Back

How much are households willing to invest in hand hygiene enabling technologies? A randomised pricing experiment in Lusaka, Zambia.

Davies, K.; Mwila-Kazimbaya, K.; Ross, I.; Tadiri, E.; Chipungu, J.; Dreibelbis, R.

2025-10-28 public and global health
10.1101/2025.10.23.25338632 medRxiv
Show abstract

Access to a dedicated handwashing facility (HWF) increases handwashing with soap (HWWS), an effective behaviour for preventing respiratory and diarrheal disease. Achieving universal hand hygiene will require household investment of a HWF, yet the ability and willingness of end-users to meet these costs has received little attention in stated or revealed preference studies. This two-phase voucher-based randomised pricing experiment explored whether households in peri-urban Lusaka, Zambia were willing to invest in a HWF. In Phase 1, three HWFs were tested with 60 households using two of the three HWFs for two weeks (20 households per combination). There was strong preference for a locally manufactured bucket with a tap and metal stand (Kalingalinga bucket; 250 ZMW, [~]US$10), over high-cost (1000 ZMW, [~]US$38) and low-cost (100 ZMW, [~]US$4) pre-manufactured HWFs. In Phase 2, n=160 used the Kalingalinga bucket for two weeks. At the end of the trial period, participants received a 50 ZMW gift ([~]US$2) and a randomly assigned discount voucher (20%, 40%, 60%, 80%; 40 households per group), before deciding whether to purchase the HWF. Ninety-eight percent (39/40) of households offered an 80% discount purchased the HWF (effective price: 0 ZMW), compared with 30% (12/40) offered a 20% discount (effective price: 150 ZMW). Predictive modelling estimated that 50% of households would purchase at an effective price of 103 ZMW ([~]40% of the retail price). Despite declining the purchase offer, all non-purchasing households reported positive stated willingness-to-pay, suggesting latent demand. Findings highlight the need for financial interventions that make desirable HWFs affordable.

Published in PLOS Water (predicted rank #9) · training set

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.