Subsidize or Suffer

The standard for energy production and consumption in the US has historically been the use of coal or oil. In the earliest days of the industrial revolution, coal was king. For a society to achieve such a goal as the expansion and fortification of infrastructure, well, society as a whole, the means absolutely justify the ends. But what if the means of producing energy didn’t have to look like what they did over a hundred years ago? Well they don’t. The use of new renewable, sustainable energy could prove to be beneficial in many aspects, environmental health especially. By subsidizing the use of renewables for the purpose of energy production, the state of NE could stand to become a positive influence for the rest of the nation in the fight against climate change. If it isn’t entirely obvious to you by now, the world’s climate is changing due to anthropogenic activities. In other words, we messed up guys. As the ice caps melt and the air turns a stale brown, the “environmental debt” we have dug ourselves into only grows larger and larger. Unfortunately for the earth, economic principles don’t apply and bankruptcy in this case is literally death. To avoid such an event would require a paradigm shift of society, specifically aimed at the energy production sector. Currently, energy production is one of the US markets with the highest rate of subsidies. But that isn’t to say that we are heading in the wrong direction as a society necessarily. The US Energy Information Administration (EIA) reported that in 2010, natural gas received $24B and coal received$21B. In 2016, natural gas received $32B and coal received$14B, a nearly 40% decrease in funding in just 6 years. This is a good sign, it means that we are willing to put our chips into another pile at the very least, and at most means that we are willing to invest in more sustainable fuels – as natural burns slightly cleaner than oil and much cleaner than coal. The fossil fuel industry currently employs millions of people. A shift away from the use of fossil fuels would certainly eliminate these jobs. This could be seen as a negative through the right (or wrong) lens. But that’s not to say that expanding on clean energy wouldn’t make any jobs either. A 2017 report from the Department of Energy shows that nearly 1 million clean energy jobs have been created in the US alone. This number being nearly 5 times that of US fossil fuel workers. By continuing to subsidize the energy industry in the direction of fossil fuels is to cosign on the accelerated heat death of the planet at the hands of the ignorant. But it is not too late! Several regions in the US have or are planning to have economic policies put in place to combat climate change. Currently there is a 30% rebate on solar PV panels at the federal level, which is wonderful for residential and commercial installation. In addition to this, there are currently 14 different financial incentive programs in relation to renewable energy usage. Most of which are locked to specific regions (DSIRE, 2019). In Nebraska there are strong Net metering laws. This means that any energy produced at the household level has to be connected to the collective power grid. This connection could allow for power produced at the individual level to be sold back to the local energy providers. More money doesn’t have to mean a loss in environmental quality! What Nebraska currently lacks is a substantial subsidy for the implementation of renewables. At the individual level this could look like an installation rebate, and at the industrial level it could simply mimic current practices in the fossil fuels industry

Do commuters suffer from job-education mismatch?

The migration literature shows that cross-border skill transfer is associated with a risk of increased job-education mismatch. This paper examines whether the problems of job-education mismatch often found among migrants also apply to cross-border commuters and compares cross-border commuters to within-country commuters as well as non-commuters and recent and established migrants in this respect. We find that cross-border commuters and recent migrants from EU15 countries have lower over- but higher under-education rates than non-commuters, but that for cross-border commuters and recent migrants from the NMS12 the opposite applies. Within-country commuters finally have lower over- but higher under-education rates than non-commuters in both regions. Please note: The alternative choice regarding Session theme is K. Spatial issues of the labour market

Does the Gravity Model Suffer from Selection Bias?

When analyzing bilateral trade flow data, zero trade flows are quite common and problematic when a gravity equation is estimated with a log-linear functional form. This has caused many researchers to either ignore the zero trade flows or to replace zero with a small positive number. Both of these actions bias the resulting parameter estimates of the gravity equation. In this study we correct for this misspecification by using the Heckman selection model to estimate the bilateral trade flows for 46 agrifood products, for the period 1990 to 2000, for 52 countries. In our sample, selection bias rarely affects the signs of variables but often has a substantial effect on the magnitude, statistical significance and economic interpretation of the marginal effects. Hence, treating zero trade flows properly is important from both a statistical and an economics perspective.Gravity model, selection bias, Agrifood Trade, Heckman Selection Model, marginal effects, Agricultural and Food Policy, Demand and Price Analysis, International Relations/Trade,

Preaching God\u27s compassion: comforting those who suffer

Title: Preaching God\u27s compassion: comforting those who suffer. Author: Aden, LeRoy Preaching God\u27s compassion 176 p. Publisher: Minneapolis : Fortress Pr, 2002

Do Deep Neural Networks Suffer from Crowding?

Crowding is a visual effect suffered by humans, in which an object that can be recognized in isolation can no longer be recognized when other objects, called flankers, are placed close to it. In this work, we study the effect of crowding in artificial Deep Neural Networks for object recognition. We analyze both standard deep convolutional neural networks (DCNNs) as well as a new version of DCNNs which is 1) multi-scale and 2) with size of the convolution filters change depending on the eccentricity wrt to the center of fixation. Such networks, that we call eccentricity-dependent, are a computational model of the feedforward path of the primate visual cortex. Our results reveal that the eccentricity-dependent model, trained on target objects in isolation, can recognize such targets in the presence of flankers, if the targets are near the center of the image, whereas DCNNs cannot. Also, for all tested networks, when trained on targets in isolation, we find that recognition accuracy of the networks decreases the closer the flankers are to the target and the more flankers there are. We find that visual similarity between the target and flankers also plays a role and that pooling in early layers of the network leads to more crowding. Additionally, we show that incorporating the flankers into the images of the training set does not improve performance with crowding.Comment: CBMM mem

Customers Suffer From Employee Churn: High Turnover Makes It Harder to Provide Top Service

Key Findings: • As rates of voluntary turnover climb within key business units, customers are more likely to report bad customer service. • When new workers arrive, established workers have to take time away from customer service to train the new workers in procedures and company culture. • Work units with lots of new employees have more trouble managing turnover and receive the lowest customer service ratings. • Bigger may not be better—larger work units have particular difficulty managing turnover and receive lower customer service scores than smaller ones. • A tight, cohesive work group seems to suffer from turnover as much as a less-bonded group

Better that X guilty persons escape than that one innocent suffer

The principle that it is better to let some guilty individuals be set free than to mistakenly convict an innocent person is generally shared by legal scholars, judges and lawmakers of modern societies. The paper shows why this common trait of criminal procedure is also efficient. It extends the standard Polinsky and Shavell (2007) model of deterrence and shows that when the costs of convictions are positive, and guilty individuals are more likely to be convicted than innocent individuals it is always efficient to minimize the number of wrongful convictions, while a more than minimal amount of wrongful acquittals may be optimal.Type I errors, Type II errors, evidence, optimal underdeterrence, Blackstone Pareto distribution, optimal screening