Environment and Health
Friday, Jan. 6, 2017 7:30 PM – 9:30 PM
- Chair: Meredith Fowlie, University of California-Berkeley
Congestion Pricing and Children's Health
AbstractWe examine the effects of a congestion tax in central Stockholm on ambient air pollution and children’s health. The tax reduced ambient air pollution by 5 to 10 percent, and this reduction led to a significant decrease in the rate of acute asthma attacks among children 0 to 5. The change in asthma is more gradual than the change in pollution, which may be due to the fact that health is a stock. Given the sluggish adjustment of the health stock to pollution changes, short-run estimates of the program effects may understate the long run health benefits of the program. JEL Codes: I12, Q51, Q53.
Does Forest Loss Increase Human Disease? Evidence From 21st Century Nigeria
AbstractThe loss of tropical forests is of particular concern to human health. Mounting scientific evidence demonstrates a complex chain of ecological changes after forest clearing that magnifies the risk of mosquito-borne disease outbreaks, including malaria, Yellow fever, dengue, and more recently Zika virus. Links between deforestation and the incidence of human malaria have been shown, for example, in Brazil (Olson et al. 2010) and in Malaysia (Fornace et al. 2016). To our knowledge, however, only one paper provides causal evidence from Indonesia (Garg 2015). We extend this new literature to study the links between deforestation, malaria, and child and maternal health in Nigeria that lost nearly 80 percent of its primary forest. Nigeria is home to one-fifth of Africa’s population, 97 percent of whom are at risk for malaria. An estimated 100 million malaria cases are reported annually, with over 300,000 annual deaths due to the disease (US Embassy 2011). We estimate the causal impact of deforestation on several measures of child and maternal health and mortality using temporal and spatial variation of deforestation from 1990 to 2013. Our estimation strategy involves geolinking a new high-resolution dataset of global forest change based on time-series analysis of Landsat images at a spatial resolution of 30 meters (Hansen et al. 2013) to household-level health data from the Nigerian Demographic and Health Surveys (DHS) from 1990, 2003, 2008, 2010, and 2013. We use several empirical strategies to identify the impact of deforestation on malaria outbreaks, taking into account endogenous location and confounding economic trends. A growing body of literature demonstrates the causal link between malnutrition and health with performance in school and later in the labor market (Strauss and Thomas 1998; Glewwe and Miguel 2008). We are able to assess such longer-term impacts of environmental changes on human health and wellbeing. Q56,Q23,I15.
Shale Gas Development and Drinking Water Quality
AbstractUS energy and environmental health policy lacks critically needed information regarding the economic, environmental, health and social implications of rapidly expanding unconventional oil and natural gas drilling operations, most notably shale gas development (SGD). Although these operations have created jobs and brought needed income to rural communities, there is concern over whether these operations could threaten health by contaminating local water supplies and air quality. A question that has yet to be explored is whether SGD systematically affects health through the medium of drinking water. This paper empirically tests whether SGD activity impacts drinking water quality in Pennsylvania. We do this by combining several sources of data, which allow us to relate SGD to public drinking water violations through a gas well’s (or waste facility’s) proximity to a Public Water System’s (PWS) ground and/or surface water source intake areas. We estimate a set of PWS fixed-effects models that examine the impact of “threats” faced by the PWS, defined as the presence of gas wells within 1 km of a system’s source water intake, on a number of outcomes that measure drinking water quality, including 1) Total Number of Maximum Contaminant Level (MCL) exceedances, 2) Coliform violations (unlikely to be related to the industry), and 3) MCL violations that are likely to be associated with the industry. We also estimate models using the water quality monitoring and PWS sampling data as dependent variables to capture changes in water quality that may not be large enough to receive an MCL violation. Furthermore, we exploit elevation to study models using quasi-experimental variation stemming from up- and down-gradient threats. Our results not only inform policy-makers who set and enforce health-protective standards for the industry, but researchers who study the mechanisms of pollutant exposure to better understand how these operations affect health in their communities.
- I1 - Health
- Q5 - Environmental Economics