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Register a new userWillingness to Pay to Prevent Water and Sanitation-Related Diseases Suffered by Slum Dwellers and Beneficiary Households: Evidence from Chittagong, Bangladesh
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Mohammad Nur Nobi
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A majority portion of the slum people is involved in service sectors. The city dwellers are somehow dependent on the services of those people. Pure drinking water and hygiene is a significant concern in the slums. Because of the lack of these two items, the slum people are getting sick, which causes the interruption to their services. In addition, they can transmit the diseases they suffer from to the service receiver. With these aims, this study endeavors to explore the willingness to pay of the households who receive the services of the slum people using the mixed-method techniques. Under this technique, 265 households were surveyed through face-to-face interviews, and 10 KIIs were conducted with slum people. The studys findings suggest that the households showed their willingness to pay for the improvement of the water and sanitation facilities in the slums. However, the KIIs findings show that the slum people are not willing to pay for the improvement as they claim that government should finance the project of improving water and sanitation facilities in the slums.
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This study aims to assess the net benefit of the kaptai dam on the Karnafuli river in Kaptai, Chittagong, Bangladesh. Kaptai Dam, the only hydroelectricity power source in Bangladesh, provides only 5% electricity demand of Bangladesh. The Dam is located on the Karnafuli River at Kaptai in Rangamati District, 65 km upstream from Chittagong. It is an earth-fill or embankment dam with a reservoir with a water storage capacity of 11,000 skm. Though the Dams primary purpose is to generate electricity, it became a reservoir of water used for fishing and tourism. To find the net benefit value and estimate the environmental costs and benefits, we considered the environmental net benefit from 1962 to 1997. We identify the costs of Kaptai Dam, including its establishment cost, operational costs, the costs of lives that have been lost due to conflicts, and environmental costs, including loss of biodiversity, loss of land uses, and loss of human displacements. Also, we assess the benefits of electricity production, earnings from fisheries production, and gain from tourism to Kaptai Lake. The findings show that the Dam contributes tremendous value to Bangladesh. As a source of hydroelectricity, the Kaptai Dam is a source of clean energy, and its value might have been worthy of this Dam produced a significant portion of the electricity. However, providing less than 5% of the national demand for electricity followed by various external and sensitive costs, the Dam hardly contributes to the Bangladesh economy. This study thus recommends that Bangladesh should look for other sources of clean energy that have no chances of eco-political conflicts.
The objective of this study is to understand the different behavioral considerations that govern the choice of people to engage in a crowd-shipping market. Using novel data collected by the researchers in the US, we develop discrete-continuous models. A binary logit model has been used to estimate crowd-shippers willingness to work, and an ordinary least-square regression model has been employed to calculate crowd-shippers maximum tolerance for shipping and delivery times. A selectivity-bias term has been included in the model to correct for the conditional relationships of the crowd-shippers willingness to work and their maximum travel time tolerance. The results show socio-demographic characteristics (e.g. age, gender, race, income, and education level), transporting freight experience, and number of social media usages significant influence the decision to participate in the crowd-shipping market. In addition, crowd-shippers pay expectations were found to be reasonable and concurrent with the literature on value-of-time. Findings from this research are helpful for crowd-shipping companies to identify and attract potential shippers. In addition, an understanding of crowd-shippers - their behaviors, perceptions, demographics, pay expectations, and in which contexts they are willing to divert from their route - are valuable to the development of business strategies such as matching criteria and compensation schemes for driver-partners.
Conventional wisdom suggests that large-scale refugees pose security threats to the host community or state. With massive influx of Rohingyas in Bangladesh in 2017 resulting a staggering total of 1.6 million Rohingyas, a popular discourse emerged that Bangladesh would face severe security threats. This article investigates the security experience of Bangladesh in case of Rohingya influx over a three-year period, August 2017 to August 2020. The research question I intend to address is, has Bangladesh experienced security threat due to massive Rohingya influx? If so in what ways? I test four security threat areas: societal security, economic security, internal security, and public security. I have used newspaper content analysis over past three years along with interview data collected from interviewing local people in coxs bazar area where the Rohingya camps are located. To assess if the threats are low level, medium level, or high level, I investigated both the frequency of reports and the way they are interpreted. I find that Bangladesh did not experience any serious security threats over the last three years. There are some criminal activities and offenses, but these are only low-level security threat at best. My research presents empirical evidence that challenges conventional assertions that refugees are security threats or challenges to the host states.
A new and rapidly growing econometric literature is making advances in the problem of using machine learning methods for causal inference questions. Yet, the empirical economics literature has not started to fully exploit the strengths of these modern methods. We revisit influential empirical studies with causal machine learning methods and identify several advantages of using these techniques. We show that these advantages and their implications are empirically relevant and that the use of these methods can improve the credibility of causal analysis.
In this paper, we first revisit the Koenker and Bassett variational approach to (univariate) quantile regression, emphasizing its link with latent factor representations and correlation maximization problems. We then review the multivariate extension due to Carlier et al. (2016, 2017) which relates vector quantile regression to an optimal transport problem with mean independence constraints. We introduce an entropic regularization of this problem, implement a gradient descent numerical method and illustrate its feasibility on univariate and bivariate examples.
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