RURAL WATER SOURCE CHOICE: A CHOICE EXPERIMENT FROM MERU, KENYA

A stated preference choice experiment is used to investigate factors important to rural households when selecting a primary water source. In particular, the guiding research questions are: (1) how do rural Kenyans trade-off time and price when selecting a water source? (2) how do rural Kenyans value time spent collecting water? (3) how are household characteristics relevant to water source choice? The choice experiment was administered to 388 respondents in rural Kenya. Source price and collection time are important to respondents without an at-home source. Neither income nor education is found to affect sensitivity to source price. Valuation of time is estimated to be 37% of the local unskilled wage rate on average. This choice experiment illustrates a relatively simple method of identifying water source preferences of households that can be used in other locations, however there are challenges in collecting high quality preference data and analyzing the data.Master of Scienc

Marketing Household Water Treatment: Willingness to Pay Results from an Experiment in Rural Kenya

Despite increasing availability of household water treatment products, demand in developing countries remains low. Willingness to pay for water treatment products and factors that affect demand are not well understood. In this study, we estimate willingness to pay for WaterGuard, a dilute chlorine solution for point-of-use water treatment, using actual purchase decisions at randomly assigned prices. Secondly, we identify household characteristics that are correlated with the purchase decision. Among a sample of 854 respondents from 107 villages in rural Kenya, we find that mean willingness to pay is approximately 80% of the market price. Although only 35% of sample households purchased WaterGuard at the market price, 67% of those offered a 50% discount purchased the product. A marketing message emphasizing child health did not have a significant effect on purchase behavior, overall or among the subset of households with children under five. These findings suggest that rural Kenyans are willing to pay for WaterGuard at low prices but are very sensitive to increasing price. Households with young children that could benefit the most from use of WaterGuard do not appear to be more likely to purchase the product, and a marketing message designed to target this population was ineffective

Video Surveillance Captures Student Hand Hygiene Behavior, Reactivity to Observation, and Peer Influence in Kenyan Primary Schools

This study explored the impact of using video surveillance to monitor hand hygiene behavior, and explore the role of peer influence on student hand cleaning behavior

Willingness to pay results from an experiment in rural Kenya

Non-PRIFPRI3; ISIMTI

Marketing Household Water Treatment: Willingness to Pay Results from an Experiment in Rural Kenya

Despite increasing availability of household water treatment products, demand in developing countries remains low. Willingness to pay for water treatment products and factors that affect demand are not well understood. In this study, we estimate willingness to pay for WaterGuard, a dilute chlorine solution for point-of-use water treatment, using actual purchase decisions at randomly assigned prices. Secondly, we identify household characteristics that are correlated with the purchase decision. Among a sample of 854 respondents from 107 villages in rural Kenya, we find that mean willingness to pay is approximately 80% of the market price. Although only 35% of sample households purchased WaterGuard at the market price, 67% of those offered a 50% discount purchased the product. A marketing message emphasizing child health did not have a significant effect on purchase behavior, overall or among the subset of households with children under five. These findings suggest that rural Kenyans are willing to pay for WaterGuard at low prices but are very sensitive to increasing price. Households with young children that could benefit the most from use of WaterGuard do not appear to be more likely to purchase the product, and a marketing message designed to target this population was ineffective

Statistical attribution of the influence of urban and tree cover change on streamflow: a comparison of large sample statistical approaches

The strengths and weaknesses of different statistical methodologies for attributing changes in streamflow to land cover are still poorly understood. We examine the relationships between high (Q99), mean (Qmean), and low (Q01) streamflow and urbanization or tree cover change in 729 catchments in the United States between 1992 and 2018. We apply two statistical modeling approaches and compare their performance. Panel regression models estimate the average effect of land cover changes on streamflow across all sites, and show that on average, a 1%-point increase in catchment urban area results in a small (0.6%–0.7%), but highly significant increase in mean and high flows. Meanwhile, a 1%-point increase in tree cover does not correspond to strongly significant changes in flow. We also fit a generalized linear model to each individual site, which results in highly varied model coefficients. The medians of the single-site coefficients show no significant relationships between either urbanization or tree cover change and any streamflow quantile (although at individual sites, the coefficients may be statistically significant and positive or negative). On the other hand, the GLM coefficients may provide greater nuance in catchments with specific attributes. This variation is not well represented through the panel model estimates of average effect, unless moderators are carefully considered. We highlight the value of statistical approaches for large-sample attribution of hydrological change, while cautioning that considerable variability exists

Video surveillance captures student hand hygiene behavior, reactivity to observation, and peer influence in Kenyan primary schools.

In-person structured observation is considered the best approach for measuring hand hygiene behavior, yet is expensive, time consuming, and may alter behavior. Video surveillance could be a useful tool for objectively monitoring hand hygiene behavior if validated against current methods.Student hand cleaning behavior was monitored with video surveillance and in-person structured observation, both simultaneously and separately, at four primary schools in urban Kenya over a study period of 8 weeks.Video surveillance and in-person observation captured similar rates of hand cleaning (absolute difference <5%, p = 0.74). Video surveillance documented higher hand cleaning rates (71%) when at least one other person was present at the hand cleaning station, compared to when a student was alone (48%; rate ratio  = 1.14 [95% CI 1.01-1.28]). Students increased hand cleaning rates during simultaneous video and in-person monitoring as compared to single-method monitoring, suggesting reactivity to each method of monitoring. This trend was documented at schools receiving a handwashing with soap intervention, but not at schools receiving a sanitizer intervention.Video surveillance of hand hygiene behavior yields results comparable to in-person observation among schools in a resource-constrained setting. Video surveillance also has certain advantages over in-person observation, including rapid data processing and the capability to capture new behavioral insights. Peer influence can significantly improve student hand cleaning behavior and, when possible, should be exploited in the design and implementation of school hand hygiene programs

Student hand cleaning rates (% of toileting events) and duration captured by in-person structured observation <i>versus</i> video observation.

<p>*p<0.05.</p>φ<p>Rate-ratios (RR) and p-values reported for Poisson regression analysis of rate differences between video and in-person data, while controlling for the individual school at which the data were collected.</p

Hand cleaning rates (% of toileting events) when the subject was observed to be alone in the video frame <i>versus</i> when other students were present in the frame, as captured by video surveillance.

<p>Hand cleaning rates (% of toileting events) when the subject was observed to be alone in the video frame <i>versus</i> when other students were present in the frame, as captured by video surveillance.</p

Average rate of hand cleaning (% of toileting events) captured by video surveillance preceding and during in-person observation (days with concurrent video/in-person observation only).

<p>Average rate of hand cleaning (% of toileting events) captured by video surveillance preceding and during in-person observation (days with concurrent video/in-person observation only).</p