Benefit Estimation of Water Quality Improvements in Bagmati River Using Choice Modeling

Environmental Economics and Policy,

Knowledge, Information, and Water Treatment Behavior of Residents in the Kathmandu Valley, Nepal

Access to safe drinking water plays a crucial role in the overall social and economic development of a community. Unsafe water delivered to household taps increases the risks of waterborne diseases and threatens population health. Consumers can adopt a number of averting behaviors such as filtering or boiling their water. While these approaches are effective in reducing the likelihood of contracting a waterborne disease, not all households treat their water. Given this, it is important to develop a better understanding of factors that influence water treatment behavior. In this paper, we examine determinants of water treatment behavior using survey data (N=1200) from Kathmandu, Nepal. In particular, this paper focuses on the impacts of knowledge, exposure to information, and community participation on drinking water treatment behavior. Previous research has found that income, education level, awareness, and exposure to media are major factors that impact the individual-level decision to treat water before using it. We contribute to this literature by explicitly examining how knowledge about waterborne diseases, exposure to water quality information campaigns, and participation in community organizations impact drinking water treatment behavior. The results from probit regression analyses suggest that either a one percentage increase in the knowledge index or community participation index both increase the likelihood of utilizing drinking water treatment methods by about 0.17 percentage points. Households connected to the distribution system are 31 percentage points more likely to treat water compared to those that are not connected to the system. Multinomial results indicate that wealthier households use more than one treatment method

Estimating Population Attribute Values in a Table: “Get Me Started in” Iterative Proportional Fitting

Iterative proportional fitting (IPF) is a technique that can be used to adjust a distribution reported in one data set by totals reported in others. IPF is used to revise tables of data where the information is incomplete, inaccurate, outdated, or a sample. Although widely applied, the IPF methodology is rarely presented in a way that is accessible to nonexpert users. This article fills that gap through discussion of how to operationalize the method and argues that IPF is an accessible and transparent tool that can be applied to a range of data situations in population geography and demography. It offers three case study examples where IPF has been applied to geographical data problems; the data and algorithms are made available to users as supplementary material