Job Change and Job Stability among Less-Skilled Young Workers

In this paper we review evidence from previous studies of job and employment instability among less-educated young workers, and we provide new evidence from the National Longitudinal Survey of Youth. We find that early employment instability contributes somewhat to the low levels of employment observed among high school dropouts, especially females. Important determinants of job stability include the cognitive skills of the workers themselves (as measured by math test scores), current or previous experience and job tenure, and a variety of job characteristics including starting wages, occupation, and industry. Job instability among female dropouts seems to be strongly related to fertility history and marital status. Some implications for policy, especially welfare reform, are discussed as well.

Estimating the Effect of Training on Employment and Unemployment Durations: Evidence From Experimental Data

Using data from a social experiment, we estimate the impact of training on the duration of employment and unemployment spells for AFDC recipients. Although an experimental design eliminates the need to construct a comparison group for this analysis, simple comparisons between the average durations or the transition rates of treatments' and controls' employment and unemployment spells lead to biased estimates of the effects of training. We present and implement several econometric approaches that demonstrate the importance of and correct for these biases. For the training program studied in the paper, we find that it raised employment rates because employment durations increased. In contrast, training did not lead to shorter unemployment spells.

How perilous are broad-scale correlations with environmental variables?

Many studies correlate geographic variation of biotic variables (e.g., species ranges, species richness, etc.) with variation in environmental variables (climate, topography, history). Often, the resulting correlations are interpreted as evidence of causal links. However, both the dependent and independent variables in these analyses are strongly spatially structured. Several studies have suggested that spatially structured variables may be significantly correlated with one another despite the absence of a causal link between them. In this study we ask: if two variables are spatially structured, but causally unrelated, how strong is the expected correlation between them? As a specific example, we consider the correlations between broad-scale variation in gamma species richness and climatic variables. Are these correlations likely to be statistical artefacts? To answer these questions, we randomly generated pseudo-climatic variables that have the same range and spatial autocorrelation as temperature and precipitation in the Americas. We related mammal and bird species richness both to the real and the pseudo-climatic variables. We also observed the correlations among pseudo-climate simulations. Correlations among randomly generated, spatially unstructured, variables are very small. In contrast, the median correlations between spatially structured variables are near r2=0.1 – 0.3, and the 95% confidence limits extend to r2=0.6 – 0.7. Viewing this as a null expectation, given spatially structured variables, it is worth nothing that published richness–climate correlations are typically marginally stronger than these values. However, many other published richness–environment correlations would fail this test. Tests of the “predictive ability” of a correlation cannot reliably distinguish correlations due to spatial structure from causal relationships. Our results suggest a three-part update of Tobler’s “First Law of Geography”: #1) Everything in geography that is spatially structured will be collinear. #2) Near things are more related than distant things. #3) The more strongly spatially structured two variables are, the stronger the collinearity between them will be

Earnings Losses of Displaced Workers

The 1990-1991 recession has intensified concerns about the consequences of workers' job losses. To estimate the magnitude and temporal pattern of displaced workers' earnings losses, we exploit an unusual administrative data set that includes both employees' quarterly earnings histories and information about their firms. We find that when high-tenure workers separate from distressed firms their long-term losses average 25 percent per year. Further, their losses mount even prior to separation, are not limited to workers in a few industrial sectors, and are substantial even for those who find new jobs in similar firms. This evidence suggests that displaced workers' earnings losses result largely from the loss of some unidentified attribute of the employment relationship.earnings, wages, losses, displaced, dislocated, workers, Jacobson, LaLonde, Sullivan

The Basis Risk of Catastrophic-Loss Index Securities

This paper analyzes the basis risk of catastrophic-loss (CAT) index derivatives, which securitize losses from catastrophic events such as hurricanes and earthquakes. We analyze the hedging effectiveness of these instruments for 255 insurers writing 93 percent of the insured residential property values in Florida, the state most severely affected by exposure to hurricanes. County-level losses are simulated for each insurer using a sophisticated model developed by Applied Insurance Research. We analyze basis risk by measuring the effectiveness of hedge portfolios, consisting of a short position each insurer's own catastrophic losses and a long position in CAT-index call spreads, in reducing insurer loss volatility, value-at-risk, and expected losses above specified thresholds. Two types of loss indices are used -- a statewide index based on insurance losses in four quadrants of the state. The principal finding is that firms in the three largest Florida market-share quartiles can hedge almost as effectively using the intra-state index contracts as they can using contracts that settle on their own losses. Hedging with the statewide contracts is effective only for insurers with the largest market shares and for smaller insurers that are highly diversified throughout the state. The results also support the agency-theoretic hypotheses that mutual insurers are more diversified than stocks and that unaffiliated single firms are more diversified than insurers that are members of groups.

Black Hills/White Justice: The Sioux Nation Versus the United States

A Review of Black Hills/White Justice: The Sioux Nation Versus the United States by Edward Lazaru

Allocating the Burden of Proof To Effectuate the Preservation and Federalism Goals of the Coastal Zone Management Act

Primarily due to policy considerations, this Note argues that courts should allocate to the federal agency proposing an activity that may affect the coastal zone the burden of proving consistency with a state CMP. This allocation effectuates Congress\u27s intent to vest states with primary control to preserve the coastal zone. Part I provides a general background of the Act\u27s consistency requirement for federally conducted activities. Part II examines the various factors that courts traditionally consider when allocating burdens of proof in litigation. Part III evaluates these factors as applied to the consistency issue under the CZMA. Part IV concludes that courts should assign the initial burden of production to the state contesting a federal agency\u27s consistency determination; the ultimate burden of proving that the activity is consistent with a state CMP, however, belongs with the federal agency

Long-term earnings losses of high-seniority displaced workers

Displaced workers

Unsupervised Deep Single-Image Intrinsic Decomposition using Illumination-Varying Image Sequences

Machine learning based Single Image Intrinsic Decomposition (SIID) methods decompose a captured scene into its albedo and shading images by using the knowledge of a large set of known and realistic ground truth decompositions. Collecting and annotating such a dataset is an approach that cannot scale to sufficient variety and realism. We free ourselves from this limitation by training on unannotated images. Our method leverages the observation that two images of the same scene but with different lighting provide useful information on their intrinsic properties: by definition, albedo is invariant to lighting conditions, and cross-combining the estimated albedo of a first image with the estimated shading of a second one should lead back to the second one's input image. We transcribe this relationship into a siamese training scheme for a deep convolutional neural network that decomposes a single image into albedo and shading. The siamese setting allows us to introduce a new loss function including such cross-combinations, and to train solely on (time-lapse) images, discarding the need for any ground truth annotations. As a result, our method has the good properties of i) taking advantage of the time-varying information of image sequences in the (pre-computed) training step, ii) not requiring ground truth data to train on, and iii) being able to decompose single images of unseen scenes at runtime. To demonstrate and evaluate our work, we additionally propose a new rendered dataset containing illumination-varying scenes and a set of quantitative metrics to evaluate SIID algorithms. Despite its unsupervised nature, our results compete with state of the art methods, including supervised and non data-driven methods.Comment: To appear in Pacific Graphics 201