negative impacts, from conditional programs. These findings underscore the necessity of considering these baseline characteristics when designing and implementing cash transfer programs to ensure equitable and effective outcomes. It is important to consider the impacts on subgroups of the population.

What steps can be taken to improve the design and implementation of cash transfer programs to maximize their benefits?

To maximize the benefits of cash transfer programs, it is crucial to understand and address heterogeneity among recipients. Policy design should consider varied factors such as prior education, poverty levels, household dynamics, and the degree to which the program is understood by participants. Flexible methods like causal machine learning, combined with qualitative research, can help uncover the specific impacts on different communities. Additionally, refining communication strategies to ensure a clear understanding of program conditions is essential. By tailoring programs to the specific needs and characteristics of diverse populations, cash transfers can become more effective tools for poverty alleviation.