Consequences of Data Error in Aggregate Indicators: Evidence from the Human Development Index

This paper examines the consequences of data error in data series used to construct aggregate indicators. Using the most popular indicator of country level economic development, the Human Development Index (HDI), we identify three separate sources of data error. We propose a simple statistical framework to investigate how data error may bias rank assignments and identify two striking consequences for the HDI. First, using the cutoff values used by the United Nations to assign a country as ‘low’, ‘medium’, or ‘high’ developed, we find that currently up to 45% of developing countries are misclassified. Moreover, by replicating prior development/macroeconomic studies, we find that key estimated parameters such as Gini coefficients and speed of convergence measures vary by up to 100% due to data error.measurement error, international comparative statistics

Reducing exposure to high levels of perfluorinated compounds in drinking water improves reproductive outcomes: evidence from an intervention in Minnesota.

BackgroundPer- and polyfluoroalkyl substances (PFASs) have been detected in drinking water supplies around the world and are the subject of intense regulatory debate. While they have been associated with several illnesses, their effects on reproductive outcomes remains uncertain.MethodsWe analyzed birth outcomes in the east Minneapolis-St. Paul metropolitan area from 2002 to 2011, where a portion of the population faced elevated exposure to PFASs due to long-term contamination of drinking water supplies from industrial waste disposal. Installation of a water filtration facility in the highly contaminated city of Oakdale, MN at the end of 2006 resulted in a sharp decrease in exposure to PFASs, creating a "natural experiment". Using a difference-in-differences approach, we compare the changes in birth outcomes before and after water filtration in Oakdale to the changes over the same period in neighboring communities where the treatment of municipal water remained constant.ResultsAverage birth weight and average gestational age were statistically significantly lower in the highly exposed population than in the control area prior to filtration of municipal water supply. The highly exposed population faced increased odds of low birth weight (adjusted odds ratio 1.36, 95% CI 1.25-1.48) and pre-term birth (adjusted odds ratio 1.14, 95% CI 1.09-1.19) relative to the control before filtration, and these differences moderated after filtration. The general fertility rate was also significantly lower in the exposed population (incidence rate ratio 0.73, 95% CI 0.69-0.77) prior to filtration and appeared to be rebounding post-2006.ConclusionsOur findings provide evidence of a causal relationship between filtration of drinking water containing high levels of exposure to PFASs and improved reproductive outcomes

Forecasting the Path of U.S. C02 Emissions Using State-Level Information

We compare the most common reduced-form models used for emissions forecasting, point out shortcomings, and suggest improvements. Using a U.S. state-level panel data set of CO2 emissions, we test the performance of existing models against a large univers

US power plant sites at risk of future sea-level rise

Unmitigated greenhouse gas emissions may increase global mean sea-level by about 1 meter during this century. Such elevation of the mean sea-level enhances the risk of flooding of coastal areas. We compute the power capacity that is currently out-of-reach of a 100-year coastal flooding but will be exposed to such a flood by the end of the century for different US states, if no adaptation measures are taken. The additional exposed capacity varies strongly among states. For Delaware it is 80% of the mean generated power load. For New York this number is 63% and for Florida 43%. The capacity that needs additional protection compared to today increases by more than 250% for Texas, 90% for Florida and 70% for New York. Current development in power plant building points towards a reduced future exposure to sea-level rise: proposed and planned power plants are less exposed than those which are currently operating. However, power plants that have been retired or canceled were less exposed than those operating at present. If sea-level rise is properly accounted for in future planning, an adaptation to sea-level rise may be costly but possible

Classification, Detection and Consequences of Data Error: Evidence from the Human Development Index

We measure and examine data error in health, education and income statistics used to construct the Human Development Index. We identify three sources of data error which are due to data updating; formula revisions; and thresholds to classify a country’s development status. We propose a simple statistical framework to calculate country specific measures of data uncertainty and investigate how data error biases rank assignments. We find that up to 34% of countries are misclassified and, by replicating prior studies, we show that key estimated parameters vary by up to 100% due to data error

On the attribution of a single event to climate change

Author Posting. © American Meteorological Society, 2014. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Climate 27 (2014): 8297–8301, doi:10.1175/JCLI-D-14-00399.1.There is growing interest in assessing the role of climate change in observed extreme weather events. Recent work in this area has focused on estimating a measure called attributable risk. A statistical formulation of this problem is described and used to construct a confidence interval for attributable risk. The resulting confidence is shown to be surprisingly wide even in the case where the event of interest is unprecedented in the historical record.GH acknowledges funding from the Federal Ministry for Education and Research. MA acknowledges partial support from the Giannini Foundation.2015-05-1

Climate, extreme heat, and electricity demand in California

Climate projections from three atmosphere-ocean climate models with a range of low to mid-high temperature sensitivity forced by the Intergovernmental Panel for Climate Change SRES higher, middle, and lower emission scenarios indicate that, over the 21st century, extreme heat events for major cities in heavily air-conditioned California will increase rapidly. These increases in temperature extremes are projected to exceed the rate of increase in mean temperature, along with increased variance. Extreme heat is defined here as the 90 percent exceedance probability (T90) of the local warmest summer days under the current climate. The number of extreme heat days in Los Angeles, where T90 is currently 95 F (32 C), may increase from 12 days to as many as 96 days per year by 2100, implying current-day heat wave conditions may last for the entire summer, with earlier onset. Overall, projected increases in extreme heat under the higher A1fi emission scenario by 2070-2099 tend to be 20-30 percent higher than those projected under the lower B1 emission scenario, ranging from approximately double the historical number of days for inland California cities (e.g. Sacramento and Fresno), up to four times for previously temperate coastal cities (e.g. Los Angeles, San Diego). These findings, combined with observed relationships between high temperature and electricity demand for air-conditioned regions, suggest potential shortfalls in transmission and supply during T90 peak electricity demand periods. When the projected extreme heat and peak demand for electricity are mapped onto current availability, maintaining technology and population constant only for demand side calculations, we find the potential for electricity deficits as high as 17 percent. Similar increases in extreme heat days are suggested for other locations across the U.S. southwest, as well as for developing nations with rapidly increasing electricity demands. Electricity response to recent extreme heat events, such as the July 2006 heat wave in California, suggests that peak electricity demand will challenge current supply, as well as future planned supply capacities when population and income growth are taken into account

Less global inequality can improve climate outcomes

Two of the biggest global challenges we face today are mitigating climate change and economic inequality. Some research suggests these goals are in conflict, based largely on the observation that a dollar spent at higher income levels is less carbon intensive than at lower income levels. We put this concern to rest. We quantify this effect in its most extreme manifestation, both within countries and between countries. We use a wide range of income elasticities of emissions (0.7–1.0) and scenarios from the Shared Socioeconomic Pathways (SSP) with the highest (SSP4) and lowest (SSP5) between-country inequality. Within countries, even with assumptions of low elasticities (0.7) and aggressive inequality reduction (Gini coefficient of 0.55 to 0.30), emissions would realistically increase by less than 8%, which would likely occur over several decades. Income convergence between countries may reduce the emissions intensity of global income growth, because the energy intensity reductions from income growth in emerging economies, such as India and China, offsets the energy increasing effect of higher growth in developing countries. Given these findings, it seems a distraction for future research to dwell on this narrow framing when there are deeper under-explored linkages and synergies between reducing income inequality and climate change, such as the effect of reducing inequality on social norms, consumption and on political mobilization around climate policy