Cash transfers: cost-effectiveness analysis
Updates
November 2024: Update to our analysis
We have made a substantial update to our psychotherapy report. This 83 page report (+ appendix) details new methods as well as an updated cost-effectiveness analysis of StrongMinds and Friendship Bench. We now estimate StrongMinds to create 40 WELLBYs per $1,000 donated (or 5.3 times more cost-effective than GiveDirectly cash transfers). We now estimate Friendship Bench to create 49 WELLBYs per $1,000 donated (or 6.4 times more cost-effective than GiveDirectly cash transfers).
April 2022: Update to our analysis
This version of the report excluded the intra-household spillovers of cash transfers. Since then, we have updated our analysis to include household spillovers and this decreases the relative cost-effectiveness of psychotherapy from 12x to 9x cash transfers. Our household analysis is based on a few studies, eight for cash transfers and three for psychotherapy. The lack of data on household effects seems like a gap in the literature that should be addressed by further research.
Executive Summary
This report explains how we determined the cost-effectiveness of cash transfers (CTs) using subjective well-being (SWB) and affective mental health (MHa). We define subjective well-being as how someone feels or thinks about their life broadlyWe further describe what we mean by subjective well-being, and why we believe they are the best measures of well-being, here.. We use “affective mental health” to refer to the class of mental health disorders and measures that relate to lower levels of affect or mood (depression and anxiety)This differs slightly from the way the term is used clinically to refer to depression, bipolar, seasonal affective disorder, and not anxiety. However, we view this difference as less relevant in practice given that the studies in our sample primarily use measures of depression (CESD10 or GDS-15) or general psychological distress (GHQ12)..
Cash transfers (CTs) are direct payments made to people living in poverty. They have been extensively studied and implemented in low- and middle-income countries (LMICs) and offer a simple and scalable way to reach people suffering from extreme financial hardship (Vivalt, 2020).
Specifically, we analyse the cost-effectiveness of sending $1000 using monthly CTs and GiveDirectly CTs (which are typically paid all at once) deployed in LMICs. We fix the total value of money that a recipient receives at $1000, because this is the value of GiveDirectly’s present CTs. These cost-effectiveness analyses form the benchmarks that we use to compare interventions.Using cash transfers as a benchmark follows the precedent of GiveWell, a charity evaluator that did pioneering work comparing the cost-effectiveness of interventions in terms of moral value. Recent academic work has also used cash transfers as a benchmark. See McIntosh & Zeitlin (2020) that compared CTs to a USAID workforce readiness program or Haushofer et al., (2020) and Blattman et al., (2017) which compared CTs to psychotherapy.
This report is part of HLI’s search for the most cost-effective interventions that donors and policymakers could fund to increase happiness. We are currently focused on studying micro-interventions in LMICs. To find out more about the wider project see section 2.3 of our research agenda.
This analysis builds on a meta-analysis by McGuire, Kaiser, and Bach-Mortensen (2020), a collaboration between HLI researchers and academics. The meta-analysis gives more background on cash transfers and a much more thorough description of the evidence base. In this cost-effectiveness analysis (CEA) we use the same data to estimate the effectiveness of cash transfers. Specifically we expand on Appendix C of the meta-analysis, which presents a method for estimating the total benefit we expect a recipient of a cash transfer to accrue. We extend that section by predicting the effectiveness of monthly and lump-CTs and adding cost information to estimate the cost-effectiveness of CTs.
We estimate that GiveDirectly transferring $1000 in a lump-sum to people in LMICs leads to an increase in SWB and MHa of 0.92 SDs-years. Monthly CTs delivered by governments increase SWB and MHa by 0.40 SDs per $1000 spent.